Toward Merging Untargeted and Targeted Methods in Mass Spectrometry-Based Metabolomics and Lipidomics

Quantitative analysis of chemoresistance-inducing fatty acid in food supplements using UHPLC-ESI-MS/MS

Polyunsaturated fatty acids are important signaling molecules. A recent study reported hexadeca-4Z,7Z,10Z,13Z-tetraenoic acid, 12-oxo-5Z,8E,10E-heptadecatrienoic acid, and (12S)-hydroxy-5Z,8E,10E-heptadecatrienoic acid as chemotherapy resistance-inducing factors when tumor cells were treated with cisplatin. Marine-based food supplements like fish oil or algae extracts are rich in polyunsaturated fatty acids and can contain large amounts of hexadeca-4Z,7Z,10Z,13Z-tetraenoic acid. Thus, it was concluded that oral uptake of hexadeca-4Z,7Z,10Z,13Z-tetraenoic acid might induce chemoresistance as shown in a mouse model. Cancer patients tend to consume food supplements containing polyunsaturated fatty acids on a regular basis. The uptake of hexadeca-4Z,7Z,10Z,13Z-tetraenoic acid and (12S)-hydroxy-5Z,8E,10E-heptadecatrienoic acid should be controlled, because even low concentrations of 0.5 ng mL^-1 showed chemoresistance-inducing effects in animal experiments. For accurate analysis of hexadeca-4Z,7Z,10Z,13Z-tetraenoic acid and (12S)-hydroxy-5Z,8E,10E-heptadecatrienoic acid a validated method was developed by using ultrahigh-performance liquid chromatography hyphenated to quadrupole time of flight mass spectrometry via electrospray ionization and sample preparation by solid-phase extraction (SPE) with 3-aminopropyl silica. A combined targeted/untargeted approach was utilized using MS/MS by data-independent acquisition with SWATH and applied to commercial food supplements (refined fish oil, fish oil capsules, algae oil capsules, and flaxseed capsules). Accurate quantification of hexadeca-4Z,7Z,10Z,13Z-tetraenoic acid and (12S)-hydroxy-5Z,8E,10E-heptadecatrienoic acid on the MS/MS level with simultaneous untargeted fatty acid screening revealed additional information. The LODs for hexadeca-4Z,7Z,10Z,13Z-tetraenoic acid and (12S)-hydroxy-5Z,8E,10E-heptadecatrienoic acid were 0.036 ng mL^-1 and 0.054 ng mL^-1, respectively. Since hexadeca-4Z,7Z,10Z,13Z-tetraenoic acid was present in the samples in large amounts and (12S)-hydroxy-5Z,8E,10E-heptadecatrienoic was not expected to be present in high concentrations, two calibration ranges, namely, 0.5-20 ng mL^-1 and 5-200 ng mL^-1, were validated. An untargeted screening identified 18-39 free fatty acids being present in the lipid extracts of the food supplement samples. Graphical abstract ᅟ.

High-Throughput Plasma Lipidomics: Detailed Mapping of the Associations with Cardiometabolic Risk Factors

High-throughput targeted lipid profiling with liquid chromatography-mass spectrometry (LC-MS) has been used extensively to identify associations between plasma lipid species and disease states. Such methods, used to characterize larger clinical cohorts, often suffer from an inability to differentiate isomeric forms of glycerophospholipids that are typically reported as the sum fatty acid carbons and double bonds. Here we report a chromatography gradient coupled with a detailed characterization of the human plasma lipidome to provide improved resolution and identification of 636 lipid species, including previously unreported species, in a 15-min analysis. We have utilized this method on a subset of the Australian Diabetes, Obesity, and Lifestyle Study and have detailed associations of plasma lipid species with anthropometric and blood glucose measures. These results highlight the importance and power of high-throughput lipidomics coupled with a detailed characterization of the lipidome to better understand lipid biology in a population setting.

Non-targeted metabolomics reveals alterations in liver and plasma of gilt-head bream exposed to oxybenzone

The extensive use of the organic UV filter oxybenzone has led to its ubiquitous occurrence in the aquatic environment, causing an ecotoxicological risk to biota. Although some studies reported adverse effects, such as reproductive toxicity, further research needs to be done in order to assess its molecular effects and mechanism of action. Therefore, in the present work, we investigated metabolic perturbations in juvenile gilt-head bream (Sparus aurata) exposed over 14 days via the water to oxybenzone (50 mg/L). The non-targeted analysis of brain, liver and plasma extracts was performed by means of UHPLC-qOrbitrap MS in positive and negative modes with both C18 and HILIC separation. Although there was no mortality or alterations in general physiological parameters during the experiment, and the metabolic profile of brain was not affected, the results of this study showed that oxybenzone could perturb both liver and plasma metabolome. The pathway enrichment suggested that different pathways in lipid metabolism (fatty acid elongation, α-linolenic acid metabolism, biosynthesis of unsaturated fatty acids and fatty acid metabolism) were significantly altered, as well as metabolites involved in phenylalanine and tyrosine metabolism. Overall, these changes are signs of possible oxidative stress and energy metabolism modification. Therefore, this research indicates that oxybenzone has adverse effects beyond the commonly studied hormonal activity, and demonstrates the sensitivity of metabolomics to assess molecular-level effects of emerging contaminants.

A lipidomic approach to understand copper resilience in oyster Crassostrea hongkongensis

Copper (Cu) can cause oxidative stress and inflammatory responses, and there is arising evidence between Cu toxicity and lipid disturbance. In this study, we examined the relationships between Cu exposure and lipid metabolism in an estuarine oyster (Crassostrea hongkongensis) and aimed to understand the effects and resilience strategies of Cu on oyster metabolism. We exposed the oysters to waterborne Cu (10 and 50 μg/L) and measured the physiological changes (condition index and clearance rate), lipid accumulation and lipid peroxidation in the oysters. We found more altered lipid responses in oysters exposed to a lower Cu concentration (10 μg/L), and speculated that oysters exposed to 50 μg/L may upregulate the defenses. We further evaluated the changes in lipidome profiling of the Cu-exposed oysters in aspects of membrane dynamics, lipid signaling and energy metabolism. We documented the phospholipid remodeling as well as quick modulation in inflammatory responses and extensive vesicle formation for subcellular compartmentalization and autophagosome formation, as well as the possible impacts on mitochondrial bioenergetics in the Cu-exposed oysters. The lipidomics approach provided a comprehensive lipid profile of possible alteration by Cu exposure. In combination with other omics approaches, it may be possible to elucidate the pathways and mechanisms in stress acclimation and resilience associated between Cu contamination and lipid metabolism.

