A Tunisian wild grape leads to metabolic fingerprints of salt tolerance.

Soil salinity is progressively impacting agriculture, including viticulture. Identification of genetic factors rendering grapevine (Vitis vinifera L.) resilience that can be introgressed into commercial varieties is necessary for safeguarding viticulture against the consequences of global climate change. To gain insight into the physiological and metabolic responses enabling salt tolerance, we compared a salt-tolerant accession of Vitis sylvestris from Tunisia, "Tebaba", with "1103 Paulsen" rootstock widely used in the Mediterranean. Salt stress was slowly increased, simulating the situation of an irrigated vineyard. We determined that "Tebaba" does not sequester sodium in the root but can cope with salinity through robust redox homeostasis. This is linked with rechanneling of metabolic pathways toward antioxidants and compatible osmolytes, buffering photosynthesis, such that cell-wall breakdown can be avoided. We propose that salt tolerance of this wild grapevine cannot be attributed to a single genetic factor but emerges from favorable metabolic fluxes that are mutually supportive. We suggest that introgression of "Tebaba" into commercial varieties is preferred over the use of "Tebaba" as a rootstock for improving salt tolerance in grapevine.

Cold tolerance of woodland strawberry (Fragaria vesca) is linked to Cold Box Factor 4 and the dehydrin Xero2.

Domesticated strawberry is susceptible to sudden frost episodes, limiting the productivity of this cash crop in regions, where they are grown during early spring. In contrast, the ancestral woodland strawberry (Fragaria vesca) has successfully colonised many habitats of the Northern Hemisphere. Thus, this species seems to harbour genetic factors promoting cold tolerance. Screening a germplasm established in frame of the German Gene Bank for Crop Wild Relatives we identified, among 70 wild accessions, a pair contrasting with respect to cold tolerance. By following the physiological, biochemical, molecular, and metabolic responses of this contrasting pair, we identified the transcription factor Cold Box Factor 4 and the dehydrin Xero-2 as molecular markers associated with superior tolerance to cold stress. Overexpression of GFP fusions with Xero-2 in tobacco BY-2 cells conferred cold tolerance to these recipient cells. A detailed analysis of the metabolome for the two contrasting genotypes allows to define metabolic signatures correlated with cold tolerance versus cold stress. This work provides a proof-of-concept for the value of crop wild relatives as genetic resources to identify genetic factors suitable to increase the stress resilience of crop plants.

Discovery and Validation of Banana Intake Biomarkers Using Untargeted Metabolomics in Human Intervention and Cross-sectional Studies.

BACKGROUND: Banana is one of the most widely consumed fruits in the world. However, information regarding its health effects is scarce. Biomarkers of banana intake would allow a more accurate assessment of its consumption in nutrition studies. OBJECTIVES: Using an untargeted metabolomics approach, we aimed to identify the banana-derived metabolites present in urine after consumption, including new candidate biomarkers of banana intake. METHODS: A randomized controlled study with a crossover design was performed on 12 healthy subjects (6 men, 6 women, mean ± SD age: 30.0 ± 4.9 y; mean ± SD BMI: 22.5 ± 2.3 kg/m2). Subjects underwent 2 dietary interventions: 1) 250 mL control drink (Fresubin 2 kcal fiber, neutral flavor; Fresenius Kabi), and 2) 240 g banana + 150 mL control drink. Twenty-four-hour urine samples were collected and analyzed with ultra-performance liquid chromatography coupled to a quadrupole time-of-flight MS and 2-dimensional GC-MS. The discovered biomarkers were confirmed in a cross-sectional study [KarMeN (Karlsruhe Metabolomics and Nutrition study)] in which 78 subjects (mean BMI: 22.8; mean age: 47 y) were selected reflecting high intake (126-378 g/d), low intake (47.3-94.5 g/d), and nonconsumption of banana. The confirmed biomarkers were examined singly or in combinations, for established criteria of validation for biomarkers of food intake. RESULTS: We identified 33 potentially bioactive banana metabolites, of which 5 metabolites, methoxyeugenol glucuronide (MEUG-GLUC), dopamine sulfate (DOP-S), salsolinol sulfate, xanthurenic acid, and 6-hydroxy-1-methyl-1,2,3,4-tetrahydro-ß-carboline sulfate, were confirmed as candidate intake biomarkers. We demonstrated that the combination of MEUG-GLUC and DOP-S performed best in predicting banana intake in high (AUCtest = 0.92) and low (AUCtest = 0.87) consumers. The new biomarkers met key criteria establishing their current applicability in nutrition and health research for assessing the occurrence of banana intake. CONCLUSIONS: Our metabolomics study in healthy men and women revealed new putative bioactive metabolites of banana and a combined biomarker of intake. These findings will help to better decipher the health effects of banana in future focused studies. This study was registered at clinicaltrials.gov as NCT03581955 and with the Ethical Committee for the Protection of Human Subjects Sud-Est 6 as CPP AU 1251, IDRCB 2016-A0013-48; the KarMeN study was registered with the German Clinical Trials Register (DRKS00004890). Details about the study can be obtained from https://www.drks.de.

