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Understanding the impact of long-term physiological and environmental stress on the human microbiota and metabolome may be important for the success of space flight. This work is logistically difficult and has a limited number of available participants. Terrestrial analogies present important opportunities to understand changes in the microbiota and metabolome and how this may impact participant health and fitness. Here, we present work from one such analogy: the Transarctic Winter Traverse expedition, which we believe is the first assessment of the microbiota and metabolome from different bodily locations during prolonged environmental and physiological stress. Bacterial load and diversity were significantly higher during the expedition when compared with baseline levels (p < 0.001) in saliva but not stool, and only a single operational taxonomic unit assigned to the Ruminococcaceae family shows significantly altered levels in stool (p < 0.001). Metabolite fingerprints show the maintenance of individual differences across saliva, stool, and plasma samples when analysed using flow infusion electrospray mass spectrometry and Fourier transform infrared spectroscopy. Significant activity-associated changes in terms of both bacterial diversity and load are seen in saliva but not in stool, and participant differences in metabolite fingerprints persist across all three sample types.

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Ginseng, a kind of functional food and medicine with high nutritional value, contains various pharmacological metabolites that influence human metabolic functions. Therefore, it is very important to analyze the composition and metabolites of ginseng. However, the analysis of active metabolites in ginseng samples usually involves various experimental steps, such as extraction, chromatographic separation, and characterization, which may be time-consuming and laborious. In this study, an internal extractive electrospray ionization mass spectrometry (iEESI-MS) method was developed to analyze active metabolites in ginseng samples with sequential sampling and no pretreatment. A total of 44 metabolites, with 32 ginsenosides, 6 sugars, and 6 organic acids, were identified in the ginseng samples. The orthogonal partial least-squares discriminant analysis (OPLS-DA) score plot showed a clear separation of ginseng samples from different origins, indicating that metabolic changes occurred under different growing conditions. This study demonstrated that different cultivation conditions of ginseng can be successfully discriminated when using iEESI-MS-based metabolite fingerprints, which provide an alternative solution for the quality identification of plant drugs.

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Over the past decade, the number of fully sequenced genomes has increased at an awe-inspiring pace. Similarly, the quality and scope of tools for the prediction of both protein structure and function has seen vast improvements. However, to pinpoint the exact function of a protein, for instance the exact reaction catalyzed by an enzyme, experimental evidence is crucial. At the same time, this step is the main bottleneck when generating a conclusive model for the function of an enzyme and to interpret its function in a physiological context. Hence, a comprehensive experimental strategy for functional annotation of enzymes that is as efficient as possible is required. Ex vivo metabolomics is a powerful non-targeted approach that overcomes several of the challenges inherent to in vitro characterization of enzymes with unknown functions. By incubating the recombinant enzyme of interest in a quasi-native metabolite extract from its tissue of origin under specific environmental and developmental conditions, the complete native substrate range can be tested in a single assay. This unlocks compounds that are commercially unavailable or otherwise difficult to procure. Coupled with non-targeted metabolomics analysis, ex vivo has the capability to test for and identify even unexpected substrates and assign the respective products of the enzymatic reaction.

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Non-targeted metabolome approaches aim to detect metabolite markers related to stress, disease, developmental or genetic perturbation. In the later context, it is also a powerful means for functional gene annotation. A prerequisite for non-targeted metabolome analyses are methods for comprehensive metabolite extraction. We present three extraction protocols for a highly efficient extraction of metabolites from plant material with a very broad metabolite coverage. The presented metabolite fingerprinting workflow is based on liquid chromatography high resolution accurate mass spectrometry (LC-HRAM-MS), which provides suitable separation of the complex sample matrix for the analysis of compounds of different polarity by positive and negative electrospray ionization and mass spectrometry. The resulting data sets are then analyzed with the software suite MarVis and the web-based interface MetaboAnalyst. MarVis offers a straightforward workflow for statistical analysis, data merging as well as visualization of multivariate data, while MetaboAnalyst is used in our hands as complementary software for statistics, correlation networks and figure generation. Finally, MarVis provides access to species-specific metabolite and pathway data bases like KEGG and BioCyc and to custom data bases tailored by the user to connect the identified markers or features with metabolites. In addition, identified marker candidates can be interactively visualized and inspected in metabolic pathway maps by KEGG pathways for a more detailed functional annotation and confirmed by mass spectrometry fragmentation experiments or coelution with authentic standards. Together this workflow is a valuable toolbox to identify novel metabolites, metabolic steps or regulatory principles and pathways.

