A view from above: cloud plots to visualize global metabolomic data

Rolling forms the diversities of small molecular nonvolatile metabolite profile and consequently shapes the bacterial community structure for Keemun black tea

The detailed dynamics of small molecular nonvolatile chemical and bacterial diversities, as well as their relationship are still unclear in the manufacturing process of Keemun black tea (KMBT). Herein, mass spectrometry-based untargeted metabolomics, Feature-based Molecular Networking (FBMN) and bacterial DNA amplicon sequencing were used to investigate the dense temporal samples of the manufacturing process. For the first time, we reveal that the pyrogallol-type catechins are oxidized asynchronously before catechol-type catechins during the black tea processing. Rolling is the key procedure for forming the small molecular nonvolatile metabolite profile (SMNMetProf), increasing the metabolite richness, and then shaping the bacterial community structure in the KMBT manufacturing process, which decreases both molecular weight and molecular polarity of the small molecular nonvolatile metabolites. The SMNMetProf of black tea is formed by the endogenous enzymatic oxidation of tea leaves, rather than bacterial fermentation.

Dynamic Changes in the Microbial Community and Metabolite Profile during the Pile Fermentation Process of Fuzhuan Brick Tea

The pile fermentation process of Fuzhuan brick tea is unique in that it involves preheating without the use of starter cultures. The detailed metabolite changes and their drivers during this procedure are not known. Characterizing these unknown changes that occur in the metabolites and microbes during pile fermentation of Fuzhuan brick tea is important for industrial modernization of this traditional fermented food. Using microbial DNA amplicon sequencing, mass spectrometry-based untargeted metabolomics, and feature-based molecular networking, we herein reveal that significant changes in the microbial community occur before changes in the metabolite profile. These changes were characterized by a decrease in Klebsiella and Aspergillus, alongside an increase in Bacillus and Eurotium. The decrease in lysophosphatidylcholines, unsaturated fatty acids, and some astringent flavan-3-ols and bitter amino acids, as well as the increase in some less astringent flavan-3-ols and sweet or umami amino acids, contributed importantly to the overall changes observed in the metabolite profile. The majority of these changes was caused by bacterial metabolism and the corresponding heat generated by it.

Associations between plant-based diets, plant foods and botanical supplements with gestational diabetes mellitus: a systematic review protocol

INTRODUCTION

Gestational diabetes mellitus (GDM) is one of the most common health complications during pregnancy. Medical nutrition therapy is the mainstay of treatment for GDM, however, there is no current consensus on optimal dietary approaches to prevent or control hyperglycaemia in pregnancy. The aim of this systematic review is to assess the relationships between plant-based dietary patterns, plant foods and botanical dietary supplements with GDM and maternal glycaemic biomarkers.

METHODS AND ANALYSIS

A predefined search strategy was used on 16 June 2021, to search PubMed, Embase and CINAHL Plus with Full Text (EBSCOhost), as well as ClinicalTrials.gov, for studies published as original articles in English. Articles will be included if they are human observational studies or clinical trials and will be excluded if they are review articles or conference abstracts. We will use Cochrane's risk of bias tools for interventions that are parallel arm (Risk of Bias tool for randomised trials version 2 (RoB 2)) and single arm, non-randomised intervention studies (Risk of Bias In Non-randomised Studies-of Interventions (ROBINS-I)). For observational, case-control and cross-sectional studies, we will use the National Heart, Lung and Blood Institute's quality assessment tools. Data will be synthesised in a narrative format describing significant results as well as presenting the results of the quality assessment of studies.

ETHICS AND DISSEMINATION

This systematic review does not require ethical approval as primary data will not be collected. The review will be published in a peer-reviewed journal and disseminated electronically and in print.

PROSPERO REGISTRATION NUMBER

CRD42022306915.

An initial investigation of accuracy required for the identification of small molecules in complex samples using quantum chemical calculated NMR chemical shifts

The majority of primary and secondary metabolites in nature have yet to be identified, representing a major challenge for metabolomics studies that currently require reference libraries from analyses of authentic compounds. Using currently available analytical methods, complete chemical characterization of metabolomes is infeasible for both technical and economic reasons. For example, unambiguous identification of metabolites is limited by the availability of authentic chemical standards, which, for the majority of molecules, do not exist. Computationally predicted or calculated data are a viable solution to expand the currently limited metabolite reference libraries, if such methods are shown to be sufficiently accurate. For example, determining nuclear magnetic resonance (NMR) spectroscopy spectra in silico has shown promise in the identification and delineation of metabolite structures. Many researchers have been taking advantage of density functional theory (DFT), a computationally inexpensive yet reputable method for the prediction of carbon and proton NMR spectra of metabolites. However, such methods are expected to have some error in predicted 13C and 1H NMR spectra with respect to experimentally measured values. This leads us to the question-what accuracy is required in predicted 13C and 1H NMR chemical shifts for confident metabolite identification? Using the set of 11,716 small molecules found in the Human Metabolome Database (HMDB), we simulated both experimental and theoretical NMR chemical shift databases. We investigated the level of accuracy required for identification of metabolites in simulated pure and impure samples by matching predicted chemical shifts to experimental data. We found 90% or more of molecules in simulated pure samples can be successfully identified when errors of 1H and 13C chemical shifts in water are below 0.6 and 7.1 ppm, respectively, and below 0.5 and 4.6 ppm in chloroform solvation, respectively. In simulated complex mixtures, as the complexity of the mixture increased, greater accuracy of the calculated chemical shifts was required, as expected. However, if the number of molecules in the mixture is known, e.g., when NMR is combined with MS and sample complexity is low, the likelihood of confident molecular identification increased by 90%.

mTORC1 and mTORC2 Complexes Regulate the Untargeted Metabolomics and Amino Acid Metabolites Profile through Mitochondrial Bioenergetic Functions in Pancreatic Beta Cells

Background: Pancreatic beta cells regulate bioenergetics efficiency and secret insulin in response to glucose and nutrient availability. The mechanistic Target of Rapamycin (mTOR) network orchestrates pancreatic progenitor cell growth and metabolism by nucleating two complexes, mTORC1 and mTORC2. Objective: To determine the impact of mTORC1/mTORC2 inhibition on amino acid metabolism in mouse pancreatic beta cells (Beta-TC-6 cells, ATCC-CRL-11506) using high-resolution metabolomics (HRM) and live-mitochondrial functions. Methods: Pancreatic beta TC-6 cells were incubated for 24 h with either: RapaLink-1 (RL); Torin-2 (T); rapamycin (R); metformin (M); a combination of RapaLink-1 and metformin (RLM); Torin-2 and metformin (TM); compared to the control. We applied high-resolution mass spectrometry (HRMS) LC-MS/MS untargeted metabolomics to compare the twenty natural amino acid profiles to the control. In addition, we quantified the bioenergetics dynamics and cellular metabolism by live-cell imaging and the MitoStress Test XF24 (Agilent, Seahorse). The real-time, live-cell approach simultaneously measures the oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) to determine cellular respiration and metabolism. Statistical significance was assessed using ANOVA on Ranks and post-hoc Welch t-Tests. Results: RapaLink-1, Torin-2, and rapamycin decreased L-aspartate levels compared to the control (p = 0.006). Metformin alone did not affect L-aspartate levels. However, L-asparagine levels decreased with all treatment groups compared to the control (p = 0.03). On the contrary, L-glutamate and glycine levels were reduced only by mTORC1/mTORC2 inhibitors RapaLink-1 and Torin-2, but not by rapamycin or metformin. The metabolic activity network model predicted that L-aspartate and AMP interact within the same activity network. Live-cell bioenergetics revealed that ATP production was significantly reduced in RapaLink-1 (122.23 ± 33.19), Torin-2 (72.37 ± 17.33) treated cells, compared to rapamycin (250.45 ± 9.41) and the vehicle control (274.23 ± 38.17), p < 0.01. However, non-mitochondrial oxygen consumption was not statistically different between RapaLink-1 (67.17 ± 3.52), Torin-2 (55.93 ± 8.76), or rapamycin (80.01 ± 4.36, p = 0.006). Conclusions: Dual mTORC1/mTORC2 inhibition by RapaLink-1 and Torin-2 differentially altered the amino acid profile and decreased mitochondrial respiration compared to rapamycin treatment which only blocks the FRB domain on mTOR. Third-generation mTOR inhibitors may alter the mitochondrial dynamics and reveal a bioenergetics profile that could be targeted to reduce mitochondrial stress.

