Untargeted Metabolite Profiling Research Articles

A classification of liquid chromatography mass spectrometry techniques for evaluation of chemical composition and quality control of traditional medicines

Natural products (NPs) and traditional medicines (TMs) are used for treatment of various diseases and also to develop new drugs. However, identification of drug leads within the immense biodiversity of living organisms is a challenging task that requires considerable time, labor, and computational resources as well as the application of modern analytical instruments. LC–MS platforms are widely used for both drug discovery and quality control of TMs and food supplements. Moreover, a large dataset generated during LC–MS analysis contains valuable information that could be extracted and handled by means of various data mining and statistical tools. Novel sophisticated LC–MS based approaches are being introduced every year. Therefore, this review is prepared for the scientists specialized in pharmacognosy and analytical chemistry of NPs as well as working in related areas, in order to navigate them in the world of diverse LC–MS based techniques and strategies currently employed for NP discovery and dereplication, quality control, pattern recognition and sample comparison, and also in targeted and untargeted metabolomic studies. The suggested classification system includes the following LC–MS based procedures: elemental composition determination, isotopic fine structure analysis, mass defect filtering, de novo identification, clustering of the compounds in Molecular Networking (MN), diagnostic fragment ion (or neutral loss) filtering, manual dereplication using MS/MS data, database-assisted peak annotation, annotation of spectral trees, MS fingerprinting, feature extraction, bucketing of LC–MS data, peak profiling, predicted metabolite screening, targeted quantification of biomarkers, quantitative analysis of multi-component system, construction of chemical fingerprints, multi-targeted and untargeted metabolite profiling.

Combining untargeted and targeted metabolomics approaches for the standardization of polyherbal formulations through UPLC-MS/MS.

Polyherbal formulations are an integral part of various indigenous medicinal systems such as Traditional Chinese Medicine (TCM) and Ayurveda. The presence of a very large number of compounds makes the quality control of polyherbal formulations very difficult. To overcome this problem, we have developed a comprehensive strategy for the dereplication of natural products in polyherbal formulations by using Adhatoda vasica as a case study. The strategy is based on five major steps: the collection of plant samples from different locations to observe the effects of environmental variables; LC-ESI-MS/MS-based untargeted metabolite profiling of the plant samples to identify marker compounds using extensive chemometric analysis of the obtained data; the identification of marker compounds in polyherbal products; the isolation, purification and characterization of the marker compounds; and MRM-based quantitative analysis of the isolated marker compounds using LC-ESI-MS/MS. Using this strategy, we identified a total of 51 compounds in the methanolic extract of A. vasica plants from 14 accessions. Chemical fingerprinting of the plant led to the identification of characteristic peaks that were used to confirm the presence of A. vasica in complex polyherbal formulations. Four quinazoline alkaloids (marker compounds) were isolated, purified and quantified in various herbal formulations containing A. vasica. This method demonstrates a comprehensive strategy based on untargeted and targeted metabolite analysis that can be used for the standardization of complex polyherbal formulations.

Serine and 1-carbon metabolism are required for HIF-mediated protection against retinopathy of prematurity.

We determined which metabolic pathways are activated by hypoxia-inducible factor 1-mediated (HIF-1-mediated) protection against oxygen-induced retinopathy (OIR) in newborn mice, the experimental correlate to retinopathy of prematurity, a leading cause of infant blindness. HIF-1 coordinates the change from oxidative to glycolytic metabolism and mediates flux through serine and 1-carbon metabolism (1CM) in hypoxic and cancer cells. We used untargeted metabolite profiling in vivo to demonstrate that hypoxia mimesis activates serine/1CM. Both [13C6] glucose labeling of metabolites in ex vivo retinal explants as well as in vivo [13C3] serine labeling of metabolites followed in liver lysates strongly suggest that retinal serine is primarily derived from hepatic glycolytic carbon and not from retinal glycolytic carbon in newborn pups. In HIF-1α2lox/2lox albumin-Cre-knockout mice, reduced or near-0 levels of serine/glycine further demonstrate the hepatic origin of retinal serine. Furthermore, inhibition of 1CM by methotrexate blocked HIF-mediated protection against OIR. This demonstrated that 1CM participates in protection induced by HIF-1 stabilization. The urea cycle also dominated pathway enrichment analyses of plasma samples. The dependence of retinal serine on hepatic HIF-1 and the upregulation of the urea cycle emphasize the importance of the liver to remote protection of the retina.