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Momordica charantia is a medicinal plant which is widely used in different traditional medicinal systems to treat several diseases. We have identified the differential distribution of phytomedicinally important metabolites in the pericarp, skin and seeds of M. charantia fruit via NMR spectroscopy. Multivariate statistical analysis showed a clustering of the metabolic profiles of seeds and pericarp, and their clear separation from the metabolic profile of the skin. The total phenolic and flavonoid content of the fruit extracts were estimated via bioassays, the radical scavenging activity was estimated via in vitro DPPH and ABTS assays and an inhibitory activity test of α-glucosidase was also performed. The pericarp and seeds contained significant amounts of phenolic compounds and flavonoids, indicating that they are a good source for antioxidants. The skin contained a significantly higher amount of phytosterols such as Charantin and momordicine, which are known to correlate with antidiabetic activity.

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Plant oxylipins are a class of lipid-derived signaling molecules being involved in the regulation of various biotic and abiotic stress responses. A major class of oxylipins are the circular derivatives to which 12-oxo-phytodienoic acid (OPDA) and its metabolite jasmonic acid (JA) belong. While OPDA and its shorter chain homologue dinor-OPDA (dnOPDA) seem to be ubiquitously found in land plants ranging from bryophytes to angiosperms, the occurrence of JA and its derivatives is still under discussion. The bryophyte Physcomitrium patens has received increased scientific interest as a non-vascular plant model organism over the last decade. Therefore, we followed the metabolism upon wounding by metabolite fingerprinting with the aim to identify jasmonates as well as novel oxylipins in P. patens. A non-targeted metabolomics approach was used to reconstruct the metabolic pathways for the synthesis of oxylipins, derived from roughanic, linoleic, α-linolenic, and arachidonic acid in wild type, the oxylipin-deficient mutants of Ppaos1 and Ppaos2, the mutants of Ppdes being deficient in all fatty acids harboring a Δ6-double bond and the C20-fatty acid-deficient mutants of Ppelo. Beside of OPDA, iso-OPDA, dnOPDA, and iso-dnOPDA, three additional C18-compounds and a metabolite being isobaric to JA were identified to accumulate after wounding. These findings can now serve as foundation for future research in determining, which compound(s) will serve as native ligand(s) for the oxylipin-receptor COI1 in P. patens.

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Green microalgae can accumulate neutral lipids, as part of a general lipid remodeling mechanism under stress such as nitrogen starvation. Lobosphaera incisa is of special interest because of its unique TAG acyl chain composition, especially 20:4 (n-6) can reach up to 21% of dry weight after nitrogen starvation. In order to identify factors that may influence the accumulation of polyunsaturated fatty acids (PUFAs), we identified recently a linoleate 13-lipoxygenase (LiLOX). It shares highest identity with plastidic enzymes from vascular plants and is induced upon nitrogen starvation. Here, we confirmed the localization of LiLOX in the stroma of plastids via transient expression in epithelial onion cells. In order to further characterize this enzyme, we focused on the identification of the endogenous substrate of LiLOX. In this regard, an ex vivo enzymatic assay, coupled with non-targeted analysis via mass spectrometry allowed the identification of MGDG, DGDG and PC as three substrate candidates, later confirmed via in vitro assays. Further investigation revealed that LiLOX has preferences towards the lipid class MGDG, which seems in agreement with its localization in the galactolipid rich plastid. Altogether, this study shows the first characterization of plastidic LOX from green algae, showing preference for MGDGs. However, lipidomics analysis did neither reveal an endogenous LiLOX product nor the final end product of MGDG oxidation. Nevertheless, the latter is a key to understanding the role of this enzyme and since its expression is highest during the degradation of the plastidic membrane, it is tempting to assume its involvement in this process.

