Metabolites, Volume 9, Issue 7 (July 2019) – 33 articles

Milk fat depression (MFD) syndrome represents a significant drawback to the dairy industry. The aim of this study was to unravel the ruminal metabolome-microbiome interaction in response to diet-induced MFD in dairy cows. Twelve healthy second parity Holstein dairy cows (days in milk (DIM) = 119 ± 14) were randomly assigned into control (CON, n = 6) group and treatment (TR, n = 6) group. Cows in TR group received a high-starch total mixed ration (TMR) designed to induce an MFD syndrome. Decreased milk fat yield and concentration in TR cows displayed the successful development of MFD syndrome. TR diet increased the relative abundance of Prevotella and decreased the relative abundance of unclassified Lachnospiraceae, Oribacterium, unclassified Veillonellaceae and Pseudobutyrivibrio in ruminal fluid. Metabolomics analysis revealed that the ruminal fluid content of glucose, amino acids and amines were significantly increased in TR cows compared with CON cows. Correlation analysis revealed that the concentration of amines and amino acids were highly correlated with the abundance of Oribacterium, Pseudobutyrivibrio, RC9_gut_group, unclassified BS11_gut_group and Selenomonas. In general, these findings revealed that TR diet reduced the rumination time and altered rumen fermentation type, which led to changes in the composition of ruminal microbiota and metabolites, and caused MFD. Full article

Stresses such as wounding or atmospheric pollutant exposure have a significant impact on plant fitness. Since it has been widely described that the metabolome directly reflects plant physiological status, a way to assess this impact is to perform a global metabolomic analysis. In this study, we investigated the effect of two abiotic stresses (mechanical wounding and ozone exposure) on parsnip metabolic balance using a liquid chromatography-mass spectrometry-based untargeted metabolomic approach. For this purpose, parsnip leaves were submitted to an acute ozone exposure or were mechanically wounded and sampled 24, 48, and 72 h post-treatment. Multivariate and univariate statistical analyses highlighted numerous differentially-accumulated metabolic features as a function of time and treatment. Mechanical wounding led to a more differentiated response than ozone exposure. We found that the levels of coumarins and fatty acyls increased in wounded leaves, while flavonoid concentration decreased in the same conditions. These results provide an overview of metabolic destabilization through differentially-accumulated compounds and provide a better understanding of global plant metabolic changes in defense mechanisms. Full article

storage Banana fruit after harvest is susceptible to chilling injury, which is featured by peel browning during cold, and it easily loses its nutrition and economic values. This study investigated the role of fibroin treatment in delaying peel browning in association with the antioxidant capability of postharvest banana fruit during cold storage. Compared to the control fruit, fibroin-treated fruit contained higher amounts of Pro and Cys during overall storage as well as higher glutathione (GSH) during the middle of storage. Conversely, fibroin-treated fruit exhibited a lower peel browning index and reactive oxygen species (ROS) level during overall storage as well as lower contents of hexadecanoic acid and octadecanoic acid by the end of storage compared to control fruit. In addition, fibroin-treated banana fruit showed higher activities of superoxide dismutase (SOD) and ascorbate peroxidase (APX) in relation to upregulation SOD, CAT, and GR as well as peroxiredoxins (MT3 and GRX) during the middle of storage. These results highlighted the role of fibroin treatment in reducing peel browning by enhancing the antioxidant capability of harvested banana fruit during cold storage. Full article