High-resolution mass spectrometry-based metabolomics reveal the disruption of jasmonic pathway in Arabidopsis thaliana upon copper oxide nanoparticle exposure.

Mass-spectrometry based metabolomics has recently emerged as a valuable technique in understanding the ecotoxicity and mode of action of a wide range of xenobiotics in the environment, including engineered nanomaterials (ENMs). However, the applications of metabolomics in elucidating the biochemical pathways affected by xenobiotics have been mostly performed using targeted analysis. In this study, the effects of copper oxide nanoparticles (CuO NPs) on Arabidopsis thaliana, a model plant, was investigated using untargeted metabolite profiling based on two platforms of high-resolution mass spectrometry (MS): (1) liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QToF-MS) and (2) LC Q Exactive™ Hybrid Quadrupole-Orbitrap™-MS (LC-Orbitrap-MS). This approach was performed to identify specific features (mass-to-charge ratios, m/z's) that are significantly changed in a reproducible manner regardless of the MS platform used in metabolomics. In addition, the total copper concentrations taken up in plant tissues were quantified using inductively coupled plasma mass spectrometry (ICP-MS), which provided evidence of translocation of CuO NPs from roots to leaves and flowering shoots. Results from untargeted metabolomics showed that there were 65 plant metabolites that were altered commonly in both LC/MS platforms resulting from CuO NPs exposure of Arabidopsis thaliana. These metabolites belong to the jasmonic acid and glucosinolates pathways, suggesting the stress response induced by CuO NPs in Arabidopsis. This study demonstrated the effectiveness of high-resolution LC/MS in providing insight on the mechanism of nanotoxicity of CuO NPs in plants.

Proposal for a chemically consistent way to annotate ions arising from the analysis of reference compounds under ESI conditions: A prerequisite to proper mass spectral database constitution in metabolomics

Large-scale untargeted metabolite profiling of complex biological matrices has become essential in many research fields of life sciences. In this context, LC-MS-based approaches offer a higher sensitivity means of analysis allowing for the detection of a large range of metabolites. The metabolite identification process starts with data set annotation, which consists in associating features such as its m/z ratio and retention time with the name of a metabolite by means of biochemical, metabolomics and spectral database searches. The compound candidate is then tentatively identified by tandem MS. Harmonized and standardized description and annotation of mass spectra and product ion spectra of selected precursor ions to facilitate data sharing exploitation especially in a metabolomics context is becoming necessary. In this perspective special feature, the authors report and illustrate their view to assign and chemically describe the variety of ion species arising from the ionization of every single small molecule under ESI-HRMS conditions, in a unified, chemically-consistent and potentially software-compatible manner. The authors are associated with the French Infrastructure for Metabolomics and Fluxomics (MetaboHUB consortium), which provides state-of-the-art tools, services and support in metabolomics and fluxomics to academic research teams and industrial partners in the fields of nutrition, health, agriculture and biotechnology.

Spatially Defined Surface Sampling Capillary Electrophoresis Mass Spectrometry.

Capillary electrophoresis mass spectrometry (CE-MS) is an established technique for targeted and untargeted analysis of metabolites from complex biological samples. However, current CE-MS devices rely on liquid sample extracts, which restricts acquisition of spatially defined chemical information from tissue samples. The ability to chemically profile distinct cellular regions in tissue can contribute better understanding to molecular foundations in health and disease. Therefore, we describe the first CE-MS device capable of untargeted metabolite profiling directly from defined morphological regions of solid tissue sections. With surface sampling capillary electrophoresis mass spectrometry (SS-CE-MS), endogenous molecules are sampled and detected from a single defined tissue location. Characterization of SS-CE-MS from different locations of the outer epidermal layer of A. Cepa demonstrated reproducible relative migration times and a peak area RSD of 20% ( n = 5). Further, relative migration times were conserved for endogenous metabolites in tissues with varying complexities, including brain, spinal cord, and kidney. Results from proof-of-principle experiments from distinct morphological tissue regions reveal simultaneous analysis of small and large biomolecules, confident metabolite annotation, identification of in-source fragmentation interferences, and discrete isomeric abundances related to biological function. We envision that this new tool will provide in-depth chemical profiling and annotation of molecules in distinct cellular regions of tissue for improved biological understanding.