Development of a Metabolite Ratio Rule-Based Method for Automated Metabolite Profiling and Species Differentiation of Four Major Cinnamon Species

A metabolomic ratio rule-based classification method was developed and programmed for automated metabolite profiling and differentiation of four major cinnamon species using ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS). The computational program identifies key cinnamon metabolites, including proanthocyanidins, cinnamaldehyde, and coumarin, from test samples through LC-MS data processing and assigns cinnamon species by critical metabolite ratios using a stepwise classification strategy. Further, 100% classification accuracy was achieved on the training sample set through critical ratio optimization, and over 95% accuracy was achieved on the validation sample set. The proposed cinnamon classification method exhibited superior accuracy compared to the metabolomic-based PLS-DA modeling method and offered great value for the authentication of cinnamon samples and evaluation of their potential health benefits.

Effects of Acute 2,3,7,8-Tetrachlorodibenzo-p-Dioxin Exposure on the Circulating and Cecal Metabolome Profile

2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) is a polyhalogenated planar hydrocarbon belonging to a group of highly toxic and persistent environmental contaminants known as "dioxins". TCDD is an animal teratogen and carcinogen that is well characterized for causing immunosuppression through activation of aryl hydrocarbon receptor (AHR). In this study, we investigated the effect of exposure of mice to an acute dose of TCDD on the metabolic profile within the serum and cecal contents to better define the effects of TCDD on host physiology. Our findings demonstrated that within the circulating metabolome following acute TCDD exposure, there was significant dysregulation in the metabolism of bioactive lipids, amino acids, and carbohydrates when compared with the vehicle (VEH)-treated mice. These widespread changes in metabolite abundance were identified to regulate host immunity via modulating nuclear factor-kappa B (NF-κB) and extracellular signal-regulated protein kinase (ERK1/2) activity and work as biomarkers for a variety of organ injuries and dysfunctions that follow TCDD exposure. Within the cecal content of mice exposed to TCDD, we were able to detect changes in inflammatory markers that regulate NF-κB, markers of injury-related inflammation, and changes in lysine degradation, nicotinamide metabolism, and butanoate metabolism, which collectively suggested an immediate suppression of broad-scale metabolic processes in the gastrointestinal tract. Collectively, these results demonstrate that acute TCDD exposure results in immediate irregularities in the circulating and intestinal metabolome, which likely contribute to TCDD toxicity and can be used as biomarkers for the early detection of individual exposure.

Benchmark Concentrations for Untargeted Metabolomics Versus Transcriptomics for Liver Injury Compounds in In Vitro Liver Models

Interpretation of untargeted metabolomics data from both in vivo and physiologically relevant in vitro model systems continues to be a significant challenge for toxicology research. Potency-based modeling of toxicological responses has served as a pillar of interpretive context and translation of testing data. In this study, we leverage the resolving power of concentration-response modeling through benchmark concentration (BMC) analysis to interpret untargeted metabolomics data from differentiated cultures of HepaRG cells exposed to a panel of reference compounds and integrate data in a potency-aligned framework with matched transcriptomic data. For this work, we characterized biological responses to classical human liver injury compounds and comparator compounds, known to not cause liver injury in humans, at 10 exposure concentrations in spent culture media by untargeted liquid chromatography-mass spectrometry analysis. The analyte features observed (with limited metabolites identified) were analyzed using BMC modeling to derive compound-induced points of departure. The results revealed liver injury compounds produced concentration-related increases in metabolomic response compared to those rarely associated with liver injury (ie, sucrose, potassium chloride). Moreover, the distributions of altered metabolomic features were largely comparable with those observed using high throughput transcriptomics, which were further extended to investigate the potential for in vitro observed biological responses to be observed in humans with exposures at therapeutic doses. These results demonstrate the utility of BMC modeling of untargeted metabolomics data as a sensitive and quantitative indicator of human liver injury potential.

Bacterial-fungal interactions revealed by genome-wide analysis of bacterial mutant fitness

Microbial interactions are expected to be major determinants of microbiome structure and function. Although fungi are found in diverse microbiomes, their interactions with bacteria remain largely uncharacterized. In this work, we characterize interactions in 16 different bacterial-fungal pairs, examining the impacts of 8 different fungi isolated from cheese rind microbiomes on 2 bacteria (Escherichia coli and a cheese-isolated Pseudomonas psychrophila). Using random barcode transposon-site sequencing with an analysis pipeline that allows statistical comparisons between different conditions, we observed that fungal partners caused widespread changes in the fitness of bacterial mutants compared to growth alone. We found that all fungal species modulated the availability of iron and biotin to bacterial species, which suggests that these may be conserved drivers of bacterial-fungal interactions. Species-specific interactions were also uncovered, a subset of which suggested fungal antibiotic production. Changes in both conserved and species-specific interactions resulted from the deletion of a global regulator of fungal specialized metabolite production. This work highlights the potential for broad impacts of fungi on bacterial species within microbiomes.

Rate normalization for sweat metabolomics biomarker discovery

As the demand for real-time exercise performance feedback increases, excreted sweat has become a biosource of interest for continuous human performance assessment. For sweat to truly fulfill this requirement, analyte concentrations must be normalized to adequately assess day-to-day differences within and among individuals. In this manuscript, data are presented highlighting the use of accurate localized sweat rate as a means for ion and global metabolomic data normalization. The results illustrate large sweat rate variability among individuals over the course of two distinct exercises protocols. Furthermore, the data show sweat rate is not symmetrical at similar locations among right and left forearms of individuals (p = 0.0007). Sweat ion conductivity analysis suggest overall sweat rate normalization reduces variability collectively among ion values and participants with principal component analysis showing 77.8% of variation in the data set attributable to sweat rate normalization. Global metabolomic analysis of sweat illustrated overall rate normalization increases the variability among test subjects with 72.7% of the variation explained by sweat rate normalization. Finally, overall rate normalized metabolomic features of sweat significantly correlated (ρ ≥ 0.7, ρ ≤ -0.7) with measured performance metrics of the individual, establishing the potential for sweat to be used as a biosource for performance monitoring. Collectively, these data illustrate the importance of accurate localized sweat rate determination, for analyte data normalization, in support for the use of sweat in biomarker discovery efforts to predict human performance.

The Impacts of Sortase A and the 4'-Phosphopantetheinyl Transferase Homolog Sfp on Streptococcus mutans Extracellular Membrane Vesicle Biogenesis

Extracellular membrane vesicles (EMVs) are produced by many Gram-positive organisms, but information regarding vesiculogenesis is incomplete. We used single gene deletions to evaluate the impacts on Streptococcus mutans EMV biogenesis of Sortase A (SrtA), which affects S. mutans EMV composition, and Sfp, a 4'-phosphopantetheinyl transferase that affects Bacillus subtilis EMV stability. ΔsrtA EMVs were notably larger than Δsfp and wild-type (WT) EMVs. EMV proteins identified from all three strains are known to be involved in cell wall biogenesis and cell architecture, bacterial adhesion, biofilm cell density and matrix development, and microbial competition. Notably, the AtlA autolysin was not processed to its mature active form in the ΔsrtA mutant. Proteomic and lipidomic analyses of all three strains revealed multiple dissimilarities between vesicular and corresponding cytoplasmic membranes (CMs). A higher proportion of EMV proteins are predicted substrates of the general secretion pathway (GSP). Accordingly, the GSP component SecA was identified as a prominent EMV-associated protein. In contrast, CMs contained more multi-pass transmembrane (TM) protein substrates of co-translational transport machineries than EMVs. EMVs from the WT, but not the mutant strains, were enriched in cardiolipin compared to CMs, and all EMVs were over-represented in polyketide flavonoids. EMVs and CMs were rich in long-chain saturated, monounsaturated, and polyunsaturated fatty acids, except for Δsfp EMVs that contained exclusively polyunsaturated fatty acids. Lipoproteins were less prevalent in EMVs of all three strains compared to their CMs. This study provides insight into biophysical characteristics of S. mutans EMVs and indicates discrete partitioning of protein and lipid components between EMVs and corresponding CMs of WT, ΔsrtA, and Δsfp strains.

