METLIN: A Technology Platform for Identifying Knowns and Unknowns

Identification of novel serum biomarker for the detection of acute myeloid leukemia based on liquid chromatography-mass spectrometry

Acute myeloid leukemia (AML) is a life-threatening hematological malignancy. Traditional diagnosis of AML depends on invasive bone marrow biopsies. To recognize the metabolic characteristics related with AML and search for early non-invasive biomarkers of AML, in this work we applied ultra-high performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry (UHPLC-Q-TOFMS)-based metabolomoc method to profile serum metabolites from 55 de novo AML patients and 45 age- and gender-matched healthy subjects and to screen and validate AML biomarkers. We observed AML-related metabolic differences mainly involved in alanine, aspartate and glutamate metabolism; d-Glutamine and d-glutamate metabolism; tryptophan metabolism; taurine and hypotaurine metabolism; and phenylalanine metabolism as well as fatty acid metabolism. A serum metabolite biomarker panel consisting of glutamic acid, kynurenine and oleic acid was defined and validated based on binary logistic regression analysis and receiver operating characteristic curves (ROC) analysis, yielding an area under the ROC curve (AUC) of 0.981 with 0.975 sensitivity and 0.933 specificity in the discovery set and an AUC of 0.973 with 0.933 sensitivity and 0.933 specificity in the validation set. This work demonstrated the UHPLC- MS-based metabolomics as a low invasive potential tool for the detection of AML, and this composite serum metabolite panel exhibited good diagnostic performance for AML in this case-control study and deserved further validation in a large-scale clinical trial. The identified metabolic pathways were also potentially worthy of further studying the pathogenesis of AML.

RANCM: a new ranking scheme for assigning confidence levels to metabolite assignments in NMR-based metabolomics studies

INTRODUCTION

The Metabolomics Standards Initiative has recommended four categories for metabolite assignments in NMR-based metabolic profiling studies. The "putatively annotated compound" category is most commonly reported by metabolomics investigators. However, there is significant ambiguity in reliability of "putatively annotated compound" assignments, which can range from low confidence made on minimal corroborating data to high confidence made on substantial corroborating data.

OBJECTIVES

To introduce a new ranking system, Rank and AssigN Confidence to Metabolites (RANCM), to assign confidence levels to "putatively annotated compound" assignments in NMR-based metabolic profiling studies.

METHODS

The ranking system was constructed with three confidence levels ranging from Rank 1 for the lowest confidence assignment level to Rank 3 for the highest confidence assignment level. A decision tree was constructed to guide rank selection for each metabolite assignment.

RESULTS

Examples are provided from experimental data demonstrating how to use the decision tree to make confidence level assignments to "putatively annotated compounds" in each of the three rank levels. A standard Excel sheet template is provided to facilitate decision-making, documentation and submission to data repositories.

CONCLUSION

RANCM is intended to reduce the ambiguity in "putatively annotated compound" assignments, to facilitate effective communication of the degree of confidence in "putatively annotated compound" assignments, and to make it easier for non-experts to evaluate the significance and reliability of NMR-based metabonomics studies. The system is straightforward to implement, based on the most common datasets collected in NMR-based metabolic profiling studies, and can be used with equal rigor and significance with any set of NMR datasets.

Integration of Metabolomics and Transcriptomics to Identify Gene-Metabolite Relationships Specific to Phenotype

Metabolomics plays an increasingly large role in translational research, with metabolomics data being generated in large cohorts, alongside other omics data such as gene expression. With this in mind, we provide a review of current approaches that integrate metabolomic and transcriptomic data. Furthermore, we provide a detailed framework for integrating metabolomic and transcriptomic data using a two-step approach: (1) numerical integration of gene and metabolite levels to identify phenotype (e.g., cancer)-specific gene-metabolite relationships using IntLIM and (2) knowledge-based integration, using pathway overrepresentation analysis through RaMP, a comprehensive database of biological pathways. Each step makes use of publicly available R packages ( https://github.com/mathelab/IntLIM and https://github.com/mathelab/RaMP-DB ), and provides a user-friendly web interface for analysis. These interfaces can be run locally through the package or can be accessed through our servers ( https://intlim.bmi.osumc.edu and https://ramp-db.bmi.osumc.edu ). The goal of this chapter is to provide step-by-step instructions on how to install the software and use the commands within the R framework, without the user interface (which is slower than running the commands through command line). Both packages are in continuous development so please refer to the GitHub sites to check for updates.