Residual feed intake (RFI) is designed to estimate net efficiency of feed use, so low RFI animals are considered for selection to reduce feeding costs. However, metabolic profiling of cows and availability of predictive metabolic biomarkers for RFI are scarce. Therefore, this study aims to generate a better understanding of metabolic mechanisms behind low and high RFI in Jerseys and Holsteins and identify potential predictive metabolic biomarkers. Each metabolite was analyzed to reveal their associations with two RFIs in two breeds by a linear regression model. An integrative analysis of metabolomics and transcriptomics was performed to explore interactions between functionally related metabolites and genes in the created metabolite networks. We found that three main clusters were detected in the heat map and all identified fatty acids (palmitoleic, hexadecanoic, octadecanoic, heptadecanoic, and tetradecanoic acid) were grouped in a cluster. The lower cluster were all from fatty acids, including palmitoleic acid, hexadecanoic acid, octadecanoic acid, heptadecanoic acid, and tetradecanoic acid. The first component of the partial least squares-discriminant analysis (PLS-DA) explained a majority (61.5%) of variations of all metabolites. A good division between two breeds was also observed. Significant differences between low and high RFIs existed in the fatty acid group (P < 0.001). Statistical results revealed clearly significant differences between breeds; however, the association of individual metabolites (leucine, ornithine, pentadecanoic acid, and valine) with the RFI status was only marginally significant or not significant due to a lower sample size. The integrated gene-metabolite pathway analysis showed that pathway impact values were higher than those of a single metabolic pathway. Both types of pathway analyses revealed three important pathways, which were aminoacyl-tRNA biosynthesis, alanine, aspartate, and glutamate metabolism, and the citrate cycle (TCA cycle). Finally, one gene (2-hydroxyacyl-CoA lyase 1 (+HACL1)) associated with two metabolites (-α-ketoglutarate and succinic acid) were identified in the gene-metabolite interaction network. This study provided novel metabolic pathways and integrated metabolic-gene expression networks in high and low RFI Holstein and Jersey cattle, thereby providing a better understanding of novel biochemical mechanisms underlying variation in feed efficiency. Full article

Metabolic changes in sorghum seedlings in response to Paenibacillus alvei (NAS-6G6)-induced systemic resistance against Fusarium pseudograminearum crown rot were investigated by means of untargeted ultra-high performance liquid chromatography-high definition mass spectrometry (UHPLC-HDMS). Treatment of seedlings with the plant growth-promoting rhizobacterium P. alvei at a concentration of 1 × 10⁸ colony forming units mL⁻¹ prior to inoculation with F. pseudograminearum lowered crown rot disease severity significantly at the highest inoculum dose of 1 × 10⁶ spores mL⁻¹. Intracellular metabolites were subsequently methanol-extracted from treated and untreated sorghum roots, stems and leaves at 1, 4 and 7 days post inoculation (d.p.i.) with F. pseudograminearum. The extracts were analysed on an UHPLC-HDMS platform, and the data chemometrically processed to determine metabolic profiles and signatures related to priming and induced resistance. Significant treatment-related differences in primary and secondary metabolism post inoculation with F. pseudograminearum were observed between P. alvei-primed versus naïve S. bicolor seedlings. The differential metabolic reprogramming in primed plants comprised of a quicker and/or enhanced upregulation of amino acid-, phytohormone-, phenylpropanoid-, flavonoid- and lipid metabolites in response to inoculation with F. pseudograminearum. Full article

Metabolomics is an effective approach to characterize the metabotype which can reflect the influence of genetics, physiological status, and environmental factors such as drug intakes, diet. Diet may change the chemopreventive efficacy of given agents due to the altered physiological status of the subject. Here, metabolomics response to a chemopreventive agent targretin or tamoxifen, in rats with methylnitrosourea-induced tumors on a standard diet (4% fat, CD) or a high fat diet (21% fat, HFD) was evaluated, and found that (1) the metabolome was substantially affected by diet and/or drug treatment; (2) multiple metabolites were identified as potential pharmacodynamic biomarkers related to targretin or tamoxifen regardless of diet and time; and (3) the primary bile acid pathway was significantly affected by targretin treatment in rats on both diets, and the lysolipid pathway was significantly affected by tamoxifen treatment in rats on the high fat diet. Full article

As a facultative intracellular pathogen, Staphylococcus aureus is able to invade and proliferate within many types of mammalian cells. Intracellular bacterial replication relies on host nutrient supplies and, therefore, cell metabolism is closely bound to intracellular infection. Here, we investigated how S. aureus invasion affects the host membrane-bound fatty acids. We quantified the relative levels of fatty acids and their labelling pattern after intracellular infection by gas chromatography-mass spectrometry (GC-MS). Interestingly, we observed that the levels of three host fatty acids—docosanoic, eicosanoic and palmitic acids—were significantly increased in response to intracellular S. aureus infection. Accordingly, labelling carbon distribution was also affected in infected cells, in comparison to the uninfected control. In addition, treatment of HeLa cells with these three fatty acids showed a cytoprotective role by directly reducing S. aureus growth. Full article