Acute effects of moderate vs. vigorous endurance exercise on urinary metabolites in healthy, young, physically active men-A multi-platform metabolomics approach.

Introduction: Endurance exercise alters whole-body as well as skeletal muscle metabolism and physiology, leading to improvements in performance and health. However, biological mechanisms underlying the body's adaptations to different endurance exercise protocols are not entirely understood. Methods: We applied a multi-platform metabolomics approach to identify urinary metabolites and associated metabolic pathways that distinguish the acute metabolic response to two endurance exercise interventions at distinct intensities. In our randomized crossover study, 16 healthy, young, and physically active men performed 30 min of continuous moderate exercise (CME) and continuous vigorous exercise (CVE). Urine was collected during three post-exercise sampling phases (U01/U02/U03: until 45/105/195 min post-exercise), providing detailed temporal information on the response of the urinary metabolome to CME and CVE. Also, fasting spot urine samples were collected pre-exercise (U00) and on the following day (U04). While untargeted two-dimensional gas chromatography-mass spectrometry (GC×GC-MS) led to the detection of 608 spectral features, 44 metabolites were identified and quantified by targeted nuclear magnetic resonance (NMR) spectroscopy or liquid chromatography-mass spectrometry (LC-MS). Results: 104 urinary metabolites showed at least one significant difference for selected comparisons of sampling time points within or between exercise trials as well as a relevant median fold change >1.5 or <0. 6 ¯ (NMR, LC-MS) or >2.0 or <0.5 (GC×GC-MS), being classified as either exercise-responsive or intensity-dependent. Our findings indicate that CVE induced more profound alterations in the urinary metabolome than CME, especially at U01, returning to baseline within 24 h after U00. Most differences between exercise trials are likely to reflect higher energy requirements during CVE, as demonstrated by greater shifts in metabolites related to glycolysis (e.g., lactate, pyruvate), tricarboxylic acid cycle (e.g., cis-aconitate, malate), purine nucleotide breakdown (e.g., hypoxanthine), and amino acid mobilization (e.g., alanine) or degradation (e.g., 4-hydroxyphenylacetate). Discussion: To conclude, this study provided first evidence of specific urinary metabolites as potential metabolic markers of endurance exercise intensity. Future studies are needed to validate our results and to examine whether acute metabolite changes in urine might also be partly reflective of mechanisms underlying the health- or performance-enhancing effects of endurance exercise, particularly if performed at high intensities.

Long-Term Mootral Application Impacts Methane Production and the Microbial Community in the Rumen Simulation Technique System.