Prediction of sepsis mortality using metabolite biomarkers in the blood: a meta-analysis of death-related pathways and prospective validation

BACKGROUND

Sepsis is a leading cause of death in intensive care units (ICUs), but outcomes of individual patients are difficult to predict. The recently developed clinical metabolomics has been recognized as a promising tool in the clinical practice of critical illness. The objective of this study was to identify the unique metabolic biomarkers and their pathways in the blood of sepsis nonsurvivors and to assess the prognostic value of these pathways.

METHODS

We searched PubMed, EMBASE, Cochrane, Web of Science, CNKI, Wangfang Data, and CQVIP from inception until July 2019. Eligible studies included the metabolomic analysis of blood samples from sepsis patients with the outcome. The metabolic pathway was assigned to each metabolite biomarker. The meta-analysis was performed using the pooled fold changes, area under the receiver operating characteristic curve (AUROC), and vote-counting of metabolic pathways. We also conducted a prospective cohort metabolomic study to validate the findings of our meta-analysis.

RESULTS

The meta-analysis included 21 cohorts reported in 16 studies with 2509 metabolite comparisons in the blood of 1287 individuals. We found highly limited overlap of the reported metabolite biomarkers across studies. However, these metabolites were enriched in several death-related metabolic pathways (DRMPs) including amino acids, mitochondrial metabolism, eicosanoids, and lysophospholipids. Prediction of sepsis death using DRMPs yielded a pooled AUROC of 0.81 (95% CI 0.76-0.87), which was similar to the combined metabolite biomarkers with a merged AUROC of 0.82 (95% CI 0.78-0.86) (P > 0.05). A prospective metabolomic analysis of 188 sepsis patients (134 survivors and 54 nonsurvivors) using the metabolites from DRMPs produced an AUROC of 0.88 (95% CI 0.78-0.97). The sensitivity and specificity for the prediction of sepsis death were 80.4% (95% CI 66.9-89.4%) and 78.8% (95% CI 62.3-89.3%), respectively.

CONCLUSIONS

DRMP analysis minimizes the discrepancies of results obtained from different metabolomic methods and is more practical than blood metabolite biomarkers for sepsis mortality prediction.

TRIAL REGISTRATION

The meta-analysis was registered on OSF Registries, and the prospective cohort study was registered on the Chinese Clinical Trial Registry (ChiCTR1800015321).

Metabolomics Based on UHPLC-Orbitrap-MS and Global Natural Product Social Molecular Networking Reveals Effects of Time Scale and Environment of Storage on the Metabolites and Taste Quality of Raw Pu-erh Tea

Raw Pu-erh tea (RPT) needs ageing before drinking. However, the influence from environment and time of storage on chemical profile and flavor of RPT is unclear. In this study, the RPTs stored in wet-hot or dry-cold environment for 1-9 years were assessed using metabolomics based on UHPLC-Orbitrap-MS and global natural product social (GNPS) feature-based molecular networking as well as electronic tongue measurement. The results exhibited that the chemical profiles of RPTs were similar at an early stage but started to differentiate from each other at the 5th and the 7th year in wet-hot and dry-cold environments. The discriminating features including N-ethyl-2-pyrrolidinone-substituted flavan-3-ols (flavoalkaloids), unsaturated fatty acids, lysophosphatidylcholines, flavan-3-ols, amino acids, and flavonol-O-glycosides among the three chemical profiles were discovered and analyzed by means of multivariate statistics, GNPS multilibraries matching, and SIRIUS calculation. The metabolomic data were consistent with the results obtained through electronic tongue measurement.

Screening of Microbial Volatile Organic Compounds for Detection of Disease in Cattle: Development of Lab-scale Method

The primary hurdle for diagnosis of some diseases is the long incubation required to culture and confirm the presence of bacteria. The concept of using microbial VOCs as "signature markers" could provide a faster and noninvasive diagnosis. Finding biomarkers is challenging due to the specificity required in complex matrices. The objectives of this study were to (1) build/test a lab-scale platform for screening of microbial VOCs and (2) apply it to Mycobacterium avium paratuberculosis; the vaccine strain of M. bovis Bacillus Calmette-Guérin; and M. kansasii to demonstrate detection times greater those typically required for culture. SPME-GC-MS was used for sampling, sample preparation, and analyses. For objective (1), a testing platform was built for headspace sampling of bacterial cultures grown in standard culture flasks via a biosecure closed-loop circulating airflow system. For (2), results show that the suites of VOCs produced by Mycobacteria ssp. change over time and that individual strains produce different VOCs. The developed method was successful in discriminating between strains using a pooled multi-group analysis, and in timepoint-specific multi- and pair-wise comparisons. The developed testing platform can be useful for minimally invasive and biosecure collection of biomarkers associated with human, wildlife and livestock diseases for development of diagnostic point-of-care and field surveillance.

A Novel Role for Triglyceride Metabolism in Foxp3 Expression

Lipid metabolism plays a key role in many cellular processes. We show here that regulatory T cells have enhanced lipid storage within subcellular lipid droplets (LD). They also express elevated amounts of both isoforms of diacylglycerol acyl transferase (DGAT1 & 2), enzymes required for the terminal step of triacylglycerol synthesis. In regulatory T-cells (Tregs), the conversion of diacylglycerols to triacylglycerols serves two additional purposes other than lipid storage. First, we demonstrate that it protects T cells from the toxic effects of saturated long chain fatty acids. Second, we show that Triglyceride formation is essential for limiting activation of protein kinase C via free diacyl glycerol moieties. Inhibition of DGAT1 resulted in elevated active PKC and nuclear NFKB, as well as impaired Foxp3 induction in response to TGFβ. Thus, Tregs utilize a positive feedback mechanism to promote sustained expression of Foxp3 associated with control of LD formation.

Opposing reactions in coenzyme A metabolism sensitize Mycobacterium tuberculosis to enzyme inhibition

Mycobacterium tuberculosis (Mtb) is the leading infectious cause of death in humans. Synthesis of lipids critical for Mtb's cell wall and virulence depends on phosphopantetheinyl transferase (PptT), an enzyme that transfers 4'-phosphopantetheine (Ppt) from coenzyme A (CoA) to diverse acyl carrier proteins. We identified a compound that kills Mtb by binding and partially inhibiting PptT. Killing of Mtb by the compound is potentiated by another enzyme encoded in the same operon, Ppt hydrolase (PptH), that undoes the PptT reaction. Thus, loss-of-function mutants of PptH displayed antimicrobial resistance. Our PptT-inhibitor cocrystal structure may aid further development of antimycobacterial agents against this long-sought target. The opposing reactions of PptT and PptH uncover a regulatory pathway in CoA physiology.

ROIMCR: a powerful analysis strategy for LC-MS metabolomic datasets

BACKGROUND

The analysis of LC-MS metabolomic datasets appears to be a challenging task in a wide range of disciplines since it demands the highly extensive processing of a vast amount of data. Different LC-MS data analysis packages have been developed in the last few years to facilitate this analysis. However, most of these strategies involve chromatographic alignment and peak shaping and often associate each "feature" (i.e., chromatographic peak) with a unique m/z measurement. Thus, the development of an alternative data analysis strategy that is applicable to most types of MS datasets and properly addresses these issues is still a challenge in the metabolomics field.