Diabetes-associated alterations in the cecal microbiome and metabolome are independent of diet or environment in the UC Davis Type 2 Diabetes Mellitus Rat model

The composition of the gut microbiome is altered in obesity and type 2 diabetes; however, it is not known whether these alterations are mediated by dietary factors or related to declines in metabolic health. To address this, cecal contents were collected from age-matched, chow-fed male University of California, Davis Type 2 Diabetes Mellitus (UCD-T2DM) rats before the onset of diabetes (prediabetic PD; n = 15), 2 wk recently diabetic (RD; n = 10), 3 mo (D3M; n = 11), and 6 mo (D6M; n = 8) postonset of diabetes. Bacterial species and functional gene counts were assessed by shotgun metagenomic sequencing of bacterial DNA in cecal contents, while metabolites were identified by gas chromatography-quadrupole time-off-flight-mass spectrometry. Metagenomic analysis showed a shift from Firmicutes species in early stages of diabetes (PD + RD) toward an enrichment of Bacteroidetes species in later stages of diabetes (D3M + D6M). In total, 45 bacterial species discriminated early and late stages of diabetes with 25 of these belonging to either Bacteroides or Prevotella genera. Furthermore, 61 bacterial gene clusters discriminated early and later stages of diabetes with elevations of enzymes related to stress response (e.g., glutathione and glutaredoxin) and amino acid, carbohydrate, and bacterial cell wall metabolism. Twenty-five cecal metabolites discriminated early vs. late stages of diabetes, with the largest differences observed in abundances of dehydroabietic acid and phosphate. Alterations in the gut microbiota and cecal metabolome track diabetes progression in UCD-T2DM rats when controlling for diet, age, and housing environment. Results suggest that diabetes-specific host signals impact the ecology and end product metabolites of the gut microbiome when diet is held constant.

A normal phase high performance liquid chromatography method for the separation of hydroxy and non-hydroxy neutral lipid classes compatible with ultraviolet and in-line liquid scintillation detection of radioisotopes

In this paper, we report a method for the separation of hydroxy fatty acid and non-hydroxy fatty acid containing neutral lipid classes via normal phase HPLC with UV detection on a PVA-Sil column. The hexane/isopropanol/methanol/water based method separates all the neutral lipids in 21 min, and subsequently flushes through the polar lipids by 27 min such that prefractionation of neutral and polar lipids are not required, and the column is re-equilibrated for the next run in 15 min, for a total run time of 45 min per sample. The separation was demonstrated at both 1.0 mL/min and 1.5 mL/min for added applicability for fraction collection or inline analysis. Separation of various hydroxy fatty acid containing lipids was demonstrated from three different plant species Ricinus communis, Physaria fendleri, and engineered Arabidopsis thaliana. Additionally, we have combined this method with an in-line liquid scintillation counter for the separation and quantification of ¹⁴C labeled lipids obtained from in vivo metabolic flux experiments conducted in the developing seeds of Arabidopsis thaliana.

Distinctive Patterns of Flavonoid Biosynthesis in Roots and Nodules of Datisca glomerata and Medicago spp. Revealed by Metabolomic and Gene Expression Profiles

Plants within the Nitrogen-fixing Clade (NFC) of Angiosperms form root nodule symbioses with nitrogen-fixing bacteria. Actinorhizal plants (in Cucurbitales, Fagales, Rosales) form symbioses with the actinobacteria Frankia while legumes (Fabales) form symbioses with proteobacterial rhizobia. Flavonoids, secondary metabolites of the phenylpropanoid pathway, have been shown to play major roles in legume root nodule symbioses: as signal molecules that in turn trigger rhizobial nodulation initiation signals and acting as polar auxin transport inhibitors, enabling a key step in nodule organogenesis. To explore a potentially broader role for flavonoids in root nodule symbioses across the NFC, we combined metabolomic and transcriptomic analyses of roots and nodules of the actinorhizal host Datisca glomerata and legumes of the genus Medicago. Patterns of biosynthetic pathways were inferred from flavonoid metabolite profiles and phenylpropanoid gene expression patterns in the two hosts to identify similarities and differences. Similar classes of flavonoids were represented in both hosts, and an increase in flavonoids generally in the nodules was observed, with differences in flavonoids prominent in each host. While both hosts produced derivatives of naringenin, the metabolite profile in D. glomerata indicated an emphasis on the pinocembrin biosynthetic pathway, and an abundance of flavonols with potential roles in symbiosis. Additionally, the gene expression profile indicated a decrease in expression in the lignin/monolignol pathway. In Medicago sativa, by contrast, isoflavonoids were highly abundant featuring more diverse and derived isoflavonoids than D. glomerata. Gene expression patterns supported these differences in metabolic pathways, especially evident in a difference in expression of cinnamic acid 4-hydroxylase (C4H), which was expressed at substantially lower levels in D. glomerata than in a Medicago truncatula transcriptome where it was highly expressed. C4H is a major rate-limiting step in phenylpropanoid biosynthesis that separates the pinocembrin pathway from the lignin/monolignol and naringenin-based flavonoid branches. Shikimate O-hydroxycinnamoyltransferase, the link between flavonoid biosynthesis and the lignin/monolignol pathway, was also expressed at much lower levels in D. glomerata than in M. truncatula. Our results indicate (a) a likely major role for flavonoids in actinorhizal nodules, and (b) differences in metabolic flux in flavonoid and phenylpropanoid biosynthesis between the different hosts in symbiosis.

Strategies for large-scale targeted metabolomics quantification by liquid chromatography-mass spectrometry

Advances in liquid chromatography-mass spectrometry (LC-MS) instruments and analytical strategies have brought about great progress in targeted metabolomics analysis. This methodology is now capable of performing precise targeted measurement of dozens or hundreds of metabolites in complex biological samples. Classic targeted quantification assay using the multiple reaction monitoring (MRM) mode has been the foundation of high-quality metabolite quantitation. However, utilization of this strategy in biological studies has been limited by its relatively low metabolite coverage and throughput capacity. A number of methods for large-scale targeted metabolomics assay which have been developed overcome these limitations. These strategies have enabled extended metabolite coverage which is defined as targeting of large numbers of metabolites, while maintaining reliable quantification performance. These recently developed techniques thus bridge the gap between traditional targeted metabolite quantification and untargeted metabolomics profiling, and have proven to be powerful tools for metabolomics study. Although the LC-MRM-MS strategy has been used widely in large-scale metabolomics quantification analysis due to its fast scan speed and ideal analytic stability, there are still drawbacks which are due to the low resolution of the triple quadrupole instruments used for MRM assays. New approaches have been developed to expand the options for large-scale targeted metabolomics study, using high-resolution instruments such as parallel reaction monitoring (PRM). MRM and PRM-based techniques are now attractive strategies for quantitative metabolomics analysis and high-throughput biomarker discovery. Here we provide an overview of the major developments in LC-MS-based strategies for large-scale targeted metabolomics quantification in biological samples. The advantages of LC-MRM/PRM-MS based analytical strategies which may be used in multiplexed and high throughput quantitation for a wide range of metabolites are highlighted. In particular, PRM and MRM strategies are compared, and we summarize the work flow commonly used for large-scale targeted metabolomics analysis including sample preparation, LC separation and data analysis, as well as recent applications in biological studies.