UHPLC-QTOF-MS/MS-SPE-NMR: A Solution to the Metabolomics Grand Challenge of Higher-Throughput, Confident Metabolite Identifications

Metabolomics represents a powerful, complementary approach for studying biological system responses to various biotic and abiotic stimuli. A major challenge in metabolomics is the lack of reliable annotations for all metabolites detected in complex MS and/or NMR data. To meet this challenge, we have developed an integrated UHPLC-QTOF-MS/MS-SPE-NMR system for higher-throughput metabolite identifications, which provides advanced biological context and enhances the scientific value of metabolomics data for understanding systems biology. This integrated instrumental method is less labor-intensive and more cost-effective than conventional individual methods (LC; MS; SPE; NMR). It enables the simultaneous purification and identification of primary and secondary metabolites present in biological samples. In this chapter, we describe the configuration and use of UHPLC-MS/MS-SPE-NMR in metabolite analyses ranging from sample extraction to higher-throughput metabolite annotation. With the integrated UHPLC-QTOF-MS/MS-SPE-NMR method, we have purified and confidently identified more than 100 previously known as well as unknown triterpene and flavonoid glycosides while noting that most of the identified compounds are not commercially available.

Personalized Metabolomics

The human metabolome is the cumulative product of ingested metabolites and those produced by the body and its microbiota. Together these metabolites can dynamically report on the health and disease state of an individual, as well as their response to drug treatments and other external perturbations. Profiling metabolites in human body fluids provides an opportunity to identify biomarkers and stratify patients for personalized treatments but requires the development of high-throughput approaches compatible with large cohort and longitudinal studies. Here we review in detail sample preparation and analytical liquid chromatography-mass spectrometry (LC-MS) methods to measure the broad chemical diversity of metabolites found in human plasma and urine.

Sensitive untargeted identification of short hydrophilic peptides by high performance liquid chromatography on porous graphitic carbon coupled to high resolution mass spectrometry

The combination of an efficient chromatographic separation with post-column addition of a supercharging agent was evaluated for the determination of small peptides. The procedure takes advantage of porous graphitic carbon (PGC) ability in retaining very polar and ionic molecules to overstep the poor retention of small peptides on conventional reversed phase (RP) columns. The method was developed specifically for the most hydrophilic di-, tri- and tetrapeptides, which are not identified in ordinary peptidomics experiments. In addition to retention mechanisms acting on conventional RP, the method exploited the charge induced interactions generated by the charges on the peptides with the polarizable surface of PGC. This results in efficient retention of very short and highly polar peptides using classical RP mobile phases. The effects of varying mobile phase composition (organic solvent and ion-pairing additives) as well as column temperature have been thoroughly investigated using short peptide standards. Under optimized conditions (water and acetonitrile/tetrahydrofuran 99:1 (v/v), both with 0.15% trifluoroacetic acid, as phase A and B, respectively, 0.5 mL min^-1 flowrate at 50 °C) the effect of post-column addition of 3-nitrobenzylic alcohol was also investigated allowing effective coupling of the chromatographic system with high resolution mass spectrometry. Finally, an untargeted approach for peptide identification was pursued, based on precursor identification in database with all possible combinations of the 20 natural amino acids and fragmentation spectra matching to in silico generated spectra. The method was then applied to investigation of the short endogenous peptides in human serum from healthy individuals resulting in the identification of 30 short peptides.

A systemic workflow for profiling metabolome and lipidome in tissue

Simple metabolome and lipidome sample preparation procedures involving two successive extractions using small pieces of tissue, and a subsequent metabolite identification (MetID) strategy were developed. The sample preparation can significantly circumvent incomplete analysis due to insufficient amounts of tissue as a result of splitting into several aliquots for multiple measurements, with advantages over the similar previously reported methods in metabolite coverage, extraction efficiency, method robustness and friendly experimental operation. A MetID strategy, based on the integration of MS information mining (including adduct ions, in-source CID, MS information from both ESI (+) and ESI (-), characteristic fragmentation ions (CFIs), constant neutral losses (CNLs) and multimers) and in silico MS simulation, was demonstrated. A large number of adduct ions (83 features), in-source CID (123 features), ESI (+/-) ionization (20 features), CFIs& CNLs (more than 120 features) and multimers (17 features) were mined by manually or in silico recognition/filtering, which provide the most suspicious structures for subsequent in silico MS simulation. The unknown features presented the same score distribution as the known (83 features) features with scores ≥25% (geomean score: 52%) and with satisfactory match for the main ions of interest. The MS/MS noise and fragment ions of coeluted quasi-molecular ions of interest are the main reason for the low score in the simulation. Manual check/evaluation is always suggested for the simulation with a score less than 50%. This strategy presents satisfactory performance with 2.5 times more metabolites structurally characterized compared with that of the traditional method based on accurate-mass-based MS and MS/MS library matching. This strategy would be useful for potentially identifying metabolites without available MS/MS information in the library.