RESULTS

Here, we present an alternative approach called ROIMCR to: i) filter and compress massive LC-MS datasets while transforming their original structure into a data matrix of features without losing relevant information through the search of regions of interest (ROIs) in the m/z domain and ii) resolve compressed data to identify their contributing pure components without previous alignment or peak shaping by applying a Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) analysis. In this study, the basics of the ROIMCR method are presented in detail and a detailed description of its implementation is also provided. Data were analyzed using the MATLAB (The MathWorks, Inc., www.mathworks.com ) programming and computing environment. The application of the ROIMCR methodology is described in detail, with an example of LC-MS data generated in a lipidomic study and with other examples of recent applications.

CONCLUSIONS

The methodology presented here combines the benefits of data filtering and compression based on the searching of ROI features, without the loss of spectral accuracy. The method has the benefits of the application of the powerful MCR-ALS data resolution method without the necessity of performing chromatographic peak alignment or modelling. The presented method is a powerful alternative to other existing data analysis approaches that do not use the MCR-ALS method to resolve LC-MS data. The ROIMCR method also represents an improved strategy compared to the direct applications of the MCR-ALS method that use less-powerful data compression strategies such as binning and windowing. Overall, the strategy presented here confirms the usefulness of the ROIMCR chemometrics method for analyzing LC-MS untargeted metabolomics data.

Detection of Volatile Organic Compounds (VOCs) in Urine via Gas Chromatography-Mass Spectrometry QTOF to Differentiate Between Localized and Metastatic Models of Breast Cancer

Breast cancer is the most common cancer detected in women and current screening methods for the disease are not sensitive. Volatile organic compounds (VOCs) include endogenous metabolites that provide information about health and disease which might be useful to develop a better screening method for breast cancer. The goal of this study was to classify mice with and without tumors and compare tumors localized to the mammary pad and tumor cells injected into the iliac artery by differences in VOCs in urine. After 4T1.2 tumor cells were injected into BALB/c mice either in the mammary pad or into the iliac artery, urine was collected, VOCs from urine headspace were concentrated by solid phase microextraction and results were analyzed by gas chromatography-mass spectrometry quadrupole time-of-flight. Multivariate and univariate statistical analyses were employed to find potential biomarkers for breast cancer and metastatic breast cancer in mice models. A set of six VOCs classified mice with and without tumors with an area under the receiver operator characteristic (ROC AUC) of 0.98 (95% confidence interval [0.85, 1.00]) via five-fold cross validation. Classification of mice with tumors in the mammary pad and iliac artery was executed utilizing a different set of six VOCs, with a ROC AUC of 0.96 (95% confidence interval [0.75, 1.00]).

Metabolomics in Stem Cell Biology Research

Stem cell research has been greatly facilitated by comprehensive and integrative multi-omics studies. As a unique approach of functional analysis, metabolomics measures many metabolites and activities of metabolic pathways which can directly indicate cellular energetic status, cell proliferation and fitness, and stem cell fate choices such as self-renewal versus differentiation. Here we describe the methods of applying metabolomics, ¹³C-labeled glucose and glutamine tracing with mouse embryonic stem cells (ES cells), metabolite analysis using mass spectrometry tools, and the following statistical and computational modeling analysis. Integration of these methods into the more common gene expression and epigenetics analysis toolbox will help to generate a more complete picture and in-depth understanding of one's stem cells of interest.

Metabolomics reveals defensive mechanisms adapted by maize on exposure to high molecular weight polycyclic aromatic hydrocarbons

Polycyclic aromatic hydrocarbons are an important group of persistent organic pollutants. Using plants to remediate PAHs has been recognized as a cost-effective and environmentally friendly technique. However, the overall impact of PAHs on the regulation of plant metabolism has not yet been explored. In this study, we analyzed the alteration in the maize (Zea mays L.) metabolome on exposure to high molecular weight PAHs such as benzo[a]pyrene (BaP) and pyrene (PYR) in a hydroponic medium, individually and as a mixture (BaP + PYR) using GC-MS. The differences in the metabolites were analyzed using XCMS (an acronym for various forms (X) of chromatography-mass spectrometry), an online-based data analysis tool. A significant variation in metabolites was observed between treatment groups and the unspiked control group. The univariate, multivariate and pathway impact analysis showed there were more significant alterations in metabolic profiles between individual PAHs and the mixture of BaP and PYR. The marked changes in the metabolites of galactose metabolism and aminoacyl tRNA biosynthesis in PAHs treated maize leaves exhibit the adaptive defensive mechanisms for individual and PAHs mixture. Therefore, the metabolomics approach is essential for an understanding of the complex biochemical responses of plants to PAHs contaminants. This knowledge will shed new light in the field of phytoremediation, bio-monitoring, and environmental risk assessment.

The proteomic and metabolomic characterization of exercise-induced sweat for human performance monitoring: A pilot investigation

Sweat is a biofluid with several attractive attributes. However, investigation into sweat for biomarker discovery applications is still in its infancy. To add support for the use of sweat as a non-invasive media for human performance monitoring, volunteer participants were subjected to a physical exertion model using a treadmill. Following exercise, sweat was collected, aliquotted, and analyzed for metabolite and protein content via high-resolution mass spectrometry. Overall, the proteomic analysis illustrates significant enrichment steps will be required for proteomic biomarker discovery from single sweat samples as protein abundance is low in this medium. Furthermore, the results indicate a potential for protein degradation, or a large number of low molecular weight protein/peptides, in these samples. Metabolomic analysis shows a strong correlation in the overall abundance among sweat metabolites. Finally, hierarchical clustering of participant metabolite abundances show trends emerging, although no significant trends were observed (alpha = 0.8, lambda = 1 standard error via cross validation). However, these data suggest with a greater number of biological replicates, stronger, statistically significant results, can be obtained. Collectively, this study represents the first to simultaneously use both proteomic and metabolomic analysis to investigate sweat. These data highlight several pitfalls of sweat analysis for biomarker discovery applications.

An automated framework for NMR chemical shift calculations of small organic molecules

When using nuclear magnetic resonance (NMR) to assist in chemical identification in complex samples, researchers commonly rely on databases for chemical shift spectra. However, authentic standards are typically depended upon to build libraries experimentally. Considering complex biological samples, such as blood and soil, the entirety of NMR spectra required for all possible compounds would be infeasible to ascertain due to limitations of available standards and experimental processing time. As an alternative, we introduce the in silico Chemical Library Engine (ISiCLE) NMR chemical shift module to accurately and automatically calculate NMR chemical shifts of small organic molecules through use of quantum chemical calculations. ISiCLE performs density functional theory (DFT)-based calculations for predicting chemical properties—specifically NMR chemical shifts in this manuscript—via the open source, high-performance computational chemistry software, NWChem. ISiCLE calculates the NMR chemical shifts of sets of molecules using any available combination of DFT method, solvent, and NMR-active nuclei, using both user-selected reference compounds and/or linear regression methods. Calculated NMR chemical shifts are provided to the user for each molecule, along with comparisons with respect to a number of metrics commonly used in the literature. Here, we demonstrate ISiCLE using a set of 312 molecules, ranging in size up to 90 carbon atoms. For each, calculation of NMR chemical shifts have been performed with 8 different levels of DFT theory, and with solvation effects using the implicit solvent Conductor-like Screening Model. The DFT method dependence of the calculated chemical shifts have been systematically investigated through benchmarking and subsequently compared to experimental data available in the literature. Furthermore, ISiCLE has been applied to a set of 80 methylcyclohexane conformers, combined via Boltzmann weighting and compared to experimental values. We demonstrate that our protocol shows promise in the automation of chemical shift calculations and, ultimately, the expansion of chemical shift libraries.

