Multilevel data analysis of a crossover designed human nutritional intervention study

The abundance of metabolites related to protein methylation correlates with the metastatic capacity of human melanoma xenografts

Metabolic reprogramming is a major factor in transformation, and particular metabolic phenotypes correlate with oncogenotype, tumor progression, and metastasis. By profiling metabolites in 17 patient-derived xenograft melanoma models, we identified durable metabolomic signatures that correlate with biological features of the tumors. BRAF mutant tumors had metabolomic and metabolic flux features of enhanced glycolysis compared to BRAF wild-type tumors. Tumors that metastasized efficiently from their primary sites had elevated levels of metabolites related to protein methylation, including trimethyllysine (TML). TML levels correlated with histone H3 trimethylation at Lys⁹ and Lys²⁷, and methylation at these sites was also enhanced in efficiently metastasizing tumors. Erasing either of these marks by genetically or pharmacologically silencing the histone methyltransferase SETDB1 or EZH2 had no effect on primary tumor growth but reduced cellular invasiveness and metastatic spread. Thus, metabolite profiling can uncover targetable epigenetic requirements for the metastasis of human melanoma cells.

Metabolic profiles of adipose-derived and bone marrow-derived stromal cells from elderly coronary heart disease patients by capillary liquid chromatography quadrupole time-of-flight mass spectrometry

Adipose-tissue derived mesenchymal stem cell (ADSC)-based therapy is a promising option for patients with atherosclerotic conditions, including coronary artery disease. However, the potential differences in the metabolic characteristics between bone marrow-derived mesenchymal stem cells (BMSCs) and ADSCs have remained to be fully elucidated. The present study aimed to compare the metabolic profiles of BMSCs and ADSCs via liquid chromatography quadrupole time-of-flight mass spectrometry. BMSCs and ADSCs obtained from elderly coronary heart disease patients were cultured, and after three passages, supernatants of each cell type were collected and systematically analysed. Substantial differences were detected between the metabolite signatures of ADSCs and BMSCs. In addition, further analysis using partial least-squares discriminant analysis score plots indicated significant differences between the supernatants of the two cell types. The following metabolites were deemed to be responsible for the potential differences in the metabolic characteristics of BMSCs and ADSCs: D-lactic acid, hydroxyindoleacetaldehyde, α-D-glucose, bovinic acid, 9,10-epoxyoctadecenoic acid, glyceraldehyde, phenylpyruvic acid, L-octanoylcarnitine, retinyl ester, α-ketoisovaleric acid, guanidoacetic acid, N-acetylneuraminic acid, imidazoleacetic acid riboside, sphingosine and pseudouridine 5′-phosphate. Based on these findings, there may be significant differences in the following metabolic pathways: The linoleic acid metabolic pathway, galactose metabolism, argentines and proline metabolism, retinol metabolism, glycine and serine metabolism, galactose metabolism, and amino sugar and nucleotide sugar metabolism. In conclusion, substantial differences in metabolic characteristics were detected between BMSCs and ADSCs, which may be associated with the different efficacies of atherosclerosis therapies employing these cell types.

Improving Visualization and Interpretation of Metabolome-Wide Association Studies: An Application in a Population-Based Cohort Using Untargeted 1H NMR Metabolic Profiling

¹H NMR spectroscopy of biofluids generates reproducible data allowing detection and quantification of small molecules in large population cohorts. Statistical models to analyze such data are now well-established, and the use of univariate metabolome wide association studies (MWAS) investigating the spectral features separately has emerged as a computationally efficient and interpretable alternative to multivariate models. The MWAS rely on the accurate estimation of a metabolome wide significance level (MWSL) to be applied to control the family wise error rate. Subsequent interpretation requires efficient visualization and formal feature annotation, which, in-turn, call for efficient prioritization of spectral variables of interest. Using human serum ¹H NMR spectroscopic profiles from 3948 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), we have performed a series of MWAS for serum levels of glucose. We first propose an extension of the conventional MWSL that yields stable estimates of the MWSL across the different model parameterizations and distributional features of the outcome. We propose both efficient visualization methods and a strategy based on subsampling and internal validation to prioritize the associations. Our work proposes and illustrates practical and scalable solutions to facilitate the implementation of the MWAS approach and improve interpretation in large cohort studies.