Deep Annotation of Hydroxycinnamic Acid Amides in Plants Based on Ultra-High-Performance Liquid Chromatography-High-Resolution Mass Spectrometry and Its In Silico Database

Hydroxycinnamic acid amides (HCAAs), diversely distributed secondary metabolites in plants, play essential roles in plant growth and developmental processes. Most current approaches can be used to analyze a few known HCAAs in a given plant. A novel method for comprehensive detection of plant HCAAs is urgently needed. In this study, a deep annotation method of HCAAs was proposed on the basis of ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) and its in silico database of HCAAs. To construct an in silico UHPLC-HRMS HCAAs database, a total of 846 HCAAs were generated from the most common phenolic acid and polyamine/aromatic monoamine substrates according to possible biosynthesis reactions, which represent the structures of plant-specialized HCAAs. The characteristic MS/MS fragmentation patterns of HCAAs were extracted from reference mixtures. Four quantitative structure-retention relationship (QSRR) models were developed to predict retention times of mono-trans-HCAAs (aromatic amines conjugates), mono-trans-HCAAs (aliphatic amines conjugates), bis-HCAAs, and tris-HCAAs. The developed method was applied for identifying HCAAs in seeds (maize, wheat, and rice), roots (rice), and leaves (rice and tobacco). A total of 79 HCAAs were detected: 42 of them were identified in these plants for the first time, and 20 of them have never been reported to exist in plants. The results showed that the developed method can be used to identify HCAAs in a plant without prior knowledge of HCAA distributions. To the best of our knowledge, it is the first UHPLC-HRMS database developed for effective deep annotation of HCAAs from nontargeted UHPLC-HRMS data. It is useful for the identification of novel HCAAs in plants.

Contribution of Untargeted Metabolomics for Future Assessment of Biotech Crops

The nutritional value and safety of food crops are ultimately determined by their chemical composition. Recent developments in the field of metabolomics have made it possible to characterize the metabolic profile of crops in a comprehensive and high-throughput manner. Here, we propose that state-of-the-art untargeted metabolomics technology should be leveraged for safety assessment of new crop products. We suggest generally applicable experimental design principles that facilitate the efficient and rigorous identification of both intended and unintended metabolic alterations associated with a newly engineered trait. Our proposition could contribute to increased transparency of the safety assessment process for new biotech crops.

Tools and resources for metabolomics research community: A 2017-2018 update

The scale at which MS- and NMR-based platforms generate metabolomics datasets for both research, core, and clinical facilities to address challenges in the various sciences-ranging from biomedical to agricultural-is underappreciated. Thus, metabolomics efforts spanning microbe, environment, plant, animal, and human systems have led to continual and concomitant growth of in silico resources for analysis and interpretation of these datasets. These software tools, resources, and databases drive the field forward to help keep pace with the amount of data being generated and the sophisticated and diverse analytical platforms that are being used to generate these metabolomics datasets. To address challenges in data preprocessing, metabolite annotation, statistical interrogation, visualization, interpretation, and integration, the metabolomics and informatics research community comes up with hundreds of tools every year. The purpose of the present review is to provide a brief and useful summary of more than 95 metabolomics tools, software, and databases that were either developed or significantly improved during 2017-2018. We hope to see this review help readers, developers, and researchers to obtain informed access to these thorough lists of resources for further improvisation, implementation, and application in due course of time.

Analytical considerations for (un)-targeted metabolomic studies with special focus on forensic applications

Over the past few years, the interest in metabolomics has increased in various fields including forensic toxicology. Forensic analysis typically requires a high degree of accuracy, which is often a problem in metabolomics applications. We aimed for a systematic evaluation of different analytical considerations of a metabolomics workflow allowing a targeted approach within an untargeted setup. Samples with 69 metabolites from different chemical classes were qualitatively and quantitatively analyzed on a high resolution quadrupole time of flight mass spectrometer coupled to liquid chromatography (UHPLC-QTOF). Three issues were addressed: (a) Two different approaches on "blind matrix" a simulated body fluid (SBF) and plasma-filtrate, were tested for calibration samples; (b) comparison of two different HPLC columns, reverse-phase (RP) and hydrophilic interaction chromatography (HILIC); and (c) comparison of three different acquisition modes (TOF-MS, information dependent data acquisition (IDA), and sequential window acquisition of all theoretical fragment-ion spectra (SWATH). Samples were measured repeatedly for method comparison based on sensitivity, accuracy, precision, and detection robustness. The blind matrices showed similar accuracy for most analytes, while SBF provided an easier preparation with satisfying results. To cover a wide part of the human metabolome, a combination of RP and HILIC showed the best results. The different scan modes performed equally regarding metabolite quantification while TOF-MS was more sensitive but lacked MS/MS spectra generation. IDA and SWATH files were aligned to various databases where IDA showed good MS/MS spectra matches. SWATH seemed to be beneficial in detection rate but was incompatible with many important software tools in metabolomics.