Carbonic anhydrases (CAs, EC 4.2.1.1) are ubiquitous metalloenzymes, which catalyze the crucial physiological CO₂ hydration/dehydration reaction (CO₂ + H₂O ⇌ HCO₃⁻ + H⁺) balancing the equilibrium between CO₂, H₂CO₃, HCO₃⁻ and CO₃²⁻. It has been demonstrated that their selective inhibition alters the equilibrium of the metabolites above affecting the biosynthesis and energy metabolism of the organism. In this context, our interest has been focalized on the fungus Malassezia restricta, which may trigger dandruff and seborrheic dermatitis altering the complex bacterial and fungal equilibrium of the human scalp. We investigated a rather large number of inorganic metal-complexing anions (a well-known class of CA inhibitors) for their interaction with the β-CA (MreCA) encoded by the M. restricta genome. The results were compared with those obtained for the two human α-CA isoforms (hCAI and hCAII) and the β-CA from Malassezia globosa. The most effective MreCA inhibitors were diethyldithiocarbamate, sulfamide, phenyl arsenic acid, stannate, tellurate, tetraborate, selenocyanate, trithiocarbonate, and bicarbonate. The different K_I values obtained for the four proteins investigated might be attributed to the architectural features of their catalytic site. The anion inhibition profile is essential for better understanding the inhibition/catalytic mechanisms of these enzymes and for designing novel types of inhibitors, which may have clinical applications for the management of dandruff and seborrheic dermatitis. Full article

The trithiolato bridged diruthenium complex DiRu-1 [(p-MeC₆H₄ⁱPr)₂Ru₂(SC₆H₄-p-Bu^t)₃]⁺ is highly cytotoxic against various cancer cell lines, but its exact mode of action remains unknown. The present ¹H HR-MAS NMR-based metabolomic study was performed on ovarian cancer cell line A2780, on its cis-Pt resistant variant A2780cisR, and on the cell line HEK-293 treated with 0.03 µM and 0.015 µM of DiRu-1 corresponding to full and half IC₅₀ doses, respectively, to investigate the mode of action of this ruthenium complex. The resulting changes in the metabolic profile of the cell lines were studied using HR-MAS NMR of cell lysates and a subsequent statistical analysis. We show that DiRu-1 in a 0.03 µM dose has significant impact on the levels of a number of metabolites, such as glutamine, glutamate, glutathione, cysteine, lipid, creatine, lactate, and acetate, especially pronounced in the A2780cisR cell line. The IC₅₀/2 dose shows some significant changes, but full IC₅₀ appears to be necessary to observe the full effect. Overall, the metabolic changes observed suggest that redox homeostasis, the Warburg effect, and the lipid metabolism are affected by DiRu-1. Full article

The application of metabolomics technology to epidemiological studies is emerging as a new approach to elucidate disease etiology and for biomarker discovery. However, analysis of metabolomics data is complex and there is an urgent need for the standardization of analysis workflow and reporting of study findings. To inform the development of such guidelines, we conducted a survey of 47 cohort representatives from the Consortium of Metabolomics Studies (COMETS) to gain insights into the current strategies and procedures used for analyzing metabolomics data in epidemiological studies worldwide. The results indicated a variety of applied analytical strategies, from biospecimen and data pre-processing and quality control to statistical analysis and reporting of study findings. These strategies included methods commonly used within the metabolomics community and applied in epidemiological research, as well as novel approaches to pre-processing pipelines and data analysis. To help with these discrepancies, we propose use of open-source initiatives such as the online web-based tool COMETS Analytics, which includes helpful tools to guide analytical workflow and the standardized reporting of findings from metabolomics analyses within epidemiological studies. Ultimately, this will improve the quality of statistical analyses, research findings, and study reproducibility. Full article

Metabolomics has started to embrace computational approaches for chemical interpretation of large data sets. Yet, metabolite annotation remains a key challenge. Recently, molecular networking and MS2LDA emerged as molecular mining tools that find molecular families and substructures in mass spectrometry fragmentation data. Moreover, in silico annotation tools obtain and rank candidate molecules for fragmentation spectra. Ideally, all structural information obtained and inferred from these computational tools could be combined to increase the resulting chemical insight one can obtain from a data set. However, integration is currently hampered as each tool has its own output format and efficient matching of data across these tools is lacking. Here, we introduce MolNetEnhancer, a workflow that combines the outputs from molecular networking, MS2LDA, in silico annotation tools (such as Network Annotation Propagation or DEREPLICATOR), and the automated chemical classification through ClassyFire to provide a more comprehensive chemical overview of metabolomics data whilst at the same time illuminating structural details for each fragmentation spectrum. We present examples from four plant and bacterial case studies and show how MolNetEnhancer enables the chemical annotation, visualization, and discovery of the subtle substructural diversity within molecular families. We conclude that MolNetEnhancer is a useful tool that greatly assists the metabolomics researcher in deciphering the metabolome through combination of multiple independent in silico pipelines. Full article