Isotope tracing assisted metabolic profiling: Application to understanding HSP60 silencing mediated tumor progression

Isotope-tracing facilitates the understanding of metabolic regulation in biological systems. Depending on the selection of tracers, some essential metabolites cannot be traced. A comprehensive understanding of the regulated pathways can only be achieved with focus beyond labeled metabolites. The isotope tracing assisted metabolic profiling described here is a platform for high throughput mapping of isotope labeled metabolites with simultaneous metabolomics profiling. This approach incorporates an in-house MS/MS library for metabolite identification and ID-based quantitation. An "Isotopic" software was developed to generate potential labeled isotopomers. Using this platform, a total of 394 metabolites were reliably identified based on MS/MS confirmation in 3 million 293 T cells, among which 54 and 43 metabolites were discovered to carry extensive labels (>2%) from ¹³C₆-glucose and ¹³C₅-glutamine respectively. Citrate flowing into malate shuttle was also observed. More interestingly, the rate-limiting step in NAD and UDP-GlcNAc biosynthesis was clearly observed according to time course labeling. In HSP60 knockdown cell lines, enhanced purine and pyrimidine biosynthesis were confirmed by the abundance and labeling percentages of intermediate metabolites.

Comprehensive MS/MS profiling by UHPLC-ESI-QTOF-MS/MS using SWATH data-independent acquisition for the study of platelet lipidomes in coronary artery disease

A non-targeted lipidomics workflow based on C8 core-shell particle ultra high-performance liquid chromatography (UHPLC) hyphenated to ESI-QTOF-MS in data-independent acquisition (DIA) mode with sequential window acquisition of all theoretical fragment ion spectra (SWATH) was developed and applied to differential platelet lipidomics profiling of cardiovascular disease patients (stable angina pectoris (n = 10), ST-elevated myocardial infarction (n = 13)) against healthy controls (n = 10). DIA with SWATH generates comprehensive MS and MS/MS data throughout the entire chromatograms and all study samples. Hence, chromatograms can be extracted based on precursors or fragments which provided some benefits in terms of assay specificity in some cases. SWATH acquisition offers flexible experimental design with variable Q1 isolation windows. Liquid chromatography as well as SWATH settings were optimized to cover the lipidome of human platelets. The flexibility of the SWATH experiment design was utilized to implement target SWATH windows with narrow 5 Da Q1 precursor ion selection width (multiple reaction monitoring (MRM)-like SWATH windows) for the detection of low abundant oxidized phospholipids. Data processing was performed with MS-DIAL, and its feasibilities and caveats are discussed by illustrative examples. Thereby, identification of lipids is still a bottleneck in non-targeted lipidomics workflow. MS-DIAL, however, offers automatic identification via spectral matching using an in silico library. In total 1971 molecular features were detected cross the samples of which 611 were identified (total score >70%). The quality of the acquired data was validated with embedded quality control samples (n = 11). 80.3% of all features detected in the QC samples showed a coefficient of variation of below 30%. Multivariate statistics were used to visualize differences in the lipidome of distinct sample groups at a false discovery rate of 5%.

Gut microbial and metabolomic profiles after fecal microbiota transplantation in pediatric ulcerative colitis patients

Ulcerative colitis is a chronic inflammatory disease of the colon that carries a significant disease burden in children. Therefore, new therapeutic approaches are being explored to help children living with this disease. Fecal microbiota transplantation (FMT) has been successful in some children with ulcerative colitis. However, the mechanism of its therapeutic effect in this patient population is not well understood. To characterize changes in gut microbial and metabolomic profiles after FMT, we performed 16S rRNA gene sequencing, shotgun metagenomic sequencing, virome analysis and untargeted metabolomics by gas chromatography-time of flight-mass spectrometry on stool samples collected before and after FMT from four children with ulcerative colitis who responded to this treatment. Alpha diversity of the gut microbiota increased after intervention, with species richness rising from 251 (S.D. 125) to 358 (S.D. 27). In responders, the mean relative abundance of bacteria in the class Clostridia shifted toward donor levels, increasing from 33% (S.D. 11%) to 54% (S.D. 16%). Patient metabolomic and viromic profiles exhibited a similar but less pronounced shift toward donor profiles after FMT. The fecal concentrations of several metabolites were altered after FMT, correlating with clinical improvement. Larger studies using a similar multi-omics approach may suggest novel strategies for the treatment of pediatric ulcerative colitis.

Challenges, progress and promises of metabolite annotation for LC-MS-based metabolomics

Accurate annotation is vital for data interpretation; however, metabolite identification is a major bottleneck in untargeted metabolomics. Although community guidelines for metabolite identification were published over a decade ago, adaptation of the recommended standards has been limited. The complexity of LC-MS data due to combinations of various chromatographic and mass spectrometric acquisition methods has resulted in the advent of diverse workflows, which often involve non-standardized manual curation. Herein, we review the parameters involved in metabolite reporting and provide a workflow to estimate the level of confidence in reported metabolite annotation. The future of metabolite identification will be heavily based upon the use of metabolome data repositories and associated data analysis tools, which will enable data to be shared, re-analyzed and re-annotated in an automated fashion.

Metabolic signature of the aging eye in mice

Aging is a major risk factor for age-related ocular diseases including age-related macular degeneration in the retina and retinal pigment epithelium (RPE), cataracts in the lens, glaucoma in the optic nerve, and dry eye syndrome in the cornea. We used targeted metabolomics to analyze metabolites from young (6 weeks) and old (73 weeks) eyes in C57 BL6/J mice. Old mice had diminished electroretinogram responses and decreased number of photoreceptors in their retinas. Among the 297 detected metabolites, 45-114 metabolites are significantly altered in aged eye tissues, mostly in the neuronal tissues (retina and optic nerve) and less in cornea, RPE/choroid, and lens. We noted that changes of metabolites in mitochondrial metabolism and glucose metabolism are common features in the aged retina, RPE/choroid, and optic nerve. The aging retina, cornea, and optic nerve also share similar changes in Nicotinamide adenine dinucleotide (NAD), 1-methylnicotinamides, 3-methylhistidine, and other methylated metabolites. Metabolites in taurine metabolism are strikingly influenced by aging in the cornea and lens. In conclusion, the aging eye has both common and tissue-specific metabolic signatures. These changes may be attributed to dysregulated mitochondrial metabolism, reprogrammed glucose metabolism and impaired methylation in the aging eye. Our findings provide biochemical insights into the mechanisms of age-related ocular changes.