An improved method for extraction of polar and charged metabolites from cyanobacteria

A key requirement for 13C Metabolic flux analysis (13C-MFA), a widely used technique to estimate intracellular metabolic fluxes, is an efficient method for the extraction of intermediate metabolites for analysis via liquid chromatography mass spectrometry (LC/MS). The 13C isotopic labeling results in further distribution of an already sparse pool of intermediate metabolites into isotopologues, each appearing as a separate chromatographic feature. We examined some of the reported solvent systems for the extraction of polar intracellular metabolites from three strains of cyanobacteria of the genus Synechococcus, viz., Synechococcus sp. PCC 7002, Synechococcus elongatus PCC 7942, and a newly isolated Synechococcus elongatus PCC 11801 (manuscript under review). High resolution-LC/MS was used to assess the relative abundance of the extracted metabolites. The different solvent systems used for extraction led to statistically significant changes in the extraction efficiency for a large number of metabolites. While a few hundred m/z features or potential metabolites were detected with different solvent systems, the abundance of over a quarter of all metabolites varied significantly from one solvent system to another. Further, the extraction methods were evaluated for a targeted set of metabolites that are important in 13C-MFA studies of photosynthetic organisms. While for the strain PCC 7002, the reported method using methanol-chloroform-water system gave satisfactory results, a mild base in the form of NH4OH had to be used in place of water to achieve adequate levels of extraction for PCC 7942 and PCC 11801. While minor changes in extraction solvent resulted in dramatic changes in the extraction efficiency of a number of compounds, certain metabolites such as amino acids and organic acids were adequately extracted in all the solvent systems tested. Overall, we present a new improved method for extraction using a methanol-chloroform-NH4OH system. Our method improves the extraction of polar compounds such as sugar phosphates, bisphosphates, that are central to 13C-MFA studies.

Exploratory Application of Neuropharmacometabolomics in Severe Childhood Traumatic Brain Injury

OBJECTIVES

To employ metabolomics-based pathway and network analyses to evaluate the cerebrospinal fluid metabolome after severe traumatic brain injury in children and the capacity of combination therapy with probenecid and N-acetylcysteine to impact glutathione-related and other pathways and networks, relative to placebo treatment.

DESIGN

Analysis of cerebrospinal fluid obtained from children enrolled in an Institutional Review Board-approved, randomized, placebo-controlled trial of a combination of probenecid and N-acetylcysteine after severe traumatic brain injury (Trial Registration NCT01322009).

SETTING

Thirty-six-bed PICU in a university-affiliated children's hospital.

PATIENTS AND SUBJECTS

Twelve children 2-18 years old after severe traumatic brain injury and five age-matched control subjects.

INTERVENTION

Probenecid (25 mg/kg) and N-acetylcysteine (140 mg/kg) or placebo administered via naso/orogastric tube.

MEASUREMENTS AND MAIN RESULTS

The cerebrospinal fluid metabolome was analyzed in samples from traumatic brain injury patients 24 hours after the first dose of drugs or placebo and control subjects. Feature detection, retention time, alignment, annotation, and principal component analysis and statistical analysis were conducted using XCMS-online. The software "mummichog" was used for pathway and network analyses. A two-component principal component analysis revealed clustering of each of the groups, with distinct metabolomics signatures. Several novel pathways with plausible mechanistic involvement in traumatic brain injury were identified. A combination of metabolomics and pathway/network analyses showed that seven glutathione-centered pathways and two networks were enriched in the cerebrospinal fluid of traumatic brain injury patients treated with probenecid and N-acetylcysteine versus placebo-treated patients. Several additional pathways/networks consisting of components that are known substrates of probenecid-inhibitable transporters were also identified, providing additional mechanistic validation.

CONCLUSIONS

This proof-of-concept neuropharmacometabolomics assessment reveals alterations in known and previously unidentified metabolic pathways and supports therapeutic target engagement of the combination of probenecid and N-acetylcysteine treatment after severe traumatic brain injury in children.

Serum metabolomic profiling predicts synovial gene expression in rheumatoid arthritis

Background

Metabolomics is an emerging field of biomedical research that may offer a better understanding of the mechanisms of underlying conditions including inflammatory arthritis. Perturbations caused by inflamed synovial tissue can lead to correlated changes in concentrations of certain metabolites in the synovium and thereby function as potential biomarkers in blood. Here, we explore the hypothesis of whether characterization of patients’ metabolomic profiles in blood, utilizing ¹H-nuclear magnetic resonance (NMR), predicts synovial marker profiling in rheumatoid arthritis (RA).

Methods

Nineteen active, seropositive patients with RA, on concomitant methotrexate, were studied. One of the involved joints was a knee or a wrist appropriate for arthroscopy. A Bruker Avance 700 MHz spectrometer was used to acquire NMR spectra of serum samples. Gene expression in synovial tissue obtained by arthroscopy was analyzed by real-time PCR. Data processing and statistical analysis were performed in Python and SPSS.

Results

Analysis of the relationships between each synovial marker-metabolite pair using linear regression and controlling for age and gender revealed significant clustering within the data. We observed an association of serine/glycine/phenylalanine metabolism and aminoacyl-tRNA biosynthesis with lymphoid cell gene signature. Alanine/aspartate/glutamate metabolism and choline-derived metabolites correlated with TNF-α synovial expression. Circulating ketone bodies were associated with gene expression of synovial metalloproteinases. Discriminant analysis identified serum metabolites that classified patients according to their synovial marker levels.

Conclusion

The relationship between serum metabolite profiles and synovial biomarker profiling suggests that NMR may be a promising tool for predicting specific pathogenic pathways in the inflamed synovium of patients with RA.

Electronic supplementary material

The online version of this article (10.1186/s13075-018-1655-3) contains supplementary material, which is available to authorized users.

Metabolome changes are induced in the arbuscular mycorrhizal fungus Gigaspora margarita by germination and by its bacterial endosymbiont

Metabolomic profiling is becoming an increasingly important technique in the larger field of systems biology by allowing the simultaneous measurement of thousands of small molecules participating in and resulting from cellular reactions. In this way, metabolomics presents an opportunity to observe the physiological state of a system, which may provide the ability to monitor the whole of cellular metabolism as the technology progresses. The arbuscular mycorrhizal fungus Gigaspora margarita has not previously been explored with regard to metabolite composition. To develop a better understanding of G. margarita and the influences of its endosymbiont Candidatus Glomeribacter gigasporarum, a metabolomic analysis was applied to quiescent and germinated spores with and without endobacteria. Over 100 metabolites were identified and greater than 2600 unique unidentified spectral features were observed. Multivariate analysis of the metabolomes was performed, and a differentiation between all metabolic states of spores and spores hosting the endobacteria was observed. The known metabolites were recruited to many biochemical pathways, with many being involved in maintenance of the antioxidant potential, tyrosine metabolism, and melanin production. Each of the pathways had higher metabolite abundances in the presence of the endosymbiont. These metabolomics data also agree with previously reported transcriptomics results demonstrating the capability of this technique to confirm hypotheses and showing the feasibility of multi-omic approaches for the study of arbuscular mycorrhizal fungi and their endobacterial communities. Challenges still exist in metabolomic analysis, e.g., the identification of compounds is demanding due to incomplete libraries. A metabolomics technique to probe the effects of bacterial endosymbionts on fungal physiology is presented herein, and this method is useful for hypothesis generation as well as testing as noted above.

Spontaneous DNA damage to the nuclear genome promotes senescence, redox imbalance and aging

Accumulation of senescent cells over time contributes to aging and age-related diseases. However, what drives senescence in vivo is not clear. Here we used a genetic approach to determine if spontaneous nuclear DNA damage is sufficient to initiate senescence in mammals. Ercc1-/∆ mice with reduced expression of ERCC1-XPF endonuclease have impaired capacity to repair the nuclear genome. Ercc1-/∆ mice accumulated spontaneous, oxidative DNA damage more rapidly than wild-type (WT) mice. As a consequence, senescent cells accumulated more rapidly in Ercc1-/∆ mice compared to repair-competent animals. However, the levels of DNA damage and senescent cells in Ercc1-/∆ mice never exceeded that observed in old WT mice. Surprisingly, levels of reactive oxygen species (ROS) were increased in tissues of Ercc1-/∆ mice to an extent identical to naturally-aged WT mice. Increased enzymatic production of ROS and decreased antioxidants contributed to the elevation in oxidative stress in both Ercc1-/∆ and aged WT mice. Chronic treatment of Ercc1-/∆ mice with the mitochondrial-targeted radical scavenger XJB-5-131 attenuated oxidative DNA damage, senescence and age-related pathology. Our findings indicate that nuclear genotoxic stress arises, at least in part, due to mitochondrial-derived ROS, and this spontaneous DNA damage is sufficient to drive increased levels of ROS, cellular senescence, and the consequent age-related physiological decline.