Methane emissions by ruminants contribute to global warming and result in a loss of dietary energy for the animals. One possibility of reducing methane emissions is by dietary strategies. In the present trial, we investigated the long-term effects of Mootral, a feed additive consisting of garlic powder (Allium sativum) and bitter orange extracts (Citrus aurantium), on fermentation parameters and the microbial community in the rumen simulation technique (RUSITEC) system. The experiment lasted 38 days and was divided into three phases: an equilibration period of 7 days, a baseline period (BL) of 3 days, and experimental period (EP) of 28 days. Twelve fermentation vessels were divided into three groups (n = 4): control (CON), short-term (ST), and long-term (LT) application. From day 11 to day 27, 1.7 g of Mootral was added to the ST vessels; LT vessels received 1.7 g of Mootral daily for the entire EP. With the onset of Mootral application, methane production was significantly reduced in both groups until day 18. Thereafter, the production rate returned to the initial quantity. Furthermore, the short chain fatty acid fermentation profile was significantly altered by Mootral application; the molar proportion of acetate decreased, while the proportions of propionate and butyrate increased. Metabolomic analysis revealed further changes in metabolite concentrations associated with the Mootral supplementation period. The methyl coenzyme-M reductase gene copy number was reduced in the liquid and solid phase, whereas the treatment did not affect the abundance of bacteria. At the end of the BL, Methanomicrobia was the most abundant archaeal class. Mootral supplementation induced an increase in the relative abundance of Methanomassiliicoccales and a reduction in the relative abundance of Methanomicrobia, however, this effect was transient. Abundances of bacterial families were only marginally altered by the treatment. In conclusion, Mootral has the transient ability to reduce methane production significantly due to a selective effect on archaea numbers and archaeal community composition with little effect on the bacterial community.

The effect of potassium fertilization on the metabolite profile of tomato fruit (Solanum lycopersicum L.).

The macronutrient potassium (K) has vital physiological functions in plants and its availability can strongly impact quality of crops like tomato. The impact of K nutrition on conventional tomato fruit quality parameters has been described several times, but detailed investigations on the effect of K supply on the fruit metabolite profile are still rare. To fill this gap, we investigated the influence of K fertilization on the metabolite profile of tomato fruits. For this purpose, an outdoor pot experiment with three different cocktail tomato cultivars was performed. A fertilization regimen with five K levels was applied, ranging from deficiency to sufficient supply. Fruit samples were analyzed by untargeted GC×GC-MS to cover the primary metabolite profile as well as some secondary metabolites. As verified using ICP-OES, fruit K content was highly proportional to the supplied amount of K. At the metabolite profile level, the most prominent and cultivar-independent effect of increased K fertilization was the rise of tricarboxylic acid (TCA) cycle intermediates. Further effects were more cultivar-specific, for example an increase of the mobile nitrogen pool (e.g. amines like putrescine and amides like asparagine), changes in the profile of minor sugars (especially disaccharides) as well as higher levels of some secondary metabolites. Pronounced response patterns were mainly observed in the cultivars Primavera and Yellow Submarine that were recently characterized as higher yielding, demanding a stronger consideration of cultivar differences in future studies.

Sexual Dimorphism of Metabolite Profiles in Pigs Depends on the Genetic Background.

The study aimed to investigate possible systematic effects in the basic underlying variability of individual metabolomic data. In this context, the extent of gender- and genotype-dependent differences reflected in the metabolic composition of three tissues in fattening pigs was determined. The 40 pigs belonged to the genotypes PIx(LWxGL) and PIxGL with gilts and boars, respectively. Blood and tissue samples from M. longissimus dorsi and liver were directly taken at the slaughtering plant and directed to GC × GC qMS metabolite analysis. Differences were observed for various metabolite classes like amino acids, fatty acids, sugars, or organic acids. Gender-specific differences were much more pronounced than genotype-related differences, which could be due to the close genetic relation of the fattening pigs. However, the metabolic dimorphism between gilts and boars was found to be genotype-dependent, and vice versa metabolic differences between genotypes were found to be gender-dependent. Most interestingly, integration into metabolic pathways revealed different patterns for carbon (C) and nitrogen (N) usage in boars and gilts. We suppose a stronger N-recycling and increased energy metabolism in boars, whereas, in gilts, more N is presumably excreted and remaining carbon skeletons channeled into lipogenesis. Associations of metabolites to meat quality factors confirmed the applicability of metabolomics approaches for a better understanding about the impact of drivers (e.g., gender, age, breed) on physiological processes influencing meat quality. Due to the huge complexity of the drivers-traits-network, the derivation of independent biomarkers for meat quality prediction will hardly be possible.

Exploring the Diversity of Sugar Compounds in Healthy, Prediabetic, and Diabetic Volunteers.