Improved LC/MS Methods for the Analysis of Metal-Sensitive Analytes Using Medronic Acid as a Mobile Phase Additive

Phosphorylated compounds and organic acids with multiple carboxylate groups are commonly observed to have poor peak shapes and signal in LC/MS experiments. The poor peak shape is caused by the presence of trace metals, particularly iron, contributed from a variety of sources within the chromatographic system. To ameliorate this problem, different solvent additives were investigated to reduce the amount of metal in the flow path to achieve better analytical performance for these metal-sensitive compounds. Here, we introduce the use of a solvent additive that can significantly improve the peak shapes and signal of metal-sensitive metabolites for LC/MS analysis. Moreover, the additive is shown to be amenable for other metal-sensitive applications, such as the analysis of phosphopeptides and polar phosphorylated pesticides, where the instruments could be used in either positive or negative analysis mode.

Metabolic Profiling of Chloroacetanilide Herbicides in Earthworm Coelomic Fluid Using 1H NMR and GC-MS

Earthworms ( Eisenia fetida) are vital members of the soil environment. Because of their sensitivity to many contaminants, monitoring earthworm metabolism may be a useful indicator of environmental stressors. Here, metabolic profiles of exposure to five chloroacetanilide herbicides and one enantiomer (acetochlor, alachlor, butachlor, racemic metolachlor, S-metolachlor, and propachlor) are observed in earthworm coelomic fluid using proton nuclear magnetic resonance spectroscopy (NMR) and gas chromatography-mass spectrometry (GC-MS). Multiblocked-orthogonal partial least-squares-discriminant analysis (MB-OPLS-DA) and univariate analysis were used to identify metabolic perturbations in carnitine biosynthesis, carbohydrate metabolism, lipid metabolism, nitrogen metabolism, and the tricarboxylic acid cycle. Intriguingly, stereospecific metabolic responses were observed between racemic metolachlor and S-metolachlor exposed worms. These findings support the utility of coelomic fluid in monitoring metabolic perturbations induced by chloroacetanilide herbicides in nontarget organisms and reveal specificity in the metabolic impacts of herbicide analogues in earthworms.

Identification of small molecules using accurate mass MS/MS search

Tandem mass spectral library search (MS/MS) is the fastest way to correctly annotate MS/MS spectra from screening small molecules in fields such as environmental analysis, drug screening, lipid analysis, and metabolomics. The confidence in MS/MS-based annotation of chemical structures is impacted by instrumental settings and requirements, data acquisition modes including data-dependent and data-independent methods, library scoring algorithms, as well as post-curation steps. We critically discuss parameters that influence search results, such as mass accuracy, precursor ion isolation width, intensity thresholds, centroiding algorithms, and acquisition speed. A range of publicly and commercially available MS/MS databases such as NIST, MassBank, MoNA, LipidBlast, Wiley MSforID, and METLIN are surveyed. In addition, software tools including NIST MS Search, MS-DIAL, Mass Frontier, SmileMS, Mass++, and XCMS2 to perform fast MS/MS search are discussed. MS/MS scoring algorithms and challenges during compound annotation are reviewed. Advanced methods such as the in silico generation of tandem mass spectra using quantum chemistry and machine learning methods are covered. Community efforts for curation and sharing of tandem mass spectra that will allow for faster distribution of scientific discoveries are discussed.

Contemporary lipidomic analytics: opportunities and pitfalls

Recent advances in analytical techniques have greatly enhanced the depth of coverage, however lipidomic studies are still restricted to analysing only a subset of known lipids. Numerous complementary techniques are used for investigation of cellular lipidomes, including mass spectrometry (MS), nuclear magnetic resonance and vibrational spectroscopy. The development in electrospray ionization (ESI) MS has accelerated lipidomics research in the past two decades and represents one of the most widely used technique. The versatility of ESI-MS systems allows development of methods to detect and quantify a large diversity of lipid species and classes. However, highly targeted and specific approaches can preclude global analysis of many lipid classes. Indeed, experimental procedures are generally optimised for the lipid species, or lipid class of interest. Therefore, careful consideration of experimental procedures is required for characterisation of biological lipidomes. The current review will describe the lipidomic approaches for considering tissue lipid physiology. Discussion of the main sequences in a lipidomics workflow will be presented, including preparation of samples, accurate quantitation of lipid species and statistical modelling.

Preanalytical Processing and Biobanking Procedures of Biological Samples for Metabolomics Research: A White Paper, Community Perspective (for "Precision Medicine and Pharmacometabolomics Task Group"-The Metabolomics Society Initiative)

BACKGROUND

The metabolome of any given biological system contains a diverse range of low molecular weight molecules (metabolites), whose abundances can be affected by the timing and method of sample collection, storage, and handling. Thus, it is necessary to consider the requirements for preanalytical processes and biobanking in metabolomics research. Poor practice can create bias and have deleterious effects on the robustness and reproducibility of acquired data.

CONTENT

This review presents both current practice and latest evidence on preanalytical processes and biobanking of samples intended for metabolomics measurement of common biofluids and tissues. It highlights areas requiring more validation and research and provides some evidence-based guidelines on best practices.

SUMMARY

Although many researchers and biobanking personnel are familiar with the necessity of standardizing sample collection procedures at the axiomatic level (e.g., fasting status, time of day, "time to freezer," sample volume), other less obvious factors can also negatively affect the validity of a study, such as vial size, material and batch, centrifuge speeds, storage temperature, time and conditions, and even environmental changes in the collection room. Any biobank or research study should establish and follow a well-defined and validated protocol for the collection of samples for metabolomics research. This protocol should be fully documented in any resulting study and should involve all stakeholders in its design. The use of samples that have been collected using standardized and validated protocols is a prerequisite to enable robust biological interpretation unhindered by unnecessary preanalytical factors that may complicate data analysis and interpretation.

Identification and evaluation of anti-inflammatory properties of aqueous components extracted from sesame (Sesamum indicum) oil

We previously reported that sesame oil (SO) has anti-inflammatory, anti-atherosclerotic and lipid lowering properties in vivo. Our recent studies have shown that, an aqueous extract of sesame oil (SOAE) has also anti-inflammatory and anti-atherosclerotic properties but with no lipid lowering effects. The extent of reduction in atherosclerosis led us to identify components of SOAE and evaluate their anti-inflammatory properties in vitro. Liquid chromatography mass spectrometric method was used to detect and identify components of SOAE. Methoxyphenol derivatives, short and long chain carboxylic acids, dicarboxylic acids, hydroxy and oxo- carboxylic acids were detected. To our surprise, sesamol and its derivatives (lignans), were not present in the SOAE. Among the identified, a combination of methoxy phenol compounds were selected and tested their ability to reduce LPS induced inflammatory gene expression. Monocyte derived macrophages/RAW 264.7 macrophages were pre-treated with these compounds for 2 h, followed by LPS stimulation for 24 h and pro-inflammatory gene expressions were analyzed. These methoxyphenol derivatives showed potent anti-inflammatory properties. In conclusion, the anti-inflammatory molecules associated with SO may contribute the anti-inflammatory and anti-atherosclerotic properties. Also, our results shed light for the development of SOAE based non-pharmacological therapeutics, nutritional supplements and health products for various inflammatory diseases in the future.