Optimization of Electrospray Ionization Source Parameters for Lipidomics To Reduce Misannotation of In-Source Fragments as Precursor Ions

Lipidomics requires the accurate annotation of lipids in complex samples to enable determination of their biological relevance. We demonstrate that unintentional in-source fragmentation (ISF, common in lipidomics) generates ions that have identical masses to other lipids. Lysophosphatidylcholines (LPC), for example, generate in-source fragments with the same mass as free fatty acids and lysophosphatidylethanolamines (LPE). The misannotation of in-source fragments as true lipids is particularly insidious in complex matrixes since most masses are initially unannotated and comprehensive lipid standards are unavailable. Indeed, we show such LPE/LPC misannotations are incorporated in the data submitted to the National Institute of Standards and Technology (NIST) interlaboratory comparison exercise. Computer simulations exhaustively identified potential misannotations. The selection of in-source fragments of highly abundant lipids as features, instead of the correct recognition of trace lipids, can potentially lead to (i) missing the biologically relevant lipids (i.e., a false negative) and/or (ii) incorrect assignation of a phenotype to an incorrect lipid (i.e., false positive). When ISF is not eliminated in the negative ion mode, ∼40% of the 100 most abundant masses corresponding to unique phospholipids measured in plasma were artifacts from ISF. We show that chromatographic separation and ion intensity considerations assist in distinguishing precursor ions from in-source fragments, suggesting ISF may be especially problematic when complex samples are analyzed via shotgun lipidomics. We also conduct a systematic evaluation of electrospray ionization (ESI) source parameters on an Exactive equipped with a heated electrospray ionization (HESI-II) source with the objective of obtaining uniformly appropriate source conditions for a wide range of lipids, while, at the same time, reducing in-source fragmentation.

Review of recent developments in GC-MS approaches to metabolomics-based research

BACKGROUND

Metabolomics aims to identify the changes in endogenous metabolites of biological systems in response to intrinsic and extrinsic factors. This is accomplished through untargeted, semi-targeted and targeted based approaches. Untargeted and semi-targeted methods are typically applied in hypothesis-generating investigations (aimed at measuring as many metabolites as possible), while targeted approaches analyze a relatively smaller subset of biochemically important and relevant metabolites. Regardless of approach, it is well recognized amongst the metabolomics community that gas chromatography-mass spectrometry (GC-MS) is one of the most efficient, reproducible and well used analytical platforms for metabolomics research. This is due to the robust, reproducible and selective nature of the technique, as well as the large number of well-established libraries of both commercial and 'in house' metabolite databases available.

AIM OF REVIEW

This review provides an overview of developments in GC-MS based metabolomics applications, with a focus on sample preparation and preservation techniques. A number of chemical derivatization (in-time, in-liner, offline and microwave assisted) techniques are also discussed. Electron impact ionization and a summary of alternate mass analyzers are highlighted, along with a number of recently reported new GC columns suited for metabolomics. Lastly, multidimensional GC-MS and its application in environmental and biomedical research is presented, along with the importance of bioinformatics.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The purpose of this review is to both highlight and provide an update on GC-MS analytical techniques that are common in metabolomics studies. Specific emphasis is given to the key steps within the GC-MS workflow that those new to this field need to be aware of and the common pitfalls that should be looked out for when starting in this area.

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.

Myeloperoxidase oxidation of methionine associates with early cystic fibrosis lung disease

Cystic fibrosis (CF) lung disease progressively worsens from infancy to adulthood. Disease-driven changes in early CF airway fluid metabolites may identify therapeutic targets to curb progression.CF patients aged 12-38 months (n=24; three out of 24 later denoted as CF screen positive, inconclusive diagnosis) received chest computed tomography scans, scored by the Perth-Rotterdam Annotated Grid Morphometric Analysis for CF (PRAGMA-CF) method to quantify total lung disease (PRAGMA-%Dis) and components such as bronchiectasis (PRAGMA-%Bx). Small molecules in bronchoalveolar lavage fluid (BALF) were measured with high-resolution accurate-mass metabolomics. Myeloperoxidase (MPO) was quantified by ELISA and activity assays.Increased PRAGMA-%Dis was driven by bronchiectasis and correlated with airway neutrophils. PRAGMA-%Dis correlated with 104 metabolomic features (p<0.05, q<0.25). The most significant annotated feature was methionine sulfoxide (MetO), a product of methionine oxidation by MPO-derived oxidants. We confirmed the identity of MetO in BALF and used reference calibration to confirm correlation with PRAGMA-%Dis (Spearman's ρ=0.582, p=0.0029), extending to bronchiectasis (PRAGMA-%Bx; ρ=0.698, p=1.5×10^-4), airway neutrophils (ρ=0.569, p=0.0046) and BALF MPO (ρ=0.803, p=3.9×10^-6).BALF MetO associates with structural lung damage, airway neutrophils and MPO in early CF. Further studies are needed to establish whether methionine oxidation directly contributes to early CF lung disease and explore potential therapeutic targets indicated by these findings.