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Curcuma longa, Curcuma xanthorrhiza, and Curcuma manga have been widely used for herbal or traditional medicine purposes. It was reported that turmeric plants provided several biological activities such as antioxidant, anti-inflammatory, hepatoprotector, cardioprotector, and anticancer activities. Authentication of the Curcuma species is important to ensure its authenticity and to avoid adulteration practices. Plants from different origins will have different metabolite compositions because metabolites are affected by soil nutrition, climate, temperature, and humidity. 1H-NMR spectroscopy, principal component analysis (PCA), and orthogonal projections to latent structures-discriminant analysis (OPLS-DA) were used for authentication of C. longa, C. xanthorrhiza, and C. manga from seven different origins in Indonesia. From the 1H-NMR analysis it was obtained that 14 metabolites were responsible for generating classification model such as curcumin, demethoxycurcumin, alanine, methionine, threonine, lysine, alpha-glucose, beta-glucose, sucrose, alpha-fructose, beta-fructose, fumaric acid, tyrosine, and formate. Both PCA and OPLS-DA model demonstrated goodness of fit (R2 value more than 0.8) and good predictivity (Q2 value more than 0.45). All OPLS-DA models were validated by assessing the permutation test results with high value of original R2 and Q2. It can be concluded that metabolite fingerprinting using 1H-NMR spectroscopy and chemometrics provide a powerful tool for authentication of herbal and medicinal plants.

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Viscum album L., commonly known as European mistletoe, is a hemi-parasitic plant of the Santalaceae family. The in vitro and in vivo effects of V. album differ, according to its host tree. However, little is known about the host-dependent phytochemical diversity in V. album. In this study, the metabolic profiles of V. album ssp. album from Malus domestica Bork., Quercus robur L., and Ulmus carpinifolia Gled were compared. Leaves, stems, and berries were collected in Switzerland, by the same procedure, in September 2016 and 2017. The methanolic extracts were analyzed by ultra-performance liquid chromatography, coupled to electrospray quadrupole time-of-flight mass spectrometry in positive ionization mode. The data were submitted to partial-least square discriminant analysis (PLS-DA) and the results showed that the V. album ssp. album samples were clustered into three groups, according to the three distinct host trees. Seven compounds, with high VIP scores (variable importance in projection), were responsible for this differentiation. The following four compounds were detected in both the harvest years: arginine, pipecolic acid or lysine, dimethoxycoumarin, and sinapyl alcohol, suggesting their use as host specific V. album biomarkers. The present work highlights the importance of standardized harvest and analytical procedures for the reproducibility of the chemical results of herbal materials.

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More and more evidence has proved that urinary metabolites can instantly reflect disease state. Therefore, ultra-sensitive and reproducible detection of urinary metabolites in a high-throughput way is urgently desirable for clinical diagnosis. Matrix-free laser desorption/ionization mass spectrometry (LDI-MS) is a high-throughput platform for metabolites detection, but it is encountered by severe interference from numerous salts in urine samples, because the crystallized urine salt on dried samples could result in poor reproducibility in LDI-MS detection. The present work proposed a tip-contact extraction (TCE) technique to eliminate interference from the urine salt. Vertical silicon nanowire arrays decorated with the fluorinated ethylene propylene film (FEP@VSiNWs) could effectively extract metabolites from the urine sample dropping on its surface. High salt tolerance was observed in the subsequent LDI-MS detection of the metabolites extracted on the tip of FEP@VSiNWs even in the presence of 1 M urea. Stable and reproducible mass spectra for non-target metabolic analysis were obtained in real urine samples with different dilution folds. Urinary metabolites collected from bladder cancer (BC) patients were reliably profiled by the TCE method coupled with negative LDI-MS. Based on this platform, potential metabolic biomarkers that can distinguish BC patients and normal controls were uncovered.