Intriguing Interaction of Bacteriophage-Host Association: An Understanding in the Era of Omics

Innovations in next-generation sequencing technology have introduced new avenues in microbial studies through “omics” approaches. This technology has considerably augmented the knowledge of the microbial world without isolation prior to their identification. With an enormous volume of bacterial “omics” data, considerable attempts have been recently invested to improve an insight into virosphere. The interplay between bacteriophages and their host has created a significant influence on the biogeochemical cycles, microbial diversity, and bacterial population regulation. This review highlights various concepts such as genomics, transcriptomics, proteomics, and metabolomics to infer the phylogenetic affiliation and function of bacteriophages and their impact on diverse microbial communities. Omics technologies illuminate the role of bacteriophage in an environment, the influences of phage proteins on the bacterial host and provide information about the genes important for interaction with bacteria. These investigations will reveal some of bio-molecules and biomarkers of the novel phage which demand to be unveiled.

Chemometric analysis of comprehensive two dimensional gas chromatography-mass spectrometry metabolomics data

Comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS) is a powerful tool for separation and identification of analytes in complex natural samples. In this paper, different chemometric methods were compared for fast non-targeted GC×GC-TOFMS metabolomic profiling of the crustaceous species Daphnia magna and a general chemometric strategy and workflow is proposed. The strategy proposed in this work combined the compression of GC×GC-TOFMS data matrices in the retention time direction using wavelets and the appropriate column-wise data matrix augmentation arrangement, and its modeling by Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS). Using the proposed strategy, eighty different D. magna metabolites were resolved and identified. After calculation of the peak capacities of different columns and peak area changes of these metabolites, the best instrumental configuration and column combination for the GC×GC-TOFMS metabolomic study of D. magna are proposed and discussed. The procedure described in this work can be applied as a general method for untargeted GC×GC-TOFMS data processing and metabolomic profiling.

Metabolomic analysis of human oral cancer cells with adenylate kinase 2 or phosphorylate glycerol kinase 1 inhibition

The purpose of this study was to use liquid chromatography-mass spectrometry (LC-MS) with XCMS for a quantitative metabolomic analysis of UM1 and UM2 oral cancer cells after knockdown of metabolic enzyme adenylate kinase 2 (AK2) or phosphorylate glycerol kinase 1 (PGK1). UM1 and UM2 cells were initially transfected with AK2 siRNA, PGK1 siRNA or scrambled control siRNA, and then analyzed with LC-MS for metabolic profiles. XCMS analysis of the untargeted metabolomics data revealed a total of 3200-4700 metabolite features from the transfected UM1 or UM2 cancer cells and 369-585 significantly changed metabolites due to AK2 or PGK1 suppression. In addition, cluster analysis showed that a common group of metabolites were altered by AK2 knockdown or by PGK1 knockdown between the UM1 and UM2 cells. However, the set of significantly changed metabolites due to AK2 knockdown was found to be distinct from those significantly changed by PGK1 knockdown. Our study has demonstrated that LC-MS with XCMS is an efficient tool for metabolomic analysis of oral cancer cells, and knockdown of different genes results in distinct changes in metabolic phenotypes in oral cancer cells.

Comparative mass spectrometry-based metabolomics strategies for the investigation of microbial secondary metabolites

Covering: 2000 to 2016The labor-intensive process of microbial natural product discovery is contingent upon identifying discrete secondary metabolites of interest within complex biological extracts, which contain inventories of all extractable small molecules produced by an organism or consortium. Historically, compound isolation prioritization has been driven by observed biological activity and/or relative metabolite abundance and followed by dereplication via accurate mass analysis. Decades of discovery using variants of these methods has generated the natural pharmacopeia but also contributes to recent high rediscovery rates. However, genomic sequencing reveals substantial untapped potential in previously mined organisms, and can provide useful prescience of potentially new secondary metabolites that ultimately enables isolation. Recently, advances in comparative metabolomics analyses have been coupled to secondary metabolic predictions to accelerate bioactivity and abundance-independent discovery work flows. In this review we will discuss the various analytical and computational techniques that enable MS-based metabolomic applications to natural product discovery and discuss the future prospects for comparative metabolomics in natural product discovery.

The Lipopolysaccharide-Induced Metabolome Signature in Arabidopsis thaliana Reveals Dynamic Reprogramming of Phytoalexin and Phytoanticipin Pathways

Lipopolysaccharides (LPSs), as MAMP molecules, trigger the activation of signal transduction pathways involved in defence. Currently, plant metabolomics is providing new dimensions into understanding the intracellular adaptive responses to external stimuli. The effect of LPS on the metabolomes of Arabidopsis thaliana cells and leaf tissue was investigated over a 24 h period. Cellular metabolites and those secreted into the medium were extracted with methanol and liquid chromatography coupled to mass spectrometry was used for quantitative and qualitative analyses. Multivariate statistical data analyses were used to extract interpretable information from the generated multidimensional LC-MS data. The results show that LPS perception triggered differential changes in the metabolomes of cells and leaves, leading to variation in the biosynthesis of specialised secondary metabolites. Time-dependent changes in metabolite profiles were observed and biomarkers associated with the LPS-induced response were tentatively identified. These include the phytohormones salicylic acid and jasmonic acid, and also the associated methyl esters and sugar conjugates. The induced defensive state resulted in increases in indole-and other glucosinolates, indole derivatives, camalexin as well as cinnamic acid derivatives and other phenylpropanoids. These annotated metabolites indicate dynamic reprogramming of metabolic pathways that are functionally related towards creating an enhanced defensive capacity. The results reveal new insights into the mode of action of LPS as an activator of plant innate immunity, broadens knowledge about the defence metabolite pathways involved in Arabidopsis responses to LPS, and identifies specialised metabolites of functional importance that can be employed to enhance immunity against pathogen infection.

Training in metabolomics research. II. Processing and statistical analysis of metabolomics data, metabolite identification, pathway analysis, applications of metabolomics and its future

Metabolomics, a systems biology discipline representing analysis of known and unknown pathways of metabolism, has grown tremendously over the past 20 years. Because of its comprehensive nature, metabolomics requires careful consideration of the question(s) being asked, the scale needed to answer the question(s), collection and storage of the sample specimens, methods for extraction of the metabolites from biological matrices, the analytical method(s) to be employed and the quality control of the analyses, how collected data are correlated, the statistical methods to determine metabolites undergoing significant change, putative identification of metabolites and the use of stable isotopes to aid in verifying metabolite identity and establishing pathway connections and fluxes. This second part of a comprehensive description of the methods of metabolomics focuses on data analysis, emerging methods in metabolomics and the future of this discipline. Copyright © 2016 John Wiley & Sons, Ltd.

Use of metabolomics and lipidomics to evaluate the hypocholestreolemic effect of Proanthocyanidins from grape seed in a pig model

SCOPE

This work aims to evaluate changes in the fecal metabolomic profile due to grape seed extract (GSE) intake by untargeted and targeted analysis using high resolution mass spectrometry in conjunction with multivariate statistics.

METHODS AND RESULTS

An intervention study with six crossbred female pigs was performed. The pigs followed a standard diet for 3 days, then they were fed with a supplemented diet containing 1% (w/w) of MegaNatural® Gold grape seed extract for 6 days. Fresh pig fecal samples were collected daily. A combination of untargeted high resolution mass spectrometry, multivariate analysis (PLS-DA), data-dependent MS/MS scan, and accurate mass database matching was used to measure the effect of the treatment on fecal composition. The resultant PLS-DA models showed a good discrimination among classes with great robustness and predictability. A total of 14 metabolites related to the GSE consumption were identified including biliary acid, dicarboxylic fatty acid, cholesterol metabolites, purine metabolites, and eicosanoid metabolites among others. Moreover, targeted metabolomics using GC-MS showed that cholesterol and its metabolites fecal excretion was increased due to the proanthocyanidins from grape seed extract.