Nematodes avoid and are killed by Bacillus mycoides-produced styrene

Root-knot nematodes are obligate parasites that feed on plant roots and cause serious crop losses worldwide. Bacillus species (Bacilliaceae) can produce nematicidal metabolites and have shown good potential for biological control of nematodes. In this study, Bacillus mycoides strain R2 isolated from rhizosphere soil of tomato plants exhibited high nematicidal activity against the free-living nematode Caenorhabditis elegans and the root-knot nematode Meloidogyne incognita. In a pot experiment, control efficiency of B. mycoides R2 on M. incognita was as high as 90.94%. The nematicidal compound was isolated and identified as styrene. The median lethal concentration of styrene against M. incognita was 4.55 μg/ml (m/v). The volatile styrene caused avoidance and killed nematodes primarily by the olfactory neuron and G protein signal pathway. C. elegans detected styrene with the AWB neuron; the signal was then transmitted to the downstream G protein coupled receptors CHE-3, DOP-3, and STR-2. Then signal activated G protein GPA-3 and GPA-7. The signal was then transmitted to ion channels (CNGs channel and TRPV channel), causing calcium ion internal flow and a stress response towards the increased concentration of intracellular calcium. Styrene should be registered as a nematode repellent and biocontrol agent for protection of crops against root-knot nematode attack.

Serum Metabolomics Analysis of Asthma in Different Inflammatory Phenotypes: A Cross-Sectional Study in Northeast China

BACKGROUND AND OBJECTIVE

Asthma as a chronic heterogeneous disease seriously affects the quality of life. Incorrect identification for its clinical phenotypes lead to a huge waste of medical resources. Metabolomic technique as a novel approach to explore the pathogenesis of diseases have not been used to study asthma based on their clear defined inflammatory phenotypes. This study is aimed to distinguish the divergent metabolic profile in different asthma phenotypes and clarify the pathogenesis of them.

METHODS

Participants including eosinophilic asthmatics (EA, n=13), noneosinophilic asthmatics (NEA, n=16), and healthy controls (HC, n=15) were enrolled. A global profile of untargeted serum metabolomics was identified with Ultra Performance Liquid Chromatography-Mass Spectrometry technique.

RESULTS

Multivariate analysis was performed and showed a clear distinction between EA, NEA, and HC. A total of 18 different metabolites were recognized between the three groups based on OPLS-DA model and involved in 10 perturbed metabolic pathways. Glycerophospholipid metabolism, retinol metabolism, and sphingolipid metabolism were identified as the most significant changed three pathways (impact > 0.1 and -log(P) > 4) between the phenotypes.

CONCLUSIONS

We showed that the different inflammatory phenotypes of asthma involve the immune regulation, energy, and nutrients metabolism. The clarified metabolic profile contributes to understanding the pathophysiology of asthma phenotypes and optimizing the therapeutic strategy against asthma heterogeneity.

Inhibitory Effect of Methotrexate on Rheumatoid Arthritis Inflammation and Comprehensive Metabolomics Analysis Using Ultra-Performance Liquid Chromatography-Quadrupole Time of Flight-Mass Spectrometry (UPLC-Q/TOF-MS)

Rheumatoid arthritis (RA) is a common autoimmune disease. The inflammation in joint tissue and system endanger the human health seriously. Methotrexate have exhibited a satisfactory therapeutic effect in clinical practice. The aim of this research was to establish the pharmacological mechanism of methotrexate on RA therapy. Collagen induced arthritic rats were used to identify how methotrexate alleviates inflammation in vivo. Lipopolysaccharide-induced inflammatory proliferation in macrophages was also be detected in vitro. The activation level of Nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) and Nucleotide binding domain and leucine-rich repeat pyrin 3 domain (NLRP3)/Caspase-1 and related cytokines were examined by real-time PCR and western blotting or quantified with the enzyme-linked immunosorbent assay. Comprehensive metabolomics analysis was performed to identify the alteration of metabolites. Results showed that treating with methotrexate could alleviate the inflammatory condition, downregulate the activation of NF-κB and NLRP3/Caspase-1 inflammatory pathways and reduce the level of related cytokines. Docking interaction between methotrexate and caspase-1 was visualized as six H-bonds indicating a potential inhibitory effect. Metabolomics analysis reported three perturbed metabolic inflammation related pathways including arachidonic acid, linoleic acid and sphingolipid metabolism. These findings indicated that methotrexate could inhibit the onset of inflammation in joint tissue by suppressing the activation of NF-κB and NLRP3/Caspase-1 pathways and regulating the inflammation related metabolic networks.

Light-Dependent Changes in the Spatial Localization of Metabolites in Solenostemon scutellarioides (Coleus Henna) Visualized by Matrix-Free Atmospheric Pressure Electrospray Laser Desorption Ionization Mass Spectrometry Imaging

The visualization of foliage color in plants provides immediate insight into some of the compounds that exist in the leaf. However, many non-colored compounds are also present; their cellular distributions are not readily identifiable optically. In this study we evaluate the applicability of mass spectrometry imaging (MSI) via electrospray laser desorption ionization (ELDI) to reveal the spatial distribution of metabolites. ELDI-MSI is a matrix free, atmospheric pressure ionization method that utilizes a UV laser coupled with supplemental ionization by electrospray. We specifically applied ELDI-MSI to determine the spatial distribution of metabolites in Coleus Henna half leaves that were grown with half-sections either fully illuminated or shaded. We monitored dynamic changes in the spatial distribution of metabolites in response to the change of illumination every 7 days for a 28 day period. A novel source-sink relationship was observed between the 2 halves of the experimental leaf. Furthermore, Coleus Henna leaves present visually recognizable sectors associated with the differential accumulation of flavonoids. Thus, we correlated the effect of differential illumination and presence or absence of flavonoids with metabolic changes revealed by the accumulation of carbohydrates, amino acids, and organic acids. The results show the potential of ELDI-MSI to provide spatial information for a variety of plant metabolites with little sample preparation.