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Metabolomics-the endpoint of the omics cascade-is increasingly recognized as a preferred method for understanding the ultimate responses of biological systems to stress. Flow injection electrospray (FIE) mass spectrometry (MS) has advantages for untargeted metabolic fingerprinting due to its simplicity and capability for high-throughput screening but requires a high-resolution mass spectrometer to resolve metabolite features. In this study, we developed and validated a high-throughput and highly reproducible metabolomics platform integrating FIE with ultrahigh-resolution Fourier transform ion cyclotron resonance (FTICR) MS for analysis of both polar and nonpolar metabolite features from plasma samples. FIE-FTICR MS enables high-throughput detection of hundreds of metabolite features in a single mass spectrum without a front-end separation step. Using plasma samples from genetically identical obese mice with or without type 2 diabetes (T2D), we validated the intra and intersample reproducibility of our method and its robustness for simultaneously detecting alterations in both polar and nonpolar metabolite features. Only 5 min is needed to acquire an ultra-high resolution mass spectrum in either a positive or negative ionization mode. Approximately 1000 metabolic features were reproducibly detected and annotated in each mouse plasma group. For significantly altered and highly abundant metabolite features, targeted tandem MS (MS/MS) analyses can be applied to confirm their identity. With this integrated platform, we successfully detected over 300 statistically significant metabolic features in T2D mouse plasma as compared to controls and identified new T2D biomarker candidates. This FIE-FTICR MS-based method is of high throughput and highly reproducible with great promise for metabolomics studies toward a better understanding and diagnosis of human diseases.

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Liquid chromatography-mass spectrometry (LC-MS)-based nontargeted metabolome approaches aim to detect chemotypes as markers for stress, disease, developmental, or genetic perturbation. Herein, we present a metabolite fingerprinting workflow, which is applicable for the analysis of tissues and fluids derived from plants and fungi. This is based on a broad metabolite coverage by a two-phase extraction and the separate analysis of polar, and nonpolar compounds by positive as well as negative electrospray ionization. For analysis of the resulting comprehensive data sets, the interactive and user-friendly data mining software MarVis-Suite is used. It supports statistical analysis, adduct correction, data merging, as well as visualization of multivariate data. Finally, MarVis shapes marker identification to the organism of interest. Therefore, it provides access to the species-specific databases KEGG and BioCyc and to custom databases tailored by the user.

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Copper (Cu) is a heavy metal that is widely used in industry and as such wastewater from mining or industrial operations can contain high levels of Cu. Some aquatic algal species can tolerate and bioaccumulate Cu and so could play a key role in bioremediating and recovering Cu from polluted waterways. One such species is the green alga Desmodesmus sp. AARLG074. The aim of this study was to determine how Desmodesmus is able to tolerate large alterations in its external Cu and pH environment. Specifically, we set out to measure the variations in the Cu removal efficiency, growth, ultrastructure, and cellular metabolite content in the algal cells that are associated with Cu exposure and acidity. The results showed that Desmodesmus could remove up to 80% of the copper presented in Jaworski's medium after 30 min exposure. There was a decrease in the ability of Cu removal at pH 4 compared to pH 6 indicating both pH and Cu concentration affected the efficiency of Cu removal. Furthermore, Cu had an adverse effect on algal growth and caused ultrastructural changes. Metabolite fingerprinting (FT-IR and GC-MS) revealed that the polysaccharide and amino acid content were the main metabolites affected under acid and Cu exposure. Fructose, lactose and sorbose contents significantly decreased under both acidic and Cu conditions, whilst glycerol and melezitose contents significantly increased at pH 4. The pathway analysis showed that pH had the highest impact score on alanine, aspartate and glutamate metabolism whereas Cu had the highest impact on arginine and proline metabolism. Notably both Cu and pH had impact on glutathione and galactose metabolism.

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INTRODUCTION: Comparative analysis of metabolic features of plants has a high potential for determination of quality control of active ingredients, ecological or chemotaxonomic purposes. Specifically, the development of efficient and rapid analytical tools that allow the differentiation among species, subspecies and varieties of plants is a relevant issue. Here we describe a multivariate model based on LC-MS/MS fingerprinting capable of discriminating between subspecies and varieties of the medicinal plant Chamaecrista nictitans, a rare distributed species in Costa Rica. METHODS: Determination of the chemical fingerprint was carried out on a LC-MS (ESI-QTOF) in negative ionization mode, main detected and putatively identified compounds included proanthocyanidin oligomers, several flavonoid C- and O-glycosides, and flavonoid acetates. Principal component analysis (PCA), partial least square-discriminant analysis (PLS-DA) and cluster analysis of chemical profiles were performed. RESULTS: Our method showed a clear discrimination between the subspecies and varieties of Chamaecrista nictitans, separating the samples into four fair differentiated groups: M1 = C. nictitans ssp. patellaria; M2 = C. nictitans ssp. disadena; M3 = C. nictitans ssp. nictitans var. jaliscensis and M4 = C. nictitans ssp. disadena var. pilosa. LC-MS/MS fingerprint data was validated using both morphological characters and DNA barcoding with ITS2 region. The comparison of the morphological characters against the chemical profiles and DNA barcoding shows a 63% coincidence, evidencing the morphological similarity in C. nictitans. On the other hand, genetic data and chemical profiles grouped all samples in a similar pattern, validating the functionality of our metabolomic approach. CONCLUSION: The metabolomic method described in this study allows a reliably differentiation between subspecies and varieties of C. nictitans using a straightforward protocol that lacks extensive purification steps.