SCOPE: Diabetes is thought to primarily represent a disturbance of carbohydrate metabolism; however, population studies employing metabolomics have mainly identified plasma amino acids and lipids, or their products, as biomarkers. In this pilot study, the aim is to analyze a wide spectrum of sugar compounds in the fasting state and during an oral glucose tolerance test (OGTT) in healthy, prediabetic, and type 2 diabetic volunteers. METHODS AND RESULTS: The three volunteer groups underwent a standard OGTT. Plasma samples obtained in the fasting state, 30 and 90 min after the OGTT, are subjected to a semitargeted GC-MS (gas chromatography-mass spectrometry) sugar profiling. Overall, 40 sugars are detected in plasma, of which some are yet unknown to change during an OGTT. Several sugars (e.g., trehalose) reveal significant differences between the volunteer groups both in fasting plasma and in distinct time courses after the OGTT. This suggests an endogenous production from orally absorbed glucose and/or an insulin-dependent production/removal from plasma. CONCLUSION: It is demonstrated that more sugars than expected can be found in human plasma. Since some of these show characteristic differences depending on health status, it may be worthwhile to assess their usability as biomarkers for diagnosing early-stage insulin resistance and type 2 diabetes.

Robust Markers of Coffee Consumption Identified Among the Volatile Organic Compounds in Human Urine.

SCOPE: The human volatilome has gained high interest for the discovery of potential biomarkers of diseases. However, knowledge about the diet as a crucial factor affecting the volatilome is scarce. Therefore, the search for disease biomarkers, as well as the potential use of volatiles as dietary markers is so far limited. The aim of this study is to investigate the association of the diet with the urinary volatilome with the special task to find potential markers of coffee consumption in 24 h urine samples from the Karlsruhe Metabolomics and Nutrition (KarMeN) study. METHODS AND RESULTS: Acidified urine samples are analyzed using an approach combining headspace solid phase microextraction (HS-SPME) sampling with untargeted GC×GC-MS. Overall, 138 reliably occurring volatiles are detected. To account for the unequally concentrated urine samples, results of six different commonly used normalization methods are compared. Statistical analysis evidences six potential markers of coffee consumption, the most promising being 3,4-dimethyl-2,5-furandione. A correlation analysis between the 24 h dietary recall data and the urinary volatilome reveals further promising associations. CONCLUSION: The human urinary volatilome is highly affected by the diet, enabling access to a high level of information about potential diet-related biomarkers. Therefore, it is a very promising source for further investigations on dietary markers.

Building blocks for automated elucidation of metabolites: machine learning methods for NMR prediction.

BACKGROUND: Current efforts in Metabolomics, such as the Human Metabolome Project, collect structures of biological metabolites as well as data for their characterisation, such as spectra for identification of substances and measurements of their concentration. Still, only a fraction of existing metabolites and their spectral fingerprints are known. Computer-Assisted Structure Elucidation (CASE) of biological metabolites will be an important tool to leverage this lack of knowledge. Indispensable for CASE are modules to predict spectra for hypothetical structures. This paper evaluates different statistical and machine learning methods to perform predictions of proton NMR spectra based on data from our open database NMRShiftDB. RESULTS: A mean absolute error of 0.18 ppm was achieved for the prediction of proton NMR shifts ranging from 0 to 11 ppm. Random forest, J48 decision tree and support vector machines achieved similar overall errors. HOSE codes being a notably simple method achieved a comparatively good result of 0.17 ppm mean absolute error. CONCLUSION: NMR prediction methods applied in the course of this work delivered precise predictions which can serve as a building block for Computer-Assisted Structure Elucidation for biological metabolites.

The complex human urinary sugar profile: determinants revealed in the cross-sectional KarMeN study.