Nano-LC/NSI MS Refines Lipidomics by Enhancing Lipid Coverage, Measurement Sensitivity, and Linear Dynamic Range

Nano-liquid chromatography (nLC)-nanoelectrospray (NSI) is one of the cornerstones of mass-spectrometry-based bioanalytics. Nevertheless, the application of nLC is not yet prevalent in lipid analyses. In this study, we established a reproducible nLC separation for global lipidomics and describe the merits of using such a miniaturized system for lipid analyses. In order to enable comprehensive lipid analyses that is not restricted to specific lipid classes, we particularly optimized sample preparation conditions and reversed-phase separation parameters. We further benchmarked the developed nLC system to a commonly used high flow HPLC/ESI MS system in terms of lipidome coverage and sensitivity. The comparison revealed an intensity gain between 2 and 3 orders of magnitude for individual lipid classes and an increase in the linear dynamic range of up to 2 orders of magnitude. Furthermore, the analysis of the yeast lipidome using nLC/NSI resulted in more than a 3-fold gain in lipid identifications. All in all, we identified 447 lipids from the core phospholipid lipid classes (PA, PE, PC, PS, PG, and PI) in Saccharomyces cerevisiae.

The Microbiome and Metabolome of Preterm Infant Stool Are Personalized and Not Driven by Health Outcomes, Including Necrotizing Enterocolitis and Late-Onset Sepsis

Preterm infants face health problems likely related to microbial exposures, including sepsis and necrotizing enterocolitis. However, the role of the gut microbiome in preterm infant health is poorly understood. Microbial colonization differs from that of healthy term babies because it occurs in the NICU and is often perturbed by antibiotics. We measured bacterial compositions and metabolomic profiles of 77 fecal samples from 32 preterm infants to investigate the differences between microbiomes in health and disease. Rather than finding microbial signatures of disease, we found that both the preterm infant microbiome and the metabolome were personalized and that the preterm infant gut microbiome is enriched in microbes that commonly dominate in the presence of antibiotics. These results contribute to the growing knowledge of the preterm infant microbiome and emphasize that a personalized view will be important to disentangle the health consequences of the preterm infant microbiome.

The assembly and development of the gut microbiome in infants have important consequences for immediate and long-term health. Preterm infants represent an abnormal case for bacterial colonization because of early exposure to bacteria and frequent use of antibiotics. To better understand the assembly of the gut microbiota in preterm infants, fecal samples were collected from 32 very low birth weight preterm infants over the first 6 weeks of life. Infant health outcomes included health, late-onset sepsis, and necrotizing enterocolitis (NEC). We characterized bacterial compositions by 16S rRNA gene sequencing and metabolomes by untargeted gas chromatography-mass spectrometry. Preterm infant fecal samples lacked beneficial Bifidobacterium spp. and were dominated by Enterobacteriaceae, Enterococcus, and Staphylococcus organisms due to nearly uniform antibiotic administration. Most of the variance between the microbial community compositions could be attributed to the baby from which the sample derived (permutational multivariate analysis of variance [PERMANOVA] R² = 0.48, P < 0.001), while clinical status (health, NEC, or late-onset sepsis) and overlapping times in the neonatal intensive care unit (NICU) did not explain a significant amount of variation in bacterial composition. Fecal metabolomes were also found to be unique to the individual (PERMANOVA R² = 0.43, P < 0.001) and weakly associated with bacterial composition (Mantel statistic r = 0.23 ± 0.05, P < 0.05). No measured metabolites were found to be associated with necrotizing enterocolitis, late-onset sepsis, or a healthy outcome. Overall, preterm infant gut microbial communities were personalized and reflected antibiotic usage.

IMPORTANCE Preterm infants face health problems likely related to microbial exposures, including sepsis and necrotizing enterocolitis. However, the role of the gut microbiome in preterm infant health is poorly understood. Microbial colonization differs from that of healthy term babies because it occurs in the NICU and is often perturbed by antibiotics. We measured bacterial compositions and metabolomic profiles of 77 fecal samples from 32 preterm infants to investigate the differences between microbiomes in health and disease. Rather than finding microbial signatures of disease, we found that both the preterm infant microbiome and the metabolome were personalized and that the preterm infant gut microbiome is enriched in microbes that commonly dominate in the presence of antibiotics. These results contribute to the growing knowledge of the preterm infant microbiome and emphasize that a personalized view will be important to disentangle the health consequences of the preterm infant microbiome.

Impact of euthanasia, dissection and postmortem delay on metabolic profile in mouse retina and RPE/choroid

Metabolomics studies in the retina and retinal pigment epithelium (RPE) in animal models or postmortem donors are essential to understanding the retinal metabolism and to revealing the underlying mechanisms of retinal degenerative diseases. We have studied how different methods of euthanasia (CO₂ or cervical dislocation) different isolation procedures and postmortem delay affect metabolites in mouse retina and RPE/choroid using LC MS/MS and GC MS. Compared with cervical dislocation, CO₂ exposure for 5 min dramatically degrades ATP and GTP into purine metabolites in the retina while raising intermediates in glucose metabolism and amino acids in the RPE/choroid. Isolation in cold buffer containing glucose has the least change in metabolites. Postmortem delay time-dependently and differentially impacts metabolites in the retina and RPE/choroid. In the postmortem retina, 18% of metabolites were changed at 0.5 h (h), 41% at 4 h and 51% at 8 h. However, only 6% of metabolites were changed in the postmortem RPE/choroid and it steadily increased to 20% at 8 h. Notably, both postmortem retina and RPE/choroid tissue showed increased purine metabolites. Storage of eyes in cold nutrient-rich medium substantially blocked the postmortem change in the retina and RPE/choroid. In conclusion, our study provides optimized methods to prepare fresh or postmortem retina and RPE/choroid tissue for metabolomics studies.