High-throughput metabolomics investigations, when conducted in large human cohorts, represent a potentially powerful tool for elucidating the biochemical diversity underlying human health and disease. Large-scale metabolomics data sources, generated using either targeted or nontargeted platforms, are becoming more common. Appropriate statistical analysis of these complex high-dimensional data will be critical for extracting meaningful results from such large-scale human metabolomics studies. Therefore, we consider the statistical analytical approaches that have been employed in prior human metabolomics studies. Based on the lessons learned and collective experience to date in the field, we offer a step-by-step framework for pursuing statistical analyses of cohort-based human metabolomics data, with a focus on feature selection. We discuss the range of options and approaches that may be employed at each stage of data management, analysis, and interpretation and offer guidance on the analytical decisions that need to be considered over the course of implementing a data analysis workflow. Certain pervasive analytical challenges facing the field warrant ongoing focused research. Addressing these challenges, particularly those related to analyzing human metabolomics data, will allow for more standardization of as well as advances in how research in the field is practiced. In turn, such major analytical advances will lead to substantial improvements in the overall contributions of human metabolomics investigations. Full article

Burn injury initiates a hypermetabolic response leading to muscle catabolism and organ dysfunction but has not been well-characterized by high-throughput metabolomics. We examined changes in metabolism over the first 72 h post-burn using proton nuclear magnetic resonance (¹H-NMR) spectroscopy and serum from a porcine model of severe burn injury. We sought to quantify the changes in metabolism that occur over time in response to severe burn and smoke inhalation in this preliminary study. Fifteen pigs received 40% total body surface area (TBSA) burns with additional pine bark smoke inhalation. Arterial blood was drawn at baseline (pre-burn) and every 24 h until 72 h post-injury or death. The aqueous portion of each serum sample was analyzed using ¹H-NMR spectroscopy and metabolite concentrations were used for principal component analysis (PCA). Thirty-eight metabolites were quantified in 39 samples. Of these, 31 showed significant concentration changes over time (p < 0.05). PCA revealed clustering of samples by time point on a 2D scores plot. The first 48 h post-burn were characterized by high concentrations of histamine, alanine, phenylalanine, and tyrosine. Later timepoints were characterized by rising concentrations of 2-hydroxybutyrate, 3-hydroxybutyrate, acetoacetate, and isovalerate. No significant differences in metabolism related to mortality were observed. Our work highlights the accumulation of organic acids resulting from fatty acid catabolism and oxidative stress. Further studies will be required to relate accumulation of the four organic carboxylates identified in this analysis to outcomes from burn injury. Full article

Saliva samples of seventeen soccer players were analyzed by nuclear magnetic resonance before and after an official match. Two different ways of normalizing data are discussed, using total proteins and total metabolite concentrations. Changes in markers related to energy, hydration status, amino acids and other compounds were found. The limits and advantages of using saliva to define the systemic responses to exercise are examined, both in terms of data normalization and interpretation, and the time that the effect lasts in this biofluid, which is shorter to that commonly observed in blood. The heterogeneous nature and different timing of the exercise developed by players also plays an important role in the metabolic changes that can be measured. Our work focuses mainly on three different aspects: The effect that time sampling has on the observed effect, the type of normalization that is necessary to perform in order to cope with changes in water content, and the metabolic response that can be observed using saliva. Full article