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The slow pace of discovery of new effective drugs against multi-drug resistant pathogens and largely unsuccessful combinatorial chemistry has resulted in shifting the focus back to natural products as sources of lead molecules for antimicrobial drugs, mainly due to their structural diversity. Investigation of under-explored habitats for potentially novel microorganisms provides for wider chemodiversity. In this study, four actinomycetes, namely UK-274, UK-281, UK-282 and UK-285, which showed broad-spectrum antibacterial and antifungal activities, were isolated from Timli forest range of the biodiversity-rich Himalayan region. 16S rRNA gene sequence analysis showed that the nearest neighbours of the isolates were Actinomadura nitrigenes, Streptomyces niveiscabiei, and Kitasatospora psammotica with similarity values ranging between 97 and 98% suggesting their potential as new isolates. Further morphological and phenotypic characterization strengthened this assumption. Isolate UK-282, of the rare actinomycetes Kitasatospora group, was found to produce antimicrobial activity. Metabolite fingerprinting of ethyl acetate fraction of isolate UK-282 by GC-MS and 1H NMR analysis showed the presence of three novel compounds. The study underlines that a combination approach of bioprospecting of under-studied habitats and focus on rare actinomycetes may result in the identification of novel chemodiversity.

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Dispersal is one of the strategies for organisms to deal with climate change and habitat degradation. Therefore, investigating the effects of dispersal evolution on natural populations is of considerable interest to ecologists and conservation biologists. Although it is known that dispersal itself can evolve due to selection, the behavioral, life-history and metabolic consequences of dispersal evolution are not well understood. Here, we explore these issues by subjecting four outbred laboratory populations of Drosophila melanogaster to selection for increased dispersal. The dispersal-selected populations had similar values of body size, fecundity, and longevity as the nonselected lines (controls), but evolved significantly greater locomotor activity, exploratory tendency, and aggression. Untargeted metabolomic fingerprinting through NMR spectroscopy suggested that the selected flies evolved elevated cellular respiration characterized by greater amounts of glucose, AMP, and NAD. Concurrent evolution of higher level of Octopamine and other neurotransmitters indicate a possible mechanism for the behavioral changes in the selected lines. We discuss the generalizability of our findings in the context of observations from natural populations. To the best of our knowledge, this is the first report of the evolution of metabolome due to selection for dispersal and its connection to dispersal syndrome evolution.

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Abstract Plants are a very rich source of pharmacologically relevant metabolites. However, the relative concentrations of these compounds are subject to the genetic make-up, the physiological state of the plant as well as environmental effects. Recently, metabolic perturbations through the use of abiotic stressors have proven to be a valuable strategy for increasing the levels of these compounds. Oxidative stress-associated stressors, including ionizing radiation, have also been reported to induce metabolites with various biological activities in plants. Hence, the aim of the current study was to investigate the effect of gamma radiation on the induction of purported anti-cancerous metabolites, glucomoringin and its derivatives, in Moringa oleifera Lam., Moringaceae. Here, an UHPLC-qTOF-MS-based targeted metabolic fingerprinting approach was used to evaluate the effect of gamma radiation treatment on the afore-mentioned health-beneficial secondary metabolites of M. oleifera. Following radiation, an increase in glucomoringin and three acylated derivatives was noted. As such, these molecules can be regarded as components of the inducible defense mechanism of M. oleifera as opposed to being constitutive components as it has previously been assumed. This might be an indication of a possible, yet unexplored role of moringin against the effects of oxidative stress in M. oleifera plants. The results also suggest that plants undergoing photo-oxidative stress could accumulate higher amounts of glucomoringin and related molecules.