Profiling of Small Molecular Metabolites in Nostoc flagelliforme during Periodic Desiccation.

The mass spectrometry-based metabolomics approach has become a powerful tool for the quantitative analysis of small-molecule metabolites in biological samples. Nostoc flagelliforme, an edible cyanobacterium with herbal value, serves as an unexploited bioresource for small molecules. In natural environments, N. flagelliforme undergoes repeated cycles of rehydration and dehydration, which are interrupted by either long- or short-term dormancy. In this study, we performed an untargeted metabolite profiling of N. flagelliforme samples at three physiological states: Dormant (S1), physiologically fully recovered after rehydration (S2), and physiologically partially inhibited following dehydration (S3). Significant metabolome differences were identified based on the OPLS-DA (orthogonal projections to latent structures discriminant analysis) model. In total, 183 differential metabolites (95 up-regulated; 88 down-regulated) were found during the rehydration process (S2 vs. S1), and 130 (seven up-regulated; 123 down-regulated) during the dehydration process (S3 vs. S2). Thus, it seemed that the metabolites’ biosynthesis mainly took place in the rehydration process while the degradation or possible conversion occurred in the dehydration process. In addition, lipid profile differences were particularly prominent, implying profound membrane phase changes during the rehydration–dehydration cycle. In general, this study expands our understanding of the metabolite dynamics in N. flagelliforme and provides biotechnological clues for achieving the efficient production of those metabolites with medical potential.

Combined hydrophilic interaction liquid chromatography-scanning field asymmetric waveform ion mobility spectrometry-time-of-flight mass spectrometry for untargeted metabolomics

Untargeted metabolite profiling of biological samples is a challenge for analytical science due to the high degree of complexity of biofluids. Isobaric species may also not be resolved using mass spectrometry alone. As a result of these factors, many potential biomarkers may not be detected or are masked by co-eluting interferences in conventional LC-MS metabolomic analyses. In this study, a comprehensive liquid chromatography-mass spectrometry workflow incorporating a fast-scanning miniaturised high-field asymmetric waveform ion mobility spectrometry separation (LC-FAIMS-MS) is applied to the untargeted metabolomic analysis of human urine. The time-of-flight mass spectrometer used in the study was scanned at a rate of 20 scans s−1 enabling a FAIMS CF spectrum to be acquired within a 1-s scan time, maintaining an adequate number of data points across each LC peak. The developed method is demonstrated to be able to resolve co-eluting isomeric species and shows good reproducibility (%RSD < 4.9%). The nested datasets obtained for fresh, aged, and QC urine samples were submitted for multivariate statistical analysis. Seventy unique biomarker ions showing a statistically significant difference between fresh and aged urine were identified with optimal transmission CF values obtained across the full CF spectrum. The potential of using FAIMS to select ions for in-source collision-induced dissociation is demonstrated for FAIMS-selected methylxanthine ions yielding characteristic fragment ion species indicative of the precursor.Graphical abstract

Untargeted Metabolite Profiling of Adipose Tissue in Hyperlipidemia Rats Exposed to Hawthorn Ethanol Extracts.

The study aimed to explore the metabolic changes of adipose tissue of hyperlipidemia rats with hawthorn ethanol extracts (HEE) consumption by a high-throughput metabolomics approach. HEE were mainly composed of chlorogenic acid, hyperoside, isoquercitrin, rutin, vitexin, quercetin, and apigenin by HPLC analysis. HEE administration significantly lowered levels of the total cholesterols, triglyceride and low-density lipoprotein cholesterol as compared to the high-fat diet model. Gas chromatography-mass spectrometry was used to identify adipose metabolite profiles. Numerous endogenous molecules were altered by high-fat diet and restored following intervention of HEE. Metabolites elevated in adipose, including l-threonine, aspartic acid, glutamine, mannose, inositol and oleic acid, were detected after HEE consumption. Fifteen metabolites were identified as potential biomarkers of hyperlipidemia. Pathway analysis showed that most of the discriminant metabolites were included in fatty acid biosynthesis, galactose metabolism, biosynthesis of unsaturated fatty acids, arginine and proline metabolism, alanine, aspartate and glutamate metabolism, glycerolipid metabolism and steroid biosynthesis. These metabolites and metabolic networks we found offer new insights into exploring the molecular mechanisms of lipid-lowering of hawthorn ethanol extracts on adipose tissue of rats. PRACTICAL APPLICATION: There was a very high proportion of hyperlipidemia in China. Hawthorn is attracting increasing attention owing to their health benefits, low toxicity, effectiveness and might be suitable for long-term use.