Metabolomics and Ionomics of Potato Tuber Reveals an Influence of Cultivar and Market Class on Human Nutrients and Bioactive Compounds

Potato (Solanum tuberosum L.) is an important global food crop that contains phytochemicals with demonstrated effects on human health. Understanding sources of chemical variation of potato tuber can inform breeding for improved health attributes of the cooked food. Here, a comprehensive metabolomics (UPLC- and GC-MS) and ionomics (ICP-MS) analysis of raw and cooked potato tuber was performed on 60 unique potato genotypes that span 5 market classes including russet, red, yellow, chip, and specialty potatoes. The analyses detected 2,656 compounds that included known bioactives (43 compounds), nutrients (42), lipids (76), and 23 metals. Most nutrients and bioactives were partially degraded during cooking (44 out of 85; 52%), however genotypes with high quantities of bioactives remained highest in the cooked tuber. Chemical variation was influenced by genotype and market class. Specifically, ~53% of all detected compounds from cooked potato varied among market class and 40% varied by genotype. The most notable metabolite profiles were observed in yellow-flesh potato which had higher levels of carotenoids and specialty potatoes which had the higher levels of chlorogenic acid as compared to the other market classes. Variation in several molecules with known association to health was observed among market classes and included vitamins (e.g., pyridoxal, ~2-fold variation), bioactives (e.g., chlorogenic acid, ~40-fold variation), medicinals (e.g., kukoamines, ~6-fold variation), and minerals (e.g., calcium, iron, molybdenum, ~2-fold variation). Furthermore, more metabolite variation was observed within market class than among market class (e.g., α-tocopherol, ~1-fold variation among market class vs. ~3-fold variation within market class). Taken together, the analysis characterized significant metabolite and mineral variation in raw and cooked potato tuber, and support the potential to breed new cultivars for improved health traits.

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.

SWATH Tandem Mass Spectrometry Workflow for Quantification of Mass Isotopologue Distribution of Intracellular Metabolites and Fragments Labeled with Isotopic 13C Carbon

Accurate quantification of mass isotopologue distribution (MID) of metabolites is a prerequisite for ¹³C-metabolic flux analysis. Currently used mass spectrometric (MS) techniques based on multiple reaction monitoring (MRM) place limitations on the number of MIDs that can be analyzed in a single run. Moreover, the deconvolution step results in amplification of error. Here, we demonstrate that SWATH MS/MS, a data independent acquisition (DIA) technique allows quantification of a large number of precursor and product MIDs in a single run. SWATH sequentially fragments all precursor ions in stacked mass isolation windows. Co-fragmentation of all precursor isotopologues in a single SWATH window yields higher sensitivity enabling quantification of MIDs of fragments with low abundance and lower systematic and random errors. We quantify the MIDs of 53 precursor and product ions corresponding to 19 intracellular metabolites from a dynamic ¹³C-labeling of a model cyanobacterium, Synechococcus sp. PCC 7002. The use of product MIDs resulted in an improved precision of many measured fluxes compared to when only precursor MIDs were used for flux analysis. The approach is truly untargeted and allows additional metabolites to be quantified from the same data.

Comprehensive evaluation of untargeted metabolomics data processing software in feature detection, quantification and discriminating marker selection

Data analysis represents a key challenge for untargeted metabolomics studies and it commonly requires extensive processing of more than thousands of metabolite peaks included in raw high-resolution MS data. Although a number of software packages have been developed to facilitate untargeted data processing, they have not been comprehensively scrutinized in the capability of feature detection, quantification and marker selection using a well-defined benchmark sample set. In this study, we acquired a benchmark dataset from standard mixtures consisting of 1100 compounds with specified concentration ratios including 130 compounds with significant variation of concentrations. Five software evaluated here (MS-Dial, MZmine 2, XCMS, MarkerView, and Compound Discoverer) showed similar performance in detection of true features derived from compounds in the mixtures. However, significant differences between untargeted metabolomics software were observed in relative quantification of true features in the benchmark dataset. MZmine 2 outperformed the other software in terms of quantification accuracy and it reported the most true discriminating markers together with the fewest false markers. Furthermore, we assessed selection of discriminating markers by different software using both the benchmark dataset and a real-case metabolomics dataset to propose combined usage of two software for increasing confidence of biomarker identification. Our findings from comprehensive evaluation of untargeted metabolomics software would help guide future improvements of these widely used bioinformatics tools and enable users to properly interpret their metabolomics results.

Fermentation products in the cystic fibrosis airways induce aggregation and dormancy-associated expression profiles in a CF clinical isolate of Pseudomonas aeruginosa

Pseudomonas aeruginosa is a well-known dominant opportunistic pathogen in cystic fibrosis (CF) with a wide range of metabolic capacities. However, P. aeruginosa does not colonize the airways alone, and benefits from the metabolic products of neighboring cells-especially volatile molecules that can travel between different parts of the airways easily. Here, we present a study that investigates the metabolic, gene expression profiles and phenotypic responses of a P. aeruginosa clinical isolate to fermentation products lactic acid and 2,3-butanediol, metabolites that are produced by facultative anaerobic members of the CF polymicrobial community and potential biomarkers of disease progression. Although previous studies have successfully investigated the metabolic and transcriptional profiles of P. aeruginosa, most have used common lab reference strains that may differ in important ways from clinical isolates. Using transcriptomics and metabolomics with gas chromatography time of flight mass spectrometry, we observe that fermentation products induce pyocyanin production along with the expression of genes involved in P. aeruginosa amino acid utilization, dormancy and aggregative or biofilm modes of growth. These findings have important implications for how interactions within the diverse CF microbial community influence microbial physiology, with potential clinical consequences.

Profiling of small molecule metabolites and neurotransmitters in crustacean hemolymph and neuronal tissues using reversed-phase LC-MS/MS

Crustaceans have been long used as model animals for neuromodulation studies because of their well-defined neural circuitry. The identification of small molecule metabolites and signaling molecules in circulating fluids and neuronal tissues presents unique challenges due to their diverse structures, biological functions, and wide range of concentrations. LC combined with high resolution MS/MS is one of the most powerful tools to uncover endogenous small molecules. Here we explored several sample preparation techniques (solid-phase extraction and denaturing) and MS data acquisition strategies (data-dependent acquisition and targeted MS2-based acquisition) that provided complementary coverage and improved overall identification rate in C18 LC-MS/MS experiment. By MS/MS spectral matching with mzCloud database and those generated from standard compounds, a total of 129 small molecule metabolites and neurotransmitters were identified from crustacean hemolymph and neuronal tissues. These confidently identified small molecules covered predominant biosynthetic pathways for major neurotransmitters, validating the effectiveness of the high-throughput RPLC-MS/MS approach in studying the metabolism of neurotransmitters.