CONCLUSION

The results show that oligomeric procyanidins from GSE modifies bile acid and steroid excretion, which could exert a hypocholesterolemic effect.

Advanced Multidimensional Separations in Mass Spectrometry: Navigating the Big Data Deluge

Hybrid analytical instrumentation constructed around mass spectrometry (MS) is becoming the preferred technique for addressing many grand challenges in science and medicine. From the omics sciences to drug discovery and synthetic biology, multidimensional separations based on MS provide the high peak capacity and high measurement throughput necessary to obtain large-scale measurements used to infer systems-level information. In this article, we describe multidimensional MS configurations as technologies that are big data drivers and review some new and emerging strategies for mining information from large-scale datasets. We discuss the information content that can be obtained from individual dimensions, as well as the unique information that can be derived by comparing different levels of data. Finally, we summarize some emerging data visualization strategies that seek to make highly dimensional datasets both accessible and comprehensible.

Phenylpropanoid Defences in Nicotiana tabacum Cells: Overlapping Metabolomes Indicate Common Aspects to Priming Responses Induced by Lipopolysaccharides, Chitosan and Flagellin-22

Plants have evolved both constitutive and inducible defence strategies to cope with different biotic stimuli and stresses. Exposure of a plant to a challenging stress can lead to a primed state that allows it to launch a more rapid and stronger defence. Here we applied a metabolomic approach to study and compare the responses induced in Nicotiana tabacum cells by microbe-associated molecular pattern (MAMP) molecules, namely lipopolysaccharides (LPS), chitosan (CHT) and flagellin-22 (FLG22). Early response metabolites, extracted with methanol, were analysed by UHPLC-MS/MS. Using multivariate statistical tools the metabolic profiles induced by these elicitors were analysed. In the metabolic fingerprint of these agents a total of 19 cinnamic acid derivatives conjugated to quinic acids (chlorogenic acids), shikimic acid, tyramine, polyamines or glucose were found as discriminant biomarkers. In addition, treatment with the phytohormones salicylic acid (SA), methyljasmonic acid (MJ) and abscisic acid (ABA) resulted in differentially-induced phenylpropanoid pathway metabolites. The results indicate that the phenylpropanoid pathway is activated by these elicitors while hydroxycinnamic acid derivatives are commonly associated with the metabolic response to the MAMPs, and that the activated responses are modulated by both SA and MJ, with ABA not playing a role.

Using "Omics" and Integrated Multi-Omics Approaches to Guide Probiotic Selection to Mitigate Chytridiomycosis and Other Emerging Infectious Diseases

Emerging infectious diseases in wildlife are responsible for massive population declines. In amphibians, chytridiomycosis caused by Batrachochytrium dendrobatidis, Bd, has severely affected many amphibian populations and species around the world. One promising management strategy is probiotic bioaugmentation of antifungal bacteria on amphibian skin. In vivo experimental trials using bioaugmentation strategies have had mixed results, and therefore a more informed strategy is needed to select successful probiotic candidates. Metagenomic, transcriptomic, and metabolomic methods, colloquially called "omics," are approaches that can better inform probiotic selection and optimize selection protocols. The integration of multiple omic data using bioinformatic and statistical tools and in silico models that link bacterial community structure with bacterial defensive function can allow the identification of species involved in pathogen inhibition. We recommend using 16S rRNA gene amplicon sequencing and methods such as indicator species analysis, the Kolmogorov-Smirnov Measure, and co-occurrence networks to identify bacteria that are associated with pathogen resistance in field surveys and experimental trials. In addition to 16S amplicon sequencing, we recommend approaches that give insight into symbiont function such as shotgun metagenomics, metatranscriptomics, or metabolomics to maximize the probability of finding effective probiotic candidates, which can then be isolated in culture and tested in persistence and clinical trials. An effective mitigation strategy to ameliorate chytridiomycosis and other emerging infectious diseases is necessary; the advancement of omic methods and the integration of multiple omic data provide a promising avenue toward conservation of imperiled species.

Tools for the Microbiome: Nano and Beyond

The microbiome presents great opportunities for understanding and improving the world around us and elucidating the interactions that compose it. The microbiome also poses tremendous challenges for mapping and manipulating the entangled networks of interactions among myriad diverse organisms. Here, we describe the opportunities, technical needs, and potential approaches to address these challenges, based on recent and upcoming advances in measurement and control at the nanoscale and beyond. These technical needs will provide the basis for advancing the largely descriptive studies of the microbiome to the theoretical and mechanistic understandings that will underpin the discipline of microbiome engineering. We anticipate that the new tools and methods developed will also be more broadly useful in environmental monitoring, medicine, forensics, and other areas.

Global metabolomics reveals metabolic dysregulation in ischemic retinopathy

Proliferative diabetic retinopathy (PDR) is the most severe form of diabetic retinopathy and, along with diabetic macular edema, is responsible for the majority of blindness in adults below the age of 65. Therapeutic strategies for PDR are ineffective at curtailing disease progression in all cases; however a deeper understanding of the ocular metabolic landscape in PDR through metabolomic analysis may offer new therapeutic targets. Here, global and targeted mass spectrometry-based metabolomics were used to investigate metabolism. Initial analyses on vitreous humor from patients with PDR (n = 9) and non-diabetic controls (n = 11) revealed an increase of arginine and acylcarnitine metabolism in PDR. The oxygen-induced-retinopathy (OIR) mouse model, which exhibits comparable pathological manifestations to human PDR, revealed similar increases of arginine and other metabolites in the urea cycle, as well as downregulation of purine metabolism. We validated our findings by targeted multiple reaction monitoring and through the analysis of a second set of patient samples [PDR (n = 11) and non-diabetic controls (n = 20)]. These results confirmed a predominant and consistent increase in proline in both the OIR mouse model and vitreous samples from patients with PDR, suggesting that over activity in the arginine-to-proline pathway could be used as a therapeutic target in diabetic retinopathy.

Profiling of Altered Metabolomic States in Nicotiana tabacum Cells Induced by Priming Agents

Metabolomics has developed into a valuable tool for advancing our understanding of plant metabolism. Plant innate immune defenses can be activated and enhanced so that, subsequent to being pre-sensitized, plants are able to launch a stronger and faster defense response upon exposure to pathogenic microorganisms, a phenomenon known as priming. Here, three contrasting chemical activators, namely acibenzolar-S-methyl, azelaic acid and riboflavin, were used to induce a primed state in Nicotiana tabacum cells. Identified biomarkers were then compared to responses induced by three phytohormones-abscisic acid, methyljasmonate, and salicylic acid. Altered metabolomes were studied using a metabolite fingerprinting approach based on liquid chromatography and mass spectrometry. Multivariate data models indicated that these inducers cause time-dependent metabolic perturbations in the cultured cells and revealed biomarkers of which the levels are affected by these agents. A total of 34 metabolites were annotated from the mass spectral data and online databases. Venn diagrams were used to identify common biomarkers as well as those unique to a specific agent. Results implicate 20 cinnamic acid derivatives conjugated to (i) quinic acid (chlorogenic acids), (ii) tyramine, (iii) polyamines, or (iv) glucose as discriminatory biomarkers of priming in tobacco cells. Functional roles for most of these metabolites in plant defense responses could thus be proposed. Metabolites induced by the activators belong to the early phenylpropanoid pathway, which indicates that different stimuli can activate similar pathways but with different metabolite fingerprints. Possible linkages to phytohormone-dependent pathways at a metabolomic level were indicated in the case of cells treated with salicylic acid and methyljasmonate. The results contribute to a better understanding of the priming phenomenon and advance our knowledge of cinnamic acid derivatives as versatile defense metabolites.

Pharmacometabolomics of l-carnitine treatment response phenotypes in patients with septic shock

RATIONALE

Sepsis therapeutics have a poor history of success in clinical trials, due in part to the heterogeneity of enrolled patients. Pharmacometabolomics could differentiate drug response phenotypes and permit a precision medicine approach to sepsis.