A substrate of the ABC transporter PEN3 stimulates bacterial flagellin (flg22)-induced callose deposition in Arabidopsis thaliana

Nonhost resistance of Arabidopsis thaliana against Phytophthora infestans, a filamentous eukaryotic microbe and the causal agent of potato late blight, is based on a multilayered defense system. Arabidopsis thaliana controls pathogen entry through the penetration-resistance genes PEN2 and PEN3, encoding an atypical myrosinase and an ABC transporter, respectively, required for synthesis and export of unknown indole compounds. To identify pathogen-elicited leaf surface metabolites and further unravel nonhost resistance in Arabidopsis, we performed untargeted metabolite profiling by incubating a P. infestans zoospore suspension on leaves of WT or pen3 mutant Arabidopsis plants. Among the plant-secreted metabolites, 4-methoxyindol-3-yl-methanol and S-(4-methoxy-indol-3-yl-methyl) cysteine were detected in spore suspensions recollected from WT plants, but at reduced levels from the pen3 mutant plants. In both whole-cell and microsome-based assays, 4-methoxyindol-3-yl-methanol was transported in a PEN3-dependent manner, suggesting that this compound is a PEN3 substrate. The syntheses of both compounds were dependent on functional PEN2 and phytochelatin synthase 1. None of these compounds inhibited mycelial growth of P. infestans in vitro Of note, exogenous application of 4-methoxyindol-3-yl methanol slightly elevated cytosolic Ca2+ levels and enhanced callose deposition in hydathodes of seedlings treated with a bacterial pathogen-associated molecular pattern (PAMP), flagellin (flg22). Loss of flg22-induced callose deposition in leaves of pen3 seedlings was partially reverted by the addition of 4-methoxyindol-3-yl methanol. In conclusion, we have identified a specific indole compound that is a substrate for PEN3 and contributes to the plant defense response against microbial pathogens.

The Influence of Contrasting Microbial Lifestyles on the Pre-symbiotic Metabolite Responses of Eucalyptus grandis Roots

Plant roots co-inhabit the soil with a diverse consortium of microbes of which a number attempt to enter symbiosis with the plant. These microbes may be pathogenic, mutualistic, or commensal. Hence, the health and survival of plants is heavily reliant on their ability to perceive different microbial lifestyles and respond appropriately. Emerging research suggests that there is a pivotal role for plant root secondary metabolites in responding to microbial colonization. However, it is largely unknown if plants are able to differentiate between microbes of different lifestyles and respond differently during the earliest stages of pre-symbiosis (i.e., prior to physical contact). In studying plant responses to a range of microbial isolates, we questioned: (1) if individual microbes of different lifestyles and species caused alterations to the plant root metabolome during pre-symbiosis, and (2) if these early metabolite responses correlate with the outcome of the symbiotic interaction in later phases of colonization. We compared the changes of the root tip metabolite profile of the model tree Eucalyptus grandis during pre-symbiosis with two isolates of a pathogenic fungus (Armillaria luteobubalina), one isolate of a pathogenic oomycete (Phytophthora cinnamomi), two isolates of an incompatible mutualistic fungus (Suillus granulatus), and six isolates of a compatible mutualistic fungus (Pisolithus microcarpus). Untargeted metabolite profiling revealed predominantly positive root metabolite responses at the pre-symbiosis stage, prior to any observable phenotypical changes of the root tips. Metabolite responses in the host tissue that were specific to each microbial species were identified. A deeper analysis of the root metabolomic profiles during pre-symbiotic contact with six strains of P. microcarpus showed a connection between these early metabolite responses in the root with later colonization success. Further investigation using isotopic tracing revealed a portion of metabolites found in root tips originated from the fungus. RNA-sequencing also showed that the plant roots undergo complementary transcriptomic reprogramming in response to the fungal stimuli. Taken together, our results demonstrate that the early metabolite responses of plant roots are partially selective toward the lifestyle of the interacting microbe, and that these responses can be crucial in determining the outcome of the interaction.

PAIRUP-MS: Pathway analysis and imputation to relate unknowns in profiles from mass spectrometry-based metabolite data.