LipiDex: An Integrated Software Package for High-Confidence Lipid Identification

State-of-the-art proteomics software routinely quantifies thousands of peptides per experiment with minimal need for manual validation or processing of data. For the emerging field of discovery lipidomics via liquid chromatography-tandem mass spectrometry (LC-MS/MS), comparably mature informatics tools do not exist. Here, we introduce LipiDex, a freely available software suite that unifies and automates all stages of lipid identification, reducing hands-on processing time from hours to minutes for even the most expansive datasets. LipiDex utilizes flexible in silico fragmentation templates and lipid-optimized MS/MS spectral matching routines to confidently identify and track hundreds of lipid species and unknown compounds from diverse sample matrices. Unique spectral and chromatographic peak purity algorithms accurately quantify co-isolation and co-elution of isobaric lipids, generating identifications that match the structural resolution afforded by the LC-MS/MS experiment. During final data filtering, ionization artifacts are removed to significantly reduce dataset redundancy. LipiDex interfaces with several LC-MS/MS software packages, enabling robust lipid identification to be readily incorporated into pre-existing data workflows.

Current and future perspectives of functional metabolomics in disease studies-A review

Functional metabolomics is a new concept, which studies the functions of metabolites and related enzymes focused on metabolomics. It overcomes the shortcomings of traditional discovery metabolomics of mainly relying on literatures for biological interpretation. Functional metabolomics has many advantages. Firstly, the functional roles of metabolites and related metabolic enzymes are focused. Secondly, the in vivo and in vitro experiments are conducted to validate the metabolomics findings, therefore, increasing the reliability of metabolomics study and producing the new knowledge. Thirdly, functional metabolomics can be used by biologists to investigate functions of metabolites, and related genes and proteins. In this review, we summarize the analytical, biological and clinical platforms used in functional metabolomics studies. Recent progresses of functional metabolomics in cancer, metabolic diseases and biological phenotyping are reviewed, and future development is also predicted. Because of the tremendous advantages of functional metabolomics, it will have a bright future.

Data processing, multi-omic pathway mapping, and metabolite activity analysis using XCMS Online

Systems biology is the study of complex living organisms, and as such, analysis on a systems-wide scale involves the collection of information-dense data sets that are representative of an entire phenotype. To uncover dynamic biological mechanisms, bioinformatics tools have become essential to facilitating data interpretation in large-scale analyses. Global metabolomics is one such method for performing systems biology, as metabolites represent the downstream functional products of ongoing biological processes. We have developed XCMS Online, a platform that enables online metabolomics data processing and interpretation. A systems biology workflow recently implemented within XCMS Online enables rapid metabolic pathway mapping using raw metabolomics data for investigating dysregulated metabolic processes. In addition, this platform supports integration of multi-omic (such as genomic and proteomic) data to garner further systems-wide mechanistic insight. Here, we provide an in-depth procedure showing how to effectively navigate and use the systems biology workflow within XCMS Online without a priori knowledge of the platform, including uploading liquid chromatography (LC)-mass spectrometry (MS) data from metabolite-extracted biological samples, defining the job parameters to identify features, correcting for retention time deviations, conducting statistical analysis of features between sample classes and performing predictive metabolic pathway analysis. Additional multi-omics data can be uploaded and overlaid with previously identified pathways to enhance systems-wide analysis of the observed dysregulations. We also describe unique visualization tools to assist in elucidation of statistically significant dysregulated metabolic pathways. Parameter input takes 5-10 min, depending on user experience; data processing typically takes 1-3 h, and data analysis takes ∼30 min.

Systematic profiling and comparison of the lipidomes from Panax ginseng, P. quinquefolius, and P. notoginseng by ultrahigh performance supercritical fluid chromatography/high-resolution mass spectrometry and ion mobility-derived collision cross section measurement

Lipidomics currently is still confronted with challenges from chromatographic separation and lipids identification. Here we report a lipidomics platform by integrating ultrahigh performance supercritical fluid chromatography/quadrupole time-of-flight mass spectrometry (UHPSFC/QTOF-MS) and collision cross section (CCS) measurement using ion mobility spectroscopy/time-of-flight mass spectrometry (IMS/QTOF-MS), aiming to enhance the profiling performance and identification reliability of lipids. The lipidomes extracted from three congeneric Panax species (P. ginseng, P. quinquefolius, and P. notoginseng) by methyl tert-butyl ether are comprehensively profiled and compared by use of this platform. A potent UHPSFC/QTOF-MS approach was developed on a 1.7-μm particles packed Torus 2-PIC column using CH₃OH (in CO₂) as a modifier and CH₃OH/0.2 mM ammonium acetate as the makeup liquid, enabling well resolution of six lipid subclasses by both positive and negative MS^E modes. In contrast to the reversed-phase chromatography, "normal-phase" like elution order and better resolution of polar lipids and some lipid isomers were achieved by UHPSFC separation. Pattern recognition chemometric analysis of 60 batches of Ginseng samples ultimately unveiled 24 lipid markers, of which triacylglycerols were the most important. Aside from the automated MS database searching against HMDB and LIPID MAPS, the application of CCS retrieval or CCS prediction improved lipid identification by reducing the possible hits. In conclusion, this integral platform can significantly improve the chromatographic separation and the reliability of lipids identification in lipidomics studies. It is the first report that systematically compares the lipidomic difference of three reputable Panax species, providing useful information for their quality control in addition to ginsenoside analysis.

Screening of stationary phase selectivities for global lipid profiling by ultrahigh performance supercritical fluid chromatography

The performance of seven sub-2-μm particle packed columns (2-picolylamine, 2-PIC; charged surface hybrid fluoro-phenyl, CSH-FP; high strength silica C18 SB, HSS-C₁₈; diethylamine, DEA; 1-aminoanthracene, 1-AA; high density diol and ethylene bridged hybrid; BEH) was examined for lipid separation in ultra-high performance supercritical fluid chromatography (UHPSFC) coupled to quadrupole time-of-flight mass spectrometry. Based on the results of the column screening a method for profiling of multiple lipid species from the major lipid classes was developed. Stationary phases containing β-hydroxy amines, i.e. 1-AA, DEA and 2-PIC, yielded strong retention and poor peak shapes of zwitterionic lipids with primary amine groups, such as phosphatidylserines, phosphatidylethanolamines and its lyso forms. The BEH and HSS-C₁₈ columns showed strong retention of polar and nonpolar lipids, respectively. The Diol column retained the majority of major lipid classes and also produced symmetric peaks. In addition, this column also produced the highest resolution within and between major lipid classes. An injection solvent composed of methanol:chloroform (1:2, v:v) and the addition of 20 mM ammonium formate in the mobile phase improved chromatographic separation and mass spectrometry detection in comparison to ammonium acetate or absence of additive. Finally, chromatographic and mass spectrometric parameters were optimized for the Diol column using a design of experiments approach. The separation mechanism on the Diol column depended on the lipid functionality and the length and degree of unsaturation of the acyl groups. The developed method could resolve 18 lipid classes and multiple lipids within each class, from blood serum and brain tissue in 11 min.