Oral cancer is a public health problem with an incidence of almost 50,000 and a mortality of 10,000 each year in the USA alone. Black raspberries (BRBs) have been shown to inhibit oral carcinogenesis in several preclinical models, but our understanding of how BRB phytochemicals affect the metabolic pathways during oral carcinogenesis remains incomplete. We used a well-established rat oral cancer model to determine potential metabolic pathways impacted by BRBs during oral carcinogenesis. F344 rats were exposed to the oral carcinogen 4-nitroquinoline-1-oxide in drinking water for 14 weeks, then regular drinking water for six weeks. Carcinogen exposed rats were fed a 5% or 10% BRB supplemented diet or control diet for six weeks after carcinogen exposure. RNA-Seq transcriptome analysis on rat tongue, and mass spectrometry and NMR metabolomics analysis on rat urine were performed. We tentatively identified 57 differentially or uniquely expressed metabolites and over 662 modulated genes in rats being fed with BRB. Glycolysis and AMPK pathways were modulated during BRB-mediated oral cancer chemoprevention. Glycolytic enzymes Aldoa, Hk2, Tpi1, Pgam2, Pfkl, and Pkm2 as well as the PKA-AMPK pathway genes Prkaa2, Pde4a, Pde10a, Ywhag, and Crebbp were downregulated by BRBs during oral cancer chemoprevention. Furthermore, the glycolysis metabolite glucose-6-phosphate decreased in BRB-administered rats. Our data reveal the novel metabolic pathways modulated by BRB phytochemicals that can be targeted during the chemoprevention of oral cancer. Full article

In recent decades, intensive selective breeding programs have allowed the development of disease-resistant and flavorsome apple cultivars while leading to a gradual decline of a large number of ancient varieties in many countries. However, the re-evaluation of such cultivars could lead to the production new apple-based products with health beneficial properties and/or unique flavor qualities. Herein, we report the comprehensive characterization of juices obtained from 86 old, mostly Danish, apple cultivars, by employing traditional analysis (ion chromatography, °Brix, headspace gas chromatography/mass spectrometry (GC–MS), and panel test evaluation) as well as an innovative nuclear magnetic resonance (NMR)-based screening method developed by Bruker for fruit juices, known as Spin Generated Fingerprint (SGF) Profiling™. Principal component analysis showed large differences in aroma components and sensory characteristics, including odd peculiar odors and flavors such as apricot and peach, and very different levels of phenolic compounds, acids and sugars among the analyzed juices. Moreover, we observed a tendency for late-season juices to be characterized by higher °Brix values, sugar content and they were perceived to be sweeter and more flavor intense than early-season juices. Our findings are useful for the production of specialty vintage-cultivar apple juices or mixed juices to obtain final products that are characterized both by healthy properties and peculiar sensory attributes. Full article

A pilot study was carried out on five obese/overweight patients suffering from metabolic syndrome, with the aim to evaluate postprandial effects of high fat/high glycemic load meals enriched by blueberries. Postprandial urine samples were analyzed by ¹H-NMR spectroscopy after 2 and 4 h from ingestion to identify potential markers of blueberry intake. Significant decrease of methylamines, acetoacetate, acetone and succinate, known indicators of type 2 diabetes mellitus, were observed after the intake of meals enriched with blueberries. On the other hand, an accumulation of p-hydroxyphenyl-acetic acid and 3-(3’-hydroxyphenyl)-3-hydropropionic acid originating from gut microbial dehydrogenation of proanthocyanidins and procyanidins was detected. Real-time PCR-analysis of mRNAs obtained from mononuclear blood cells showed significant changes in cytokine gene expression levels after meals integrated with blueberries. In particular, the mRNAs expression of interleukin-6 (IL-6) and Transforming Growth Factor-β (TGF-β), pro and anti-inflammation cytokines, respectively, significantly decreased and increased after blueberry supplementation, indicating a positive impact of blueberry ingestion in the reduction of risk of inflammation. The combined analysis of the urine metabolome and clinical markers represents a promising approach in monitoring the metabolic impact of blueberries in persons with metabolic syndrome. Full article

High-intensity interval training (HIIT) is known to improve performance and skeletal muscle energy metabolism. However, whether the body’s adaptation to an exhausting short-term HIIT is reflected in the resting human metabolome has not been examined so far. Therefore, a randomized controlled intervention study was performed to investigate the effect of a ten-day HIIT on the resting urinary metabolome of young active men. Fasting spot urine was collected before (−1 day) and after (+1 day; +4 days) the training intervention and 65 urinary metabolites were identified by liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) spectroscopy. Metabolite concentrations were normalized to urinary creatinine and subjected to univariate statistical analysis. One day after HIIT, no overall change in resting urinary metabolome, except a significant difference with decreasing means in urinary hypoxanthine concentration, was documented in the experimental group. As hypoxanthine is related to purine degradation, lower resting urinary hypoxanthine levels may indicate a training-induced adaptation in purine nucleotide metabolism. Full article