Background: Although sugars and sugar derivatives are an important class of metabolites involved in many physiologic processes, there is limited knowledge on their occurrence and pattern in biofluids. Objective: Our aim was to obtain a comprehensive urinary sugar profile of healthy participants and to demonstrate the wide applicability and usefulness of this sugar profiling approach for nutritional as well as clinical studies. Design: In the cross-sectional KarMeN study, the 24-h urine samples of 301 healthy participants on an unrestricted diet, assessed via a 24-h recall, were analyzed by a newly developed semitargeted gas chromatography-mass spectrometry (GC-MS) profiling method that enables the detection of known and unknown sugar compounds. Statistical analyses were performed with respect to associations of sex and diet with the urinary sugar profile. Results: In total, 40 known and 15 unknown sugar compounds were detected in human urine, ranging from mono- and disaccharides, polyols, and sugar acids to currently unknown sugar-like compounds. A number of rarely analyzed sugars were found in urine samples. Maltose was found in statistically higher concentrations in the urine of women compared with men and was also associated with menopausal status. Further, a number of individual sugar compounds associated with the consumption of specific foods, such as avocado, or food groups, such as alcoholic beverages and dairy products, were identified. Conclusions: We here provide data on the complex nature of the sugar profile in human urine, of which some compounds may have the potential to serve as dietary markers or early disease biomarkers. Thus, comprehensive urinary sugar profiling not only has the potential to increase our knowledge of host sugar metabolism, but can also reveal new dietary markers after consumption of individual food items, and may lead to the identification of early disease biomarkers in the future. The KarMeN study was registered at drks.de as DRKS00004890.

The influence of a chronic L-carnitine administration on the plasma metabolome of male Fischer 344 rats.

SCOPE: L-carnitine has been advertised as a fat-lowering and performance-enhancing supplement, although scientific evidence for its effectiveness is lacking. The uptake of about 1-2 g of L-carnitine per day may result in the formation of metabolites like trimethylamine-N-oxide (TMAO), which in turn may be converted to potential carcinogens or promote the development of cardiovascular diseases. METHODS AND RESULTS: To assess whether an L-carnitine supplementation changes overall metabolism or causes the formation of previously unknown metabolites, we analyzed plasma samples from Fischer 344 rats originating from a previous study using a multi-platform metabolomics approach comprising LC-MS/MS and GC×GC-MS methods. Despite an intake of up to 352 mg L-carnitine/kg body weight/day for 1 year, plasma concentrations of only 29 out of 359 metabolites were significantly influenced, the induced concentration changes being often comparatively small. Nevertheless, a clear dose-response relationship and a substantial concentration increase were observed for TMAO, i.e. a tenfold higher TMAO level was measured in the high-dose group when compared to the control (2.5 versus 25.0 µM). CONCLUSION: Although L-carnitine supplementation did not cause large changes in the plasma metabolome, a higher risk for cardiovascular disease due to chronically elevated TMAO plasma concentrations cannot be excluded.

Metabolite patterns predicting sex and age in participants of the Karlsruhe Metabolomics and Nutrition (KarMeN) study.

Physiological and functional parameters, such as body composition, or physical fitness are known to differ between men and women and to change with age. The goal of this study was to investigate how sex and age-related physiological conditions are reflected in the metabolome of healthy humans and whether sex and age can be predicted based on the plasma and urine metabolite profiles. In the cross-sectional KarMeN (Karlsruhe Metabolomics and Nutrition) study 301 healthy men and women aged 18-80 years were recruited. Participants were characterized in detail applying standard operating procedures for all measurements including anthropometric, clinical, and functional parameters. Fasting blood and 24 h urine samples were analyzed by targeted and untargeted metabolomics approaches, namely by mass spectrometry coupled to one- or comprehensive two-dimensional gas chromatography or liquid chromatography, and by nuclear magnetic resonance spectroscopy. This yielded in total more than 400 analytes in plasma and over 500 analytes in urine. Predictive modelling was applied on the metabolomics data set using different machine learning algorithms. Based on metabolite profiles from urine and plasma, it was possible to identify metabolite patterns which classify participants according to sex with > 90% accuracy. Plasma metabolites important for the correct classification included creatinine, branched-chain amino acids, and sarcosine. Prediction of age was also possible based on metabolite profiles for men and women, separately. Several metabolites important for this prediction could be identified including choline in plasma and sedoheptulose in urine. For women, classification according to their menopausal status was possible from metabolome data with > 80% accuracy. The metabolite profile of human urine and plasma allows the prediction of sex and age with high accuracy, which means that sex and age are associated with a discriminatory metabolite signature in healthy humans and therefore should always be considered in metabolomics studies.

A peaklet-based generic strategy for the untargeted analysis of comprehensive two-dimensional gas chromatography mass spectrometry data sets.