SWATHtoMRM: Development of High-Coverage Targeted Metabolomics Method Using SWATH Technology for Biomarker Discovery

The complexity of metabolome presents a great analytical challenge for quantitative metabolite profiling, and restricts the application of metabolomics in biomarker discovery. Targeted metabolomics using multiple-reaction monitoring (MRM) technique has excellent capability for quantitative analysis, but suffers from the limited metabolite coverage. To address this challenge, we developed a new strategy, namely, SWATHtoMRM, which utilizes the broad coverage of SWATH-MS technology to develop high-coverage targeted metabolomics method. Specifically, SWATH-MS technique was first utilized to untargeted profile one pooled biological sample and to acquire the MS² spectra for all metabolites. Then, SWATHtoMRM was used to extract the large-scale MRM transitions for targeted analysis with coverage as high as 1000-2000 metabolites. Then, we demonstrated the advantages of SWATHtoMRM method in quantitative analysis such as coverage, reproducibility, sensitivity, and dynamic range. Finally, we applied our SWATHtoMRM approach to discover potential metabolite biomarkers for colorectal cancer (CRC) diagnosis. A high-coverage targeted metabolomics method with 1303 metabolites in one injection was developed to profile colorectal cancer tissues from CRC patients. A total of 20 potential metabolite biomarkers were discovered and validated for CRC diagnosis. In plasma samples from CRC patients, 17 out of 20 potential biomarkers were further validated to be associated with tumor resection, which may have a great potential in assessing the prognosis of CRC patients after tumor resection. Together, the SWATHtoMRM strategy provides a new way to develop high-coverage targeted metabolomics method, and facilitates the application of targeted metabolomics in disease biomarker discovery. The SWATHtoMRM program is freely available on the Internet ( http://www.zhulab.cn/software.php ).

When Transcriptomics and Metabolomics Work Hand in Hand: A Case Study Characterizing Plant CDF Transcription Factors

Over the last three decades, novel “omics” platform technologies for the sequencing of DNA and complementary DNA (cDNA) (RNA-Seq), as well as for the analysis of proteins and metabolites by mass spectrometry, have become more and more available and increasingly found their way into general laboratory life. With this, the ability to generate highly multivariate datasets on the biological systems of choice has increased tremendously. However, the processing and, perhaps even more importantly, the integration of “omics” datasets still remains a bottleneck, although considerable computational and algorithmic advances have been made in recent years. In this mini-review, we use a number of recent “multi-omics” approaches realized in our laboratories as a common theme to discuss possible pitfalls of applying “omics” approaches and to highlight some useful tools for data integration and visualization in the form of an exemplified case study. In the selected example, we used a combination of transcriptomics and metabolomics alongside phenotypic analyses to functionally characterize a small number of Cycling Dof Transcription Factors (CDFs). It has to be remarked that, even though this approach is broadly used, the given workflow is only one of plenty possible ways to characterize target proteins.

Untargeted Metabolomics Reveal Lipid Alterations upon 2-Deoxyglucose Treatment in Human HaCaT Keratinocytes

The glucose analogue 2-deoxyglucose (2-DG) impedes cancer progression in animal models and is currently being assessed as an anticancer therapy, yet the mode of action of this drug of high clinical significance has not been fully delineated. In an attempt to better characterize its pharmacodynamics, an integrative UPLC-Q-Exactive-based joint metabolomic and lipidomic approach was undertaken to evaluate the metabolic perturbations induced by this drug in human HaCaT keratinocyte cells. R-XCMS data processing and subsequent multivariate pattern recognition, metabolites identification, and pathway analyses identified eight metabolites that were most significantly changed upon a 3 h 2-DG exposure. Most of these dysregulated features were emphasized in the course of lipidomic profiling and could be identified as ceramide and glucosylceramide derivatives, consistently with their involvement in cell death programming. Even though metabolomic analyses did not generally afford such clear-cut dysregulations, some alterations in phosphatidylcholine and phosphatidylethanolamine derivatives could be highlighted as well. Overall, these results support the adequacy of the proposed analytical workflow and might contribute to a better understanding of the mechanisms underlying the promising effects of 2-DG.

Feline urine metabolomic signature: characterization of low-molecular-weight substances in urine from domestic cats

Objectives This aim of this study was to characterize the composition and content of the feline urine metabolome. Methods Eight healthy domestic cats were acclimated at least 10 days before starting the study. Urine samples (~2 ml) were collected by ultrasound-guided cystocentesis. Samples were centrifuged at 1000 × g for 8 mins, and the supernatant was analyzed by gas chromatography/time-of-flight mass spectrometery. The urine metabolome was characterized using an untargeted metabolomics approach. Results Three hundred and eighteen metabolites were detected in the urine of the eight cats. These molecules are key components of at least 100 metabolic pathways. Feline urine appears to be dominated by carbohydrates, carbohydrate conjugates, organic acid and derivatives, and amino acids and analogs. The five most abundant molecules were phenaceturic acid, hippuric acid, pseudouridine phosphate and 3-(4-hydroxyphenyl) propionic acid. Conclusions and relevance This study is the first to characterize the feline urine metabolome. The results of this study revealed the presence of multiple low-molecular-weight substances that were not known to be present in feline urine. As expected, the origin of the metabolites detected in urine was diverse, including endogenous compounds and molecules biosynthesized by microbes. Also, the diet seemed to have had a relevant role on the urine metabolome. Further exploration of the urine metabolic phenotype will open a window for discovering unknown, or poorly understood, metabolic pathways. In turn, this will advance our understanding of feline biology and lead to new insights in feline physiology, nutrition and medicine.

Metabolomics of Early Stage Plant Cell-Microbe Interaction Using Stable Isotope Labeling

Metabolomics has been used in unraveling metabolites that play essential roles in plant-microbe (including pathogen) interactions. However, the problem of profiling a plant metabolome with potential contaminating metabolites from the coexisting microbes has been largely ignored. To address this problem, we implemented an effective stable isotope labeling approach, where the metabolome of a plant bacterial pathogen Pseudomonas syringae pv. tomato (Pst) DC3000 was labeled with heavy isotopes. The labeled bacterial cells were incubated with Arabidopsis thaliana epidermal peels (EPs) with guard cells, and excessive bacterial cells were subsequently removed from the plant tissues by washing. The plant metabolites were characterized by liquid chromatography mass spectrometry using multiple reactions monitoring, which can differentiate plant and bacterial metabolites. Targeted metabolomic analysis suggested that Pst DC3000 infection may modulate stomatal movement by reprograming plant signaling and primary metabolic pathways. This proof-of-concept study demonstrates the utility of this strategy in differentiation of the plant and microbe metabolomes, and it has broad applications in studying metabolic interactions between microbes and other organisms.