Metabolomics is a powerful approach for discovering biomarkers and for characterizing the biochemical consequences of genetic variation. While untargeted metabolite profiling can measure thousands of signals in a single experiment, many biologically meaningful signals cannot be readily identified as known metabolites nor compared across datasets, making it difficult to infer biology and to conduct well-powered meta-analyses across studies. To overcome these challenges, we developed a suite of computational methods, PAIRUP-MS, to match metabolite signals across mass spectrometry-based profiling datasets and to generate metabolic pathway annotations for these signals. To pair up signals measured in different datasets, where retention times (RT) are often not comparable or even available, we implemented an imputation-based approach that only requires mass-to-charge ratios (m/z). As validation, we treated each shared known metabolite as an unmatched signal and showed that PAIRUP-MS correctly matched 70–88% of these metabolites from among thousands of signals, equaling or outperforming a standard m/z- and RT-based approach. We performed further validation using genetic data: the most stringent set of matched signals and shared knowns showed comparable consistency of genetic associations across datasets. Next, we developed a pathway reconstitution method to annotate unknown signals using curated metabolic pathways containing known metabolites. We performed genetic validation for the generated annotations, showing that annotated signals associated with gene variants were more likely to be enriched for pathways functionally related to the genes compared to random expectation. Finally, we applied PAIRUP-MS to study associations between metabolites and genetic variants or body mass index (BMI) across multiple datasets, identifying up to ~6 times more significant signals and many more BMI-associated pathways compared to the standard practice of only analyzing known metabolites. These results demonstrate that PAIRUP-MS enables analysis of unknown signals in a robust, biologically meaningful manner and provides a path to more comprehensive, well-powered studies of untargeted metabolomics data.

Plant Metabolomics Using NMR Spectroscopy.

The major goal in plant metabolomics is to studycomplex extracts for the purposes of metabolic exploration and natural products discovery. To achieve this goal, plant metabolomics relies on accurate and selective acquisition of all possible chemical information, which includes maximization of the number of detected metabolites and their correct molecular assignment. Nuclear magnetic resonance (NMR) spectroscopy has been recognized as a powerful platform for obtaining the metabolite profiles of plant extracts. In this chapter, we provide a workflow for targeted and untargeted metabolite profiling of plant extracts using both 1D and 2D NMR methods. The protocol includes sample preparation, instrument operation, data processing, multivariate analysis, biomarker elucidation, and metabolite quantitation. It also addresses the annotation of plant metabolite peaks considering NMR's capabilities to cover a broad range of metabolites and elucidate structures for unknown compounds.

Optimization of Metabolite Extraction Protocols for Untargeted Metabolite Profiling of Mycoparasitic Scytalidium parasiticum using LC-TOF-MS

Basal stem rot disease of oil palm caused by Ganoderma boninense is one of the most devastating diseases in oil palm plantation resulting in low yield, loss of palm stands and shorter replanting cycle. To-date, there is no effective treatment for Ganoderma infected palms. Control measures, either chemical or cultural approaches, show varying degrees of effectiveness. The application of biological control agents which is environmental-friendly could be an attractive solution to overcome the problem. Earlier, we had isolated a mycoparasite, Scytalidium parasiticum, from the basidiomata of Ganoderma boninense. In vitro assay and nursery experiment showed that this fungus could suppress Ganoderma infection and reduce disease severity. However, metabolites which might contribute to the antagonistic or mycoparasitic effect remain unknown. In the current study, optimization of fungal sample processing, extraction, and analytical procedures were conducted to obtain metabolites from the maize substrate colonized by mycoparasitic ascomycetous Scytalidium parasiticum. This technique capable of producing sexual spores in sac-like organs. Untargeted metabolomics profiling was carried out by using Liquid Chromatography Time of Flight Mass Spectrometry (LC-ToF-MS). We found that S. parasiticum in both liquid- and solid-state cultivation gave higher metabolite when extracted with 60% methanol with 1% formic acid in combination with homogenisation methods such as ultrasonication and grinding. The findings from this study are useful for optimisation of metabolite extraction from other fungi-Ganoderma-plant interactions.

Metabolism and Biodegradation of Spacecraft Cleaning Reagents by Strains of Spacecraft-Associated Acinetobacter.