The intestine is a small world where all the chemical reactions are operated by gut microbiota. Study on the gut metabolism of natural products is a new and expanding research area that leads to new bioactive metabolites, as well as novel chemical reactions. To provide exemplary cases, flavonoid biotransformation by intestinal bacteria with focus on S-equol biosynthesis and aryl methyl ether cleavage reaction, is described in this review. Full article

Neurodegenerative diseases are prevalent and devastating. While extensive research has been done over the past decades, we are still far from comprehensively understanding what causes neurodegeneration and how we can prevent it or reverse it. Recently, systems biology approaches have led to a holistic examination of the interactions between genome, metabolome, and the environment, in order to shed new light on neurodegenerative pathogenesis. One of the new technologies that has emerged to facilitate such studies is imaging mass spectrometry (IMS). With its ability to map a wide range of small molecules with high spatial resolution, coupled with the ability to quantify them at once, without the need for a priori labeling, IMS has taken center stage in current research efforts in elucidating the role of the metabolome in driving neurodegeneration. IMS has already proven to be effective in investigating the lipidome and the proteome of various neurodegenerative diseases, such as Alzheimer’s, Parkinson’s, Huntington’s, multiple sclerosis, and amyotrophic lateral sclerosis. Here, we review the IMS platform for capturing biological snapshots of the metabolic state to shed more light on the molecular mechanisms of the diseased brain. Full article

Sarcopenia is the age-related loss of skeletal muscle mass, strength and function, which may be accelerated during periods of physical inactivity. Declines in skeletal muscle and functionality not only impacts mobility but also increases chronic disease risk, such as type 2 diabetes. The aim of this study was to measure adaptive metabolic responses to acute changes in habitual activity in a cohort of overweight, pre-diabetic older adults (age = 69 ± 4 years; BMI = 27 ± 4 kg/m², n = 17) when using non-targeted metabolite profiling by multisegment injection-capillary electrophoresis-mass spectrometry. Participants completed two weeks of step reduction (<1000 steps/day) followed by a two week recovery period, where fasting plasma samples were collected at three time intervals at baseline, after step reduction and following recovery. Two weeks of step reduction elicited increases in circulatory metabolites associated with a decline in muscle energy metabolism and protein degradation, including glutamine, carnitine and creatine (q < 0.05; effect size > 0.30), as well as methionine and deoxycarnitine (p < 0.05; effect size ≈ 0.20) as compared to baseline. Similarly, decreases in uremic toxins in plasma that promote muscle inflammation, indoxyl sulfate and hippuric acid, as well as oxoproline, a precursor used for intramuscular glutathione recycling, were also associated with physical inactivity (p < 0.05; effect size > 0.20). Our results indicate that older persons are susceptible to metabolic perturbations due to short-term step reduction that were not fully reversible with resumption of normal ambulatory activity over the same time period. These plasma biomarkers may enable early detection of inactivity-induced metabolic dysregulation in older persons at risk for sarcopenia not readily measured by current imaging techniques or muscle function tests, which is required for the design of therapeutic interventions to counter these deleterious changes in support of healthy ageing. Full article

Metabolomics has emerged as a promising technique to understand relationships between environmental factors and health status. Through comprehensive profiling of small molecules in biological samples, metabolomics generates high-dimensional data objectively, reflecting exposures, endogenous responses, and health effects, thereby providing further insights into exposure-disease associations. However, the multivariate nature of metabolomics data contributes to high complexity in analysis and interpretation. Efficient visualization techniques of multivariate data that allow direct interpretation of combined exposures, metabolome, and disease risk, are currently lacking. We have therefore developed the ‘triplot’ tool, a novel algorithm that simultaneously integrates and displays metabolites through latent variable modeling (e.g., principal component analysis, partial least squares regression, or factor analysis), their correlations with exposures, and their associations with disease risk estimates or intermediate risk factors. This paper illustrates the framework of the ‘triplot’ using two synthetic datasets that explore associations between dietary intake, plasma metabolome, and incident type 2 diabetes or BMI, an intermediate risk factor for lifestyle-related diseases. Our results demonstrate advantages of triplot over conventional visualization methods in facilitating interpretation in multivariate risk modeling with high-dimensional data. Algorithms, synthetic data, and tutorials are open source and available in the R package ‘triplot’. Full article