Nonhuman Primates with Acute Radiation Syndrome: Results from a Global Serum Metabolomics Study after 7.2 Gy Total-Body Irradiation

Threats of nuclear terrorism coupled with potential unintentional ionizing radiation exposures have necessitated the need for large-scale response efforts of such events, including high-throughput biodosimetry for medical triage. Global metabolomics utilizing mass spectrometry (MS) platforms has proven an ideal tool for generating large compound databases with relative quantification and structural information in a short amount of time. Determining metabolite panels for biodosimetry requires experimentation to evaluate the many factors associated with compound concentrations in biofluids after radiation exposures, including temporal changes, pre-existing conditions, dietary intake, partial- vs. total-body irradiation (TBI), among others. Here, we utilize a nonhuman primate (NHP) model and identify metabolites perturbed in serum after 7.2 Gy TBI without supportive care [LD_70/60, hematologic (hematopoietic) acute radiation syndrome (HARS) level H3] at 24, 36, 48 and 96 h compared to preirradiation samples with an ultra-performance liquid chromatography quadrupole time-of-flight (UPLC-QTOF) MS platform. Additionally, we document changes in cytokine levels. Temporal changes observed in serum carnitine, acylcarnitines, amino acids, lipids, deaminated purines and increases in pro-inflammatory cytokines indicate clear metabolic dysfunction after radiation exposure. Multivariate data analysis shows distinct separation from preirradiation groups and receiver operator characteristic curve analysis indicates high specificity and sensitivity based on area under the curve at all time points after 7.2 Gy irradiation. Finally, a comparison to a 6.5 Gy (LD_50/60, HARS level H2) cohort after 24 h postirradiation revealed distinctly increased separations from the 7.2 Gy cohort based on multivariate data models and higher compound fold changes. These results highlight the utility of MS platforms to differentiate time and absorbed dose after a potential radiation exposure that may aid in assigning specific medical interventions and contribute as additional biodosimetry tools.

XCMS-MRM and METLIN-MRM: a cloud library and public resource for targeted analysis of small molecules

We report XCMS-MRM and METLIN-MRM ( http://xcmsonline-mrm.scripps.edu/ and http://metlin.scripps.edu/ ), a cloud-based data-analysis platform and a public multiple-reaction monitoring (MRM) transition repository for small-molecule quantitative tandem mass spectrometry. This platform provides MRM transitions for more than 15,500 molecules and facilitates data sharing across different instruments and laboratories.

Rabbit plasma metabolomic analysis of Nitroproston®: a multi target natural prostaglandin based-drug

INTRODUCTION

Nitroproston® is a novel multi-target drug bearing natural prostaglandin E₂ (PGE₂) and nitric oxide (NO)-donating fragments for treatment of inflammatory and obstructive diseases (i.e., asthma and obstructive bronchitis).

OBJECTIVES

To investigate the effects of Nitroproston® administration on plasma metabolomics in vivo.

METHODS

Experimental in vivo study randomly assigning the target drug (treatment group) or a saline solution without the drug (vehicle control group) to 12 rabbits (n = 6 in each group). Untargeted (5880 initial features; 1869 negative-4011 positive ion peaks; UPLC-IT-TOF/MS) and 84 targeted moieties (Nitroproston® related metabolites, prostaglandins, steroids, purines, pyrimidines and amino acids; HPLC-QQQ-MS/MS) were measured from plasma at 0, 2, 4, 6, 8, 12, 18, 24, 32 and 60 min after administration.

RESULTS

PGE₂, 13,14-dihydro-15-keto-PGE₂, PGB₂, 1,3-GDN and 15-keto-PGE₂ increased in the treatment group. Steroids (i.e., cortisone, progesterone), organic acids, 3-oxododecanoic acid, nicotinate D-ribonucleoside, thymidine, the amino acids serine and aspartate, and derivatives pyridinoline, aminoadipic acid and uric acid increased (p < 0.05 AUCROC curve > 0.75) after treatment. Purines (i.e., xanthine, guanine, guanosine), bile acids, acylcarnitines and the amino acids L-tryptophan and L-phenylalanine were decreased. Nitroproston® impacted steroidogenesis, purine metabolism and ammonia recycling pathways, among others.

CONCLUSION

Nitroproston®, a multi action novel drug based on natural prostaglandins, altered metabolites (i.e., guanine, adenine, cortisol, cortisone and aspartate) involved in purine metabolism, urea and ammonia biological cycles, steroidogenesis, among other pathways. Suggested mechanisms of action, metabolic pathway interconnections and useful information to further understand the metabolic effects of prostaglandin administration are presented.