Spacecraft assembly facilities are oligotrophic and low-humidity environments, which are routinely cleaned using alcohol wipes for benchtops and spacecraft materials, and alkaline detergents for floors. Despite these cleaning protocols, spacecraft assembly facilities possess a persistent, diverse, dynamic, and low abundant core microbiome, where the Acinetobacter are among the dominant members of the community. In this report, we show that several spacecraft-associated Acinetobacter metabolize or biodegrade the spacecraft cleaning reagents of ethanol (ethyl alcohol), 2-propanol (isopropyl alcohol), and Kleenol 30 (floor detergent) under ultraminimal conditions. Using cultivation and stable isotope labeling studies, we show that ethanol is a sole carbon source when cultivating in 0.2 × M9 minimal medium containing 26 μM Fe(NH4)2(SO4)2. Although cultures expectedly did not grow solely on 2-propanol, cultivations on mixtures of ethanol and 2-propanol exhibited enhanced plate counts at mole ratios of ≤0.50. In support, enzymology experiments on cellular extracts were consistent with oxidation of ethanol and 2-propanol by a membrane-bound alcohol dehydrogenase. In the presence of Kleenol 30, untargeted metabolite profiling on ultraminimal cultures of Acinetobacter radioresistens 50v1 indicated (1) biodegradation of Kleenol 30 into products including ethylene glycols, (2) the potential metabolism of decanoate (formed during incubation of Kleenol 30 in 0.2 × M9), and (3) decreases in the abundances of several hydroxy- and ketoacids in the extracellular metabolome. In ultraminimal medium (when using ethanol as a sole carbon source), A. radioresistens 50v1 also exhibits a remarkable survival against hydrogen peroxide (∼1.5-log loss, ∼108 colony forming units (cfu)/mL, 10 mM H2O2), indicating a considerable tolerance toward oxidative stress under nutrient-restricted conditions. Together, these results suggest that the spacecraft cleaning reagents may (1) serve as nutrient sources under oligotrophic conditions and (2) sustain extremotolerances against the oxidative stresses associated with low-humidity environments. In perspective, this study provides a plausible biochemical rationale to the observed microbial ecology dynamics of spacecraft-associated environments.

Cluster Analysis of Untargeted Metabolomic Experiments.

Untargeted metabolite profiling based upon LC-MS methodology can be used to identify unique metabolic phenotypes associated with stress, disease or environmental exposure of cells using mathematical clustering. Here, we show how unsupervised data analysis is a powerful tool for both quality control and answering simple biological questions. We will demonstrate how to format untargeted mass spectrometry data for import into R, a programming language and software environment for statistical computing (R Development Core Team. R: A language and environment for statistical computing, reference index version 2.15. R Foundation for Statistical Computing, Vienna, 2012). Using R, we transform untargeted metabolite data using hierarchical clustering and principal component analysis (PCA) to create visual representations of change between biological samples and explore how these can be used predictively, in determining environmental stress, health and metabolic insight.

A metabolome-based core hybridisation strategy for the prediction of rice grain weight across environments.

SummaryMarker‐based prediction holds great promise for improving current plant and animal breeding efficiencies. However, the predictabilities of complex traits are always severely affected by negative factors, including distant relatedness, environmental discrepancies, unknown population structures, and indeterminate numbers of predictive variables. In this study, we utilised two independent F1 hybrid populations in the years 2012 and 2015 to predict rice thousand grain weight (TGW) using parental untargeted metabolite profiles with a partial least squares regression method. A stable predictive model for TGW was built based on hybrids from the population in 2012 (r = 0.75) but failed to properly predict TGW for hybrids from the population in 2015 (r = 0.27). After integrating hybrids from both populations into the training set, the TGW of hybrids could be predicted but was largely dependent on population structures. Then, core hybrids from each population were determined by principal component analysis and the TGW of hybrids in both environments were successfully predicted (r > 0.60). Moreover, adjusting the population structures and numbers of predictive analytes increased TGW predictability for hybrids in 2015 (r = 0.72). Our study demonstrates that the TGW of F1 hybrids across environments can be accurately predicted based on parental untargeted metabolite profiles with a core hybridisation strategy in rice. Metabolic biomarkers identified from early developmental stage tissues, which are grown under experimental conditions, may represent a workable approach towards the robust prediction of major agronomic traits for climate‐adaptive varieties.