OBJECTIVES

To use existing serum samples from the phase 1 clinical trial of l-carnitine treatment for severe sepsis to metabolically phenotype l-carnitine responders and nonresponders.

METHODS

Serum samples collected before (T0) and after completion of the infusion (T24, T48) from patients randomized to either l-carnitine (12 g) or placebo for the treatment of vasopressor-dependent septic shock were assayed by untargeted (1)H-nuclear magnetic resonance metabolomics. The normalized, quantified metabolite data sets of l-carnitine- and placebo-treated patients at each time point were compared by analysis of variance with post-hoc testing for multiple comparisons. Pathway analysis was performed to statistically rank metabolic networks.

MEASUREMENTS AND MAIN RESULTS

Thirty-eight metabolites were identified in all samples. Concentrations of 3-hydroxybutyrate, acetoacetate, and 3-hydroxyisovalerate were different at T0 and over time in l-carnitine-treated survivors versus nonsurvivors. Pathway analysis of pretreatment metabolites revealed that synthesis and degradation of ketone bodies had the greatest impact in differentiating l-carnitine treatment response. Analysis of all patients based on pretreatment 3-hydroxybutyrate concentration yielded distinct phenotypes. Using the T0 median 3-hydroxybutyrate level (153 μM), patients were categorized as either high or low ketone. l-Carnitine-treated low-ketone patients had greater use of carnitine as evidenced by lower post-treatment l-carnitine levels. The l-carnitine responders also had faster resolution of vasopressor requirement and a trend toward a greater improvement in mortality at 1 year (P = 0.038) compared with patients with higher 3-hydroxybutyrate.

CONCLUSIONS

The results of this preliminary study, which were not readily apparent from the parent clinical trial, show a unique metabolite profile of l-carnitine responders and introduce pharmacometabolomics as a viable strategy for informing l-carnitine responsiveness. The approach taken in this study represents a concrete example for the application of precision medicine to sepsis therapeutics that warrants further study.

Fanconi Anemia Mesenchymal Stromal Cells-Derived Glycerophospholipids Skew Hematopoietic Stem Cell Differentiation Through Toll-Like Receptor Signaling

Fanconi anemia (FA) patients develop bone marrow (BM) failure or leukemia. One standard care for these devastating complications is hematopoietic stem cell transplantation. We identified a group of mesenchymal stromal cells (MSCs)-derived metabolites, glycerophospholipids, and their endogenous inhibitor, 5-(tetradecyloxy)-2-furoic acid (TOFA), as regulators of donor hematopoietic stem and progenitor cells. We provided two pieces of evidence that TOFA could improve hematopoiesis-supporting function of FA MSCs: (a) limiting-dilution cobblestone area-forming cell assay revealed that TOFA significantly increased cobblestone colonies in Fanca-/- or Fancd2-/- cocultures compared to untreated cocultures. (b) Competitive repopulating assay using output cells collected from cocultures showed that TOFA greatly alleviated the abnormal expansion of the donor myeloid (CD45.2+Gr1+Mac1+) compartment in both peripheral blood and BM of recipient mice transplanted with cells from Fanca-/- or Fancd2-/- cocultures. Furthermore, mechanistic studies identified Tlr4 signaling as the responsible pathway mediating the effect of glycerophospholipids. Thus, targeting glycerophospholipid biosynthesis in FA MSCs could be a therapeutic strategy to improve hematopoiesis and stem cell transplantation.

Modeling and Classification of Kinetic Patterns of Dynamic Metabolic Biomarkers in Physical Activity

The objectives of this work were the classification of dynamic metabolic biomarker candidates and the modeling and characterization of kinetic regulatory mechanisms in human metabolism with response to external perturbations by physical activity. Longitudinal metabolic concentration data of 47 individuals from 4 different groups were examined, obtained from a cycle ergometry cohort study. In total, 110 metabolites (within the classes of acylcarnitines, amino acids, and sugars) were measured through a targeted metabolomics approach, combining tandem mass spectrometry (MS/MS) with the concept of stable isotope dilution (SID) for metabolite quantitation. Biomarker candidates were selected by combined analysis of maximum fold changes (MFCs) in concentrations and P-values resulting from statistical hypothesis testing. Characteristic kinetic signatures were identified through a mathematical modeling approach utilizing polynomial fitting. Modeled kinetic signatures were analyzed for groups with similar behavior by applying hierarchical cluster analysis. Kinetic shape templates were characterized, defining different forms of basic kinetic response patterns, such as sustained, early, late, and other forms, that can be used for metabolite classification. Acetylcarnitine (C2), showing a late response pattern and having the highest values in MFC and statistical significance, was classified as late marker and ranked as strong predictor (MFC = 1.97, P < 0.001). In the class of amino acids, highest values were shown for alanine (MFC = 1.42, P < 0.001), classified as late marker and strong predictor. Glucose yields a delayed response pattern, similar to a hockey stick function, being classified as delayed marker and ranked as moderate predictor (MFC = 1.32, P < 0.001). These findings coincide with existing knowledge on central metabolic pathways affected in exercise physiology, such as β-oxidation of fatty acids, glycolysis, and glycogenolysis. The presented modeling approach demonstrates high potential for dynamic biomarker identification and the investigation of kinetic mechanisms in disease or pharmacodynamics studies using MS data from longitudinal cohort studies.

Human metabolism is controlled through basic kinetic regulatory mechanisms, where the overall system aims to maintain a state of homeostasis. In response to external perturbations, such as environmental influences, nutrition or physical exercise, circulating metabolites show specific kinetic response patterns, which can be computationally modeled. In this work, we searched for dynamic metabolic biomarker candidates and analyzed specific kinetic mechanisms from longitudinal metabolic concentration data, obtained through a cycle ergometry stress test. In total, 110 metabolites measured from blood samples of 47 individuals were analyzed using tandem mass spectrometry (MS/MS). Dynamic biomarker candidates could be selected based on the amplitudes of changes in metabolite concentrations and the significance of statistical hypothesis testing. We were able to characterize specific kinetic patterns for groups of similarly behaving metabolites. Kinetic shape templates were identified, defining basic kinetic response patterns to physical exercise, such as sustained, early, late and other shape forms. The presented approach contributes to a better understanding of (patho)physiological biochemical mechanisms in human health, disease or during drug therapy, by offering tools for classifying dynamic biomarker candidates and for modeling and characterizing kinetic regulatory mechanisms from longitudinal experimental data.

An Interactive Cluster Heat Map to Visualize and Explore Multidimensional Metabolomic Data

Heat maps are a commonly used visualization tool for metabolomic data where the relative abundance of ions detected in each sample is represented with color intensity. A limitation of applying heat maps to global metabolomic data, however, is the large number of ions that have to be displayed and the lack of information provided about important metabolomic parameters such as m/z and retention time. Here we address these challenges by introducing the interactive cluster heat map in the data-processing software XCMS Online. XCMS Online (xcmsonline.scripps.edu) is a cloud-based informatic platform designed to process, statistically evaluate, and visualize mass-spectrometry based metabolomic data. An interactive heat map is provided for all data processed by XCMS Online. The heat map is clickable, allowing users to zoom and explore specific metabolite metadata (EICs, Box-and-whisker plots, mass spectra) that are linked to the METLIN metabolite database. The utility of the XCMS interactive heat map is demonstrated on metabolomic data set generated from different anatomical regions of the mouse brain.

A comparative LC-MS based profiling approach to analyze lipid composition in tissue culture systems

Although lipids participate in many cellular processes both as signaling and structural molecules, our understanding of the roles of individual lipids as well as global changes in lipid composition are limited. Here we describe an LC-MS based method to identify lipids that change in a biological process. This method describes the isolation of lipids from tissue culture cells, sample preparation for LC-MS, the LC-MS run, and the subsequent data processing steps to compare the global lipid profiles and identify species that are enhanced or depleted. Identifying lipids that change is the first step towards functional studies to unravel their roles.