Ginkgo leaf extract (GLE) is a popular herbal medicine and dietary supplement for the treatment of various diseases, including cardiovascular disease. GLE contains a variety of secondary plant metabolites, such as flavonoids and terpenoids, as active components. Some of these phytochemicals have been known to be metabolized by gut microbial enzymes. The aim of this study was to investigate the effects of the gut microbiota on the pharmacokinetics of the main constituents of GLE using antibacterial-treated mice. The bilobalide, ginkgolide A, ginkgolide B, ginkgolide C, isorhamnetin, kaempferol, and quercetin pharmacokinetic profiles of orally administered GLE (600 mg/kg), with or without ciprofloxacin pretreatment (150 mg/kg/day for 3 days), were determined. In the antibacterial-treated mice, the maximum plasma concentration (C_max) and area under the curve (AUC) of isorhamnetin were significantly (p < 0.05) increased when compared with the control group. The C_max and AUC of kaempferol and quercetin (other flavonol glycosides) were slightly higher than those of the control group, but the difference was not statistically significant, while both parameters for terpenoids of GLE showed no significant difference between the antibacterial-treated and control groups. These results showed that antibacterial consumption may increase the bioavailability of isorhamnetin by suppressing gut microbial metabolic activities. Full article

Background: different Solanaceae and Erythroxylaceae species produce tropane alkaloids. These alkaloids are the starting material in the production of different pharmaceuticals. The commercial demand for tropane alkaloids is covered by extracting them from cultivated plants. Datura stramonium is cultivated under greenhouse conditions as a source of tropane alkaloids. Here we investigate the effect of different levels of water availability in the soil on the production of tropane alkaloids by D. stramonium. Methods: We tested four irrigation levels on the accumulation of tropane alkaloids. We analyzed the profile of tropane alkaloids using an untargeted liquid chromatography/mass spectrometry method. Results: Using a combination of informatics and manual interpretation of mass spectra, we generated several structure hypotheses for signals in D. stramonium extracts that we assign as putative tropane alkaloids. Quantitation of mass spectrometry signals for our structure hypotheses across different anatomical organs allowed us to identify patterns of tropane alkaloids associated with different levels of irrigation. Furthermore, we identified anatomic partitioning of tropane alkaloid isomers with pharmaceutical applications. Conclusions: Our results show that soil water availability is an effective method for maximizing the production of specific tropane alkaloids for industrial applications. Full article

Respiratory tract infections caused by the Influenza A virus (IAV) are a worldwide problem for human and animal health. Within this study, we analyzed the impact of IAV infection on the immune-related lipidome (eicosanoids) of the pig as new infection model. For this purpose, we performed HPLC-MS/MS using dynamic multiple reaction monitoring and analyzed lung, spleen, blood plasma and bronchoalveolar lavages. IAV infection leads to collective changes in the levels of the analyzed hydroxyeicosatrienoic acids (HETEs), hydroxydocosahexaenoic acids (HDHAs) and epoxyeicosatrienoic acids (EETs), and moreover, unique eicosanoid changes in several sample types, even under mild infection conditions. In accordance with different mouse infection studies, we observed infection-related patterns for 12-HETE, 15-HETE and 17-HDHA, which seem to be common for IAV infection. Using a long-term approach of 21 days we established an experimental setup that can be used also for bacterial-viral coinfection experiments. Full article

Associations between multicollinear accelerometry-derived physical activity (PA) data and cardiometabolic health in children needs to be analyzed using an approach that can handle collinearity among the explanatory variables. The aim of this paper is to provide readers a tutorial overview of interpretation of multivariate pattern analysis models using PA accelerometry data that reveals the associations to cardiometabolic health. A total of 841 children (age 10.2 ± 0.3 years) provided valid data on accelerometry (ActiGraph GT3X+) and six indices of cardiometabolic health that were used to create a composite score. We used a high-resolution PA description including 23 intensity variables covering the intensity spectrum (from 0–99 to ≥10000 counts per minute), and multivariate pattern analysis to analyze data. We report different statistical measures of the multivariate associations between PA and cardiometabolic health and use decentile groups of PA as a basis for discussing the meaning and impact of multicollinearity. We show that for high-resolution accelerometry data; considering all explanatory variables is crucial to obtain a correct interpretation of associations to cardiometabolic health; which is otherwise strongly confounded by multicollinearity in the dataset. Thus; multivariate pattern analysis challenges the traditional interpretation of findings from linear regression models assuming independent explanatory variables Full article