Rice Grain Quality Benchmarking Through Profiling of Volatiles and Metabolites in Grains Using Gas Chromatography Mass Spectrometry.

Gas chromatograph coupled with mass spectrometer is widely used to profile volatiles and metabolites from the homogenized rice flour obtained from mature grains. Rice grains consist of central endosperm which stores majorly starch and, in addition, accumulate various storage proteins as storage reserves. The outer nutritious aleurone layer stores lipids, sugar alcohols, volatiles, antioxidants, vitamins, and various micronutrients. Once paddy sample is dehulled, milled, and ground cryogenically, the brown rice flour is subjected to extraction of primary metabolites and volatiles using an appropriate extraction method. In metabolite profiling of the liquid extract obtained from the rice sample, mixture is initially subjected to methoxyamination then silylation before being subjected to untargeted metabolite profiling. Peaks obtained are processed for noise reduction and specific signal selection. Volatile compounds are initially extracted using a solid phase adsorbent prior to analysis. All these compounds, metabolites, and volatiles are detected in the mass selective detector by fragmentation at 70eV ionization energy and the resultant mass spectrum compared with a built-in library of compounds. Data mined from the gas chromatography mass spectrometry analysis are then subjected to post-processing statistical analysis.

Untargeted Metabolite Profiling of Cerebrospinal Fluid Uncovers Biomarkers for Severity of Late Infantile Neuronal Ceroid Lipofuscinosis (CLN2, Batten Disease)

Late infantile neuronal ceroid lipofuscinosis (CLN2 disease) is a rare lysosomal storage disorder caused by a monogenetic deficiency of tripeptidyl peptidase-1 (TPP1). Despite knowledge that lipofuscin is the hallmark disease product, the relevant TPP1 substrate and its role in neuronal physiology/pathology is unknown. We hypothesized that untargeted metabolite profiling of cerebrospinal fluid (CSF) could be used as an effective tool to identify disease-associated metabolic disruptions in CLN2 disease, offering the potential to identify biomarkers that inform on disease severity and progression. Accordingly, a mass spectrometry-based untargeted metabolite profiling approach was employed to differentiate CSF from normal vs. CLN2 deficient individuals. Of 1,433 metabolite features surveyed, 29 linearly correlated with currently employed disease severity scores. With tandem mass spectrometry 8 distinct metabolite identities were structurally confirmed based on retention time and fragmentation pattern matches, vs. standards. These putative CLN2 biomarkers include 7 acetylated species – all attenuated in CLN2 compared to controls. Because acetate is the major bioenergetic fuel for support of mitochondrial respiration, deficient acetylated species in CSF suggests a brain energy defect that may drive neurodegeneration. Targeted analysis of these metabolites in CSF of CLN2 patients offers a powerful new approach for monitoring CLN2 disease progression and response to therapy.

An Untargeted Metabolomics Approach to Investigate the Metabolic Modulations of HepG2 Cells Exposed to Low Doses of Bisphenol A and 17β-Estradiol.

The model xeno-estrogen bisphenol A (BPA) has been extensively studied over the past two decades, contributing to major advances in the field of endocrine disrupting chemicals research. Besides its well documented adverse effects on reproduction and development observed in rodents, latest studies strongly suggest that BPA disrupts several endogenous metabolic pathways, with suspected steatogenic and obesogenic effects. BPA's adverse effects on reproduction are attributed to its ability to activate estrogen receptors (ERs), but its effects on metabolism and its mechanism(s) of action at low doses are so far only marginally understood. Metabolomics based approaches are increasingly used in toxicology to investigate the biological changes induced by model toxicants and chemical mixtures, to identify markers of toxicity and biological effects. In this study, we used proton nuclear magnetic resonance (1H-NMR) based untargeted metabolite profiling, followed by multivariate statistics and computational analysis of metabolic networks to examine the metabolic modulation induced in human hepatic cells (HepG2) by an exposure to low and very low doses of BPA (10−6M, 10−9M, and 10−12M), vs. the female reference hormone 17β-estradiol (E2, 10−9M, 10−12M, and 10−15M). Metabolomic analysis combined to metabolic network reconstruction highlighted different mechanisms at lower doses of exposure. At the highest dose, our results evidence that BPA shares with E2 the capability to modulate several major metabolic routes that ensure cellular functions and detoxification processes, although the effects of the model xeno-estrogen and of the natural hormone can still be distinguished.