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Background and Aims: Morphological traits in combination with metabolite fingerprinting were used to investigate inter- and intraspecies diversity within the model annual grasses Brachypodium distachyon, Brachypodium stacei and Brachypodium hybridum . Methods: Phenotypic variation of 15 morphological characters and 2219 nominal mass ( m / z ) signals generated using flow infusion electrospray ionization-mass spectrometry (FIE-MS) were evaluated in individuals from a total of 174 wild populations and six inbred lines, and 12 lines, of the three species, respectively. Basic statistics and multivariate principal component analysis and discriminant analysis were used to differentiate inter- and intraspecific variability of the two types of variable, and their association was assayed with the rcorr function. Key Results: Basic statistics and analysis of variance detected eight phenotypic characters [(stomata) leaf guard cell length, pollen grain length, (plant) height, second leaf width, inflorescence length, number of spikelets per inflorescence, lemma length, awn length] and 434 tentatively annotated metabolite signals that significantly discriminated the three species. Three phenotypic traits (pollen grain length, spikelet length, number of flowers per inflorescence) might be genetically fixed. The three species showed different metabolomic profiles. Discriminant analysis significantly discriminated the three taxa with both morphometric and metabolome traits and the intraspecific phenotypic diversity within B. distachyon and B. stacei . The populations of B. hybridum were considerably less differentiated. Conclusions: Highly explanatory metabolite signals together with morphological characters revealed concordant patterns of differentiation of the three taxa. Intraspecific phenotypic diversity was observed between northern and southern Iberian populations of B. distachyon and between eastern Mediterranean/south-western Asian and western Mediterranean populations of B. stacei . Significant association was found for pollen grain length and lemma length and ten and six metabolomic signals, respectively. These results would guide the selection of new germplasm lines of the three model grasses in ongoing genome-wide association studies.

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The present study was aimed at the development of a new analytical method for the comprehensive multi-component analysis of polyphenols in Punica granatum L. (pomegranate) juice and peel. While pomegranate juice was directly analysed after simple centrifugation, different extraction techniques, including maceration, heat reflux extraction, ultrasound-assisted extraction and microwave-assisted extraction, were compared in order to obtain a high yield of the target analytes from pomegranate peel. Dynamic maceration with a mixture of water and ethanol 80:20 (v/v) with 0.1% of hydrochloric acid as the extraction solvent provided the best result in terms of recovery of pomegranate secondary metabolites. The quali- and quantitative analysis of pomegranate polyphenols was performed by high-performance liquid chromatography with diode array and electrospray ionization-mass spectrometry detection. The application of fused-core column technology allowed us to obtain an improvement of the chromatographic performance in comparison with that of conventional particulate stationary phases, thus enabling a good separation of all constituents in a shorter time and with low solvent usage. The analytical method was completely validated to show compliance with the International Conference on Harmonization of Technical Requirements for the Registration of Pharmaceuticals for Human Use guidelines and successfully applied to the characterisation of commercial and experimental pomegranate samples, thus demonstrating its efficiency as a tool for the fingerprinting of this plant material. The quantitative data collected were submitted to principal component analysis, in order to highlight the possible presence of pomegranate samples with high content of secondary metabolites. From the statistical analysis, four experimental samples showed a notable content of bioactive compounds in the peels, while commercial ones still represent the best source of healthy juice.

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The integration of plant metabolomics to support preharvest fruit development studies can provide important insights into the biochemical mechanisms involved and lately support producers on harvesting management. A metabolomic-based strategy for fingerprinting of volatile terpenoids and norisoprenoids from Sambucus nigra L. berries from three cultivars, through ripening, was established. From 42 monoterpenic, 20 sesquiterpenic, and 14 norisoprenoid compounds, 48 compounds are reported for the first time as S. nigra berries components. Chemometric tools revealed that ripening was the factor that influenced more the volatile fraction profile and physicochemical parameters (pH, TS, and TSS), followed by cultivar. For the unripe stages, a higher overall content of the studied metabolites was observed, which gradually decreased over the ripening stages, being consistent for the three cultivars. These trends were mainly ruled by limonene, p-cymene, aromadendrene, ß-caryophyllene, and dihydroedulan, which might therefore be used by producers as an additional simple decision making tool in conjunction with physicochemical parameters.

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