To assist with management and interpretation of human metabolomics data, which are rapidly increasing in quantity and complexity, we need better visualization tools. Using a dataset of several hundred metabolite measures profiled in a cohort of ~1500 individuals sampled from a population-based community study, we performed association analyses with eight demographic and clinical traits and outcomes. We compared frequently used existing graphical approaches with a novel ‘rain plot’ approach to display the results of these analyses. The ‘rain plot’ combines features of a raindrop plot and a conventional heatmap to convey results of multiple association analyses. A rain plot can simultaneously indicate effect size, directionality, and statistical significance of associations between metabolites and several traits. This approach enables visual comparison features of all metabolites examined with a given trait. The rain plot extends prior approaches and offers complementary information for data interpretation. Additional work is needed in data visualizations for metabolomics to assist investigators in the process of understanding and convey large-scale analysis results effectively, feasibly, and practically. Full article

The pathogenesis of age-related macular degeneration (AMD), a leading cause of blindness worldwide, remains only partially understood. This has led to the current lack of accessible and reliable biofluid biomarkers for diagnosis and prognosis, and absence of treatments for dry AMD. This study aimed to assess the plasma metabolomic profiles of AMD and its severity stages with the ultimate goal of contributing to addressing these needs. We recruited two cohorts: Boston, United States (n = 196) and Coimbra, Portugal (n = 295). Fasting blood samples were analyzed using ultra-high performance liquid chromatography mass spectrometry. For each cohort, we compared plasma metabolites of AMD patients versus controls (logistic regression), and across disease stages (permutation-based cumulative logistic regression considering both eyes). Meta-analyses were then used to combine results from the two cohorts. Our results revealed that 28 metabolites differed significantly between AMD patients versus controls (false discovery rate (FDR) q-value: 4.1 × 10⁻²–1.8 × 10⁻⁵), and 67 across disease stages (FDR q-value: 4.5 × 10⁻²–1.7 × 10⁻⁴). Pathway analysis showed significant enrichment of glycerophospholipid, purine, taurine and hypotaurine, and nitrogen metabolism (p-value ≤ 0.04). In conclusion, our findings support that AMD patients present distinct plasma metabolomic profiles, which vary with disease severity. This work contributes to the understanding of AMD pathophysiology, and can be the basis of future biomarkers and precision medicine for this blinding condition. Full article

Unmet clinical diagnostic needs exist for many complex diseases, which it is hoped will be solved by the discovery of metabolomics biomarkers. However, as yet, no diagnostic tests based on metabolomics have yet been introduced to the clinic. This review is presented as a research perspective on how data analysis methods in metabolomics biomarker discovery may contribute to the failure of biomarker studies and suggests how such failures might be mitigated. The study design and data pretreatment steps are reviewed briefly in this context, and the actual data analysis step is examined more closely. Full article

Vitamin D deficiency is associated with not only cardiovascular disease itself but also cardiovascular risk factors, including obesity, hypertension, diabetes, hyperglycemia, and dyslipidemia. This study aimed to investigate the relationship between vitamin D level and lipid profile in non-obese children. A total of 243 non-obese healthy volunteers, aged 9–18 years, were enrolled from March to May 2017. Their height and weight were measured, and body mass index was calculated. Subjects underwent blood tests, including measurements of vitamin D (25(OH)D) level and lipid panels, and were divided into either the vitamin D-deficient group (<20 ng/mL) or normal group. The student’s t-test and a simple linear regression analysis were used to estimate the association between vitamin D level and lipid profile. Overall, 69.5% of non-obese children (n = 169) had a 25(OH)D level of less than 20 ng/mL. The vitamin D-deficient group showed higher triglyceride (TG) level and TG/high-density lipoprotein cholesterol (HDL-C) ratio than the normal group (TG level: 90.27 vs. 74.74 mmol/L, p = 0.003; TG/HDL-C ratio: 1.753 vs. 1.358, p = 0.003). Vitamin D level seems to affect the lipid profile, even in non-obese children, and a low vitamin D level may progress to dyslipidemia or obesity in non-obese children. Full article