The role of lipids in the inception, maintenance and complications of dengue virus infection

Dengue fever is a viral condition that has become a recurrent issue for public health in tropical countries, common endemic areas. Although viral structure and composition have been widely studied, the infection phenotype in terms of small molecules remains poorly established. This contribution providing a comprehensive overview of the metabolic implications of the virus-host interaction using a lipidomic-based approach through direct-infusion high-resolution mass spectrometry. Our results provide further evidence that lipids are part of both the immune response upon Dengue virus infection and viral infection maintenance mechanism in the organism. Furthermore, the species described herein provide evidence that such lipids may be part of the mechanism that leads to blood-related complications such as hemorrhagic fever, the severe form of the disease.

Human Metabolome Changes after a Single Dose of 3,4-Methylenedioxymethamphetamine (MDMA) with Special Focus on Steroid Metabolism and Inflammation Processes

The intake of 3,4-methylenedioxymethamphetamine (MDMA) is known to increase several endogenous substances involved in steroid and inflammation pathways. Untargeted metabolomics screening approaches can determine biochemical changes after drug exposure and can reveal new pathways, which might be involved in the pharmacology and toxicology of a drug of abuse. We analyzed plasma samples from a placebo-controlled crossover study of a single intake of MDMA. Plasma samples from a time point before and three time points after the intake of a single dose of 125 mg MDMA were screened for changes of endogenous metabolites. An untargeted metabolomics approach on a high-resolution quadrupole time-of-flight mass spectrometer coupled to liquid chromatography with two different chromatographic systems (reversed-phase and hydrophobic interaction liquid chromatography) was applied. Over 10 000 features of the human metabolome were detected. Hence, 28 metabolites were identified, which showed significant changes after administration of MDMA compared with placebo. The analysis revealed an upregulation of cortisol and pregnenolone sulfate 4 h after MDMA intake, suggesting increased stress and serotonergic activity. Furthermore, calcitriol levels were decreased after the intake of MDMA. Calcitriol is involved in the upregulation of trophic factors, which have protective effects on brain dopamine neurons. The inflammation mediators hydroxyeicosatetraenoic acid, dihydroxyeicosatetraenoic acid, and octadecadienoic acid were found to be upregulated after the intake of MDMA compared with placebo, which suggested a stimulation of inflammation pathways.

Improving metabolome coverage and data quality: advancing metabolomics and lipidomics for biomarker discovery

This Feature Article highlights some of the key challenges within the field of metabolomics and examines what role separation and analytical sciences can play to improve the use of metabolomics in biomarker discovery and personalized medicine. Recent progress in four key areas is highlighted: (i) improving metabolite coverage, (ii) developing accurate methods for unstable metabolites including in vivo global metabolomics methods, (iii) advancing inter-laboratory studies and reference materials and (iv) improving data quality, standardization and quality control of metabolomics studies.

Autonomous Multimodal Metabolomics Data Integration for Comprehensive Pathway Analysis and Systems Biology

Comprehensive metabolomic data can be achieved using multiple orthogonal separation and mass spectrometry (MS) analytical techniques. However, drawing biologically relevant conclusions from this data and combining it with additional layers of information collected by other omic technologies present a significant bioinformatic challenge. To address this, a data processing approach was designed to automate the comprehensive prediction of dysregulated metabolic pathways/networks from multiple data sources. The platform autonomously integrates multiple MS-based metabolomics data types without constraints due to different sample preparation/extraction, chromatographic separation, or MS detection method. This multimodal analysis streamlines the extraction of biological information from the metabolomics data as well as the contextualization within proteomics and transcriptomics data sets. As a proof of concept, this multimodal analysis approach was applied to a colorectal cancer (CRC) study, in which complementary liquid chromatography-mass spectrometry (LC-MS) data were combined with proteomic and transcriptomic data. Our approach provided a highly resolved overview of colon cancer metabolic dysregulation, with an average 17% increase of detected dysregulated metabolites per pathway and an increase in metabolic pathway prediction confidence. Moreover, 95% of the altered metabolic pathways matched with the dysregulated genes and proteins, providing additional validation at a systems level. The analysis platform is currently available via the XCMS Online ( XCMSOnline.scripps.edu ).

Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics

The annotation of small molecules remains a major challenge in untargeted mass spectrometry-based metabolomics. We here critically discuss structured elucidation approaches and software that are designed to help during the annotation of unknown compounds. Only by elucidating unknown metabolites first is it possible to biologically interpret complex systems, to map compounds to pathways and to create reliable predictive metabolic models for translational and clinical research. These strategies include the construction and quality of tandem mass spectral databases such as the coalition of MassBank repositories and investigations of MS/MS matching confidence. We present in silico fragmentation tools such as MS-FINDER, CFM-ID, MetFrag, ChemDistiller and CSI:FingerID that can annotate compounds from existing structure databases and that have been used in the CASMI (critical assessment of small molecule identification) contests. Furthermore, the use of retention time models from liquid chromatography and the utility of collision cross-section modelling from ion mobility experiments are covered. Workflows and published examples of successfully annotated unknown compounds are included.