Comprehensive two-dimensional gas chromatography mass spectrometry (GC×GC-MS) is a well-established key technology in analytical chemistry and increasingly used in the field of untargeted metabolomics. However, automated processing of large GC×GC-MS data sets is still a major bottleneck in untargeted, large-scale metabolomics. For this reason we introduce a novel peaklet-based alignment strategy. The algorithm is capable of an untargeted deterministic alignment exploiting a density based clustering procedure within a time constrained similarity matrix. Exploiting minimal (1)D and (2)D retention time shifts between peak modulations, the alignment is done without the need for peak merging which also eliminates the need for linear or nonlinear retention time correction procedures. The approach is validated in detail using data of urine samples from a large human metabolomics study. The data was acquired by a Shimadzu GCMS-QP2010 Ultra GC×GC-qMS system and consists of 512 runs, including 312 study samples and 178 quality control sample injections, measured within a time period of 22 days. The final result table consisted of 313 analytes, each of these being detectable in at least 75% of the study samples. In summary, we present an automated, reliable and fully transparent workflow for the analysis of large GC×GC-qMS metabolomics data sets.

On the applicability of comprehensive two-dimensional gas chromatography combined with a fast-scanning quadrupole mass spectrometer for untargeted large-scale metabolomics.

Comprehensive two-dimensional gas chromatography mass spectrometry (GC×GC-MS) offers excellent chromatographic separation performance and superior sensitivity. As such, it is eminently suitable for the analysis of complex biological samples. The applicability of a GC×GC instrument equipped with a fast-scanning qMS detector for large-scale untargeted metabolome analyses was investigated. We optimized the dimensions of an apolar×medium-polar column combination in order to meet detector requirements and to compromise between separation performance and analysis time. The final method enabled a sufficient separation (R≥1.2 or higher) of approx. 90% of all analytes detected in urine within less than 1h. Using the qMS at maximum scan speed (20,000u/s) and choosing a scan range of m/z 60-550, a data acquisition frequency of 33Hz and usually at least 10-13 data points per (2)D peak above the baseline were achieved. Peak area as well as peak height could thus be determined precisely (mean RSD 2.5%). Spectral skewing was limited regarding the data points covering the upper peak half. As a consequence, peak apex spectra could be used for the alignment of analytes in different samples. The linear dynamic range was 1-2.5 orders of magnitude, depending on the analyte. In addition, the slow transition into saturation beyond the linear dynamic range made it possible to exploit an extended "working range" for relative quantification. Long-term stability of the system was demonstrated by the analysis of more than 300 human urine study samples for which detailed repeatability and intermediate precision data are provided. In summary, the GC×GC-qMS system proved to be applicable for untargeted large scale metabolome analyses.

Dose-dependent effects of isoflavone exposure during early lifetime on the rat mammary gland: Studies on estrogen sensitivity, isoflavone metabolism, and DNA methylation.

SCOPE: Isoflavone (ISO) exposure during adolescence modulates 17ß-estradiol (E2) sensitivity of the adult mammary gland. The present study investigated the dose dependency of these effects focusing on proliferation, estrogen receptor dependent and independent gene expression, as well as DNA methylation and ISO metabolism. METHODS AND RESULTS: Female Wistar rats were lifelong exposed to an ISO-depleted diet or to diets enriched with a soy ISO extract (ISO-rich diet (IRD)) causing plasma concentrations as observed minimally (IRDlow) and maximally (IRDhigh) in Asian women. The extract was characterized by both phytochemical analysis and E-Screen. Rats were ovariectomized at postnatal day (PND) 80 and treated with E2 from PND94 to 97. In contrast to uterine response, body weight and visceral fat mass were affected by ISO. In the mammary gland, both E2-induced proliferation (proliferating cell nuclear antigen staining) and estrogen receptor activation (progesterone receptor staining) were significantly reduced by IRDhigh but not by IRDlow, which however attenuated Gdf15 mRNA expression. DNA methylation analysis revealed significant differences in the promoter regions of Aldhl1, Extl1, and WAP between IRDhigh and ISO-depleted diet. CONCLUSION: Lifelong exposure to ISO results in dose-dependent differential effects on proliferation, gene expression, and DNA methylation in rat mammary glands. Yet, a decrease in estrogen responsiveness was only achieved by IRDhigh.