Recent Advances in Targeted and Untargeted Metabolomics by NMR and MS/NMR Methods

Metabolomics has made significant progress in multiple fronts in the last 18 months. This minireview aimed to give an overview of these advancements in the light of their contribution to targeted and untargeted metabolomics. New computational approaches have emerged to overcome the manual absolute quantitation step of metabolites in one-dimensional (1D) ¹H nuclear magnetic resonance (NMR) spectra. This provides more consistency between inter-laboratory comparisons. Integration of two-dimensional (2D) NMR metabolomics databases under a unified web server allowed for very accurate identification of the metabolites that have been catalogued in these databases. For the remaining uncatalogued and unknown metabolites, new cheminformatics approaches have been developed by combining NMR and mass spectrometry (MS). These hybrid MS/NMR approaches accelerated the identification of unknowns in untargeted studies, and now they are allowing for profiling ever larger number of metabolites in application studies.

Metabolic Noise and Distinct Subpopulations Observed by Single Cell LAESI Mass Spectrometry of Plant Cells in situ

Phenotypic variations and stochastic expression of transcripts, proteins, and metabolites in biological tissues lead to cellular heterogeneity. As a result, distinct cellular subpopulations emerge. They are characterized by different metabolite expression levels and by associated metabolic noise distributions. To capture these biological variations unperturbed, highly sensitive in situ analytical techniques are needed that can sample tissue embedded single cells with minimum sample preparation. Optical fiber-based laser ablation electrospray ionization mass spectrometry (f-LAESI-MS) is a promising tool for metabolic profiling of single cells under ambient conditions. Integration of this MS-based platform with fluorescence and brightfield microscopy provides the ability to target single cells of specific type and allows for the selection of rare cells, e.g., excretory idioblasts. Analysis of individual Egeria densa leaf blade cells (n = 103) by f-LAESI-MS revealed significant differences between the prespecified subpopulations of epidermal cells (n = 97) and excretory idioblasts (n = 6) that otherwise would have been masked by the population average. Primary metabolites, e.g., malate, aspartate, and ascorbate, as well as several glucosides were detected in higher abundance in the epidermal cells. The idioblasts contained lipids, e.g., PG(16:0/18:2), and triterpene saponins, e.g., medicoside I and azukisaponin I, and their isomers. Metabolic noise for the epidermal cells were compared to results for soybean (Glycine max) root nodule cells (n = 60) infected by rhizobia (Bradyrhizobium japonicum). Whereas some primary metabolites showed lower noise in the latter, both cell types exhibited higher noise for secondary metabolites. Post hoc grouping of epidermal and root nodule cells, based on the abundance distributions for certain metabolites (e.g., malate), enabled the discovery of cellular subpopulations characterized by different mean abundance values, and the magnitudes of the corresponding metabolic noise. Comparison of prespecified populations from epidermal cells of the closely related E. densa (n = 20) and Elodea canadensis (n = 20) revealed significant differences, e.g., higher sugar content in the former and higher levels of ascorbate in the latter, and the presence of species-specific metabolites. These results demonstrate that the f-LAESI-MS single cell analysis platform has the potential to explore cellular heterogeneity and metabolic noise for hundreds of tissue-embedded cells.

Metabolomic Fingerprints of Individual Algal Cells Using the Single-Probe Mass Spectrometry Technique

Traditional approaches for the assessment of physiological responses of microbes in the environment rely on bulk filtration techniques that obscure differences among populations as well as among individual cells. Here, were report on the development on a novel micro-scale sampling device, referred to as the "Single-probe," which allows direct extraction of metabolites from living, individual phytoplankton cells for mass spectrometry (MS) analysis. The Single-probe is composed of dual-bore quartz tubing which is pulled using a laser pipette puller and fused to a silica capillary and a nano-ESI. For this study, we applied Single-probe MS technology to the marine dinoflagellate Scrippsiella trochoidea, assaying cells grown under different illumination levels and under nitrogen (N) limiting conditions as a proof of concept for the technology. In both experiments, significant differences in the cellular metabolome of individual cells could readily be identified, though the vast majority of detected metabolites could not be assigned to KEGG pathways. Using the same approach, significant changes in cellular lipid complements were observed, with individual lipids being both up- and down-regulated under light vs. dark conditions. Conversely, lipid content increased across the board under N limitation, consistent with an adjustment of Redfield stoichiometry to reflect higher C:N and C:P ratios. Overall, these data suggest that the Single-probe MS technique has the potential to allow for near in situ metabolomic analysis of individual phytoplankton cells, opening the door to targeted analyses that minimize cell manipulation and sampling artifacts, while preserving metabolic variability at the cellular level.