Effect of pistachio consumption on the modulation of urinary gut microbiota-related metabolites in prediabetic subjects

The specific nutritional composition of nuts could affect different metabolic pathways involved in a broad range of metabolic diseases. We therefore investigated whether chronic consumption of pistachio nuts modifies the urine metabolome in prediabetic subjects. We designed a randomized crossover clinical trial in 39 prediabetic subjects. They consumed a pistachio-supplemented diet (PD, 50% carbohydrates, 33% fat, including 57 g/d of pistachios daily) and a control diet (CD, 55% carbohydrates, 30% fat) for 4 months each, separated by a 2-week wash-out. Nuclear magnetic resonance (NRM) was performed to determine changes in 24-h urine metabolites. Significant changes in urine metabolites according to the different intervention periods were found in uni- and multivariate analysis. Score plot of the first two components of the multilevel partial least squares discriminant analysis (ML-PLS-DA) showed a clear separation of the intervention periods. Three metabolites related with gut microbiota metabolism (i.e., hippurate, p-cresol sulfate and dimethylamine) were found decreased in PD compared with CD (P<.05). Moreover, cis-aconitate [intermediate of the tricarboxylic acid (TCA)] was also found decreased following PD compared with CD. Intragroup analysis showed that creatinine levels were significantly increased in PD (P=.023), whereas trimethylamine N-oxide (TMAO) was found significantly reduced following PD (P=.034). Our results suggest that chronic pistachio consumption may modulate some urinary metabolites related to gut microbiota metabolism and the TCA cycle; all associated with metabolic derangements associated with insulin resistance and Type 2 diabetes.

NMR metabolomics highlights sphingosine kinase-1 as a new molecular switch in the orchestration of aberrant metabolic phenotype in cancer cells

Strong experimental evidence in animal and cellular models supports a pivotal role of sphingosine kinase‐1 (SK1) in oncogenesis. In many human cancers, SK1 levels are upregulated and these increases are linked to poor prognosis in patients. Here, by employing untargeted NMR‐based metabolomic profiling combined with functional validations, we report the crucial role of SK1 in the metabolic shift known as the Warburg effect in A2780 ovarian cancer cells. Indeed, expression of SK1 induced a high glycolytic rate, characterized by increased levels of lactate along with increased expression of the proton/monocarboxylate symporter MCT1, and decreased oxidative metabolism, associated with the accumulation of intermediates of the tricarboxylic acid cycle and reduction in CO₂ production. Additionally, SK1‐expressing cells displayed a significant increase in glucose uptake paralleled by GLUT3 transporter upregulation. The role of SK1 is not limited to the induction of aerobic glycolysis, affecting metabolic pathways that appear to support the biosynthesis of macromolecules. These findings highlight the role of SK1 signaling axis in cancer metabolic reprogramming, pointing out innovative strategies for cancer therapies.

Targeted metabolomics reveals differences in the extended postprandial plasma metabolome of healthy subjects after intake of whole-grain rye porridges versus refined wheat bread

SCOPE

We previously found that whole-grain (WG) rye porridges suppressed appetite and improved glucose metabolism. This study aimed to investigate potential plasma metabolites that may be related to differences in those appetite and glucose responses.

METHODS AND RESULTS

Twenty-one health subjects consumed six isocaloric breakfasts in a randomized cross-over study. Plain WG rye porridges (40 and 55 g), rye porridge enriched with different inulin: gluten proportions (9:3 g; 6:6 g; 3:9 g), and a 55 g refined wheat bread (control) were served as part of complete breakfast, followed by a standardized lunch. NMR metabolomics assessed 36 plasma metabolites and short chain fatty acids were measured by GC-MS from baseline up to 8 h. Pre-lunch plasma essential amino acids reflected protein composition and post-lunch plasma short chain fatty acids varied with fiber content in breakfasts. No correlations were observed between measured metabolites and glucose, insulin, or appetite responses.

CONCLUSIONS

Differences in protein and fiber contents in breakfasts altered postprandial plasma amino acids and short chain fatty acids, respectively, but were unrelated to appetite and glucose responses. Further studies are warrant to identify the underlying mechanisms for the beneficial effects on appetite and second meal glucose responses after rye-based foods.

Tryptophan and purine metabolites are consistently upregulated in the urinary metabolome of patients diagnosed with gestational diabetes mellitus throughout pregnancy: A longitudinal metabolomics study of Chinese pregnant women part 2

BACKGROUND

Gestational diabetes mellitus (GDM) is a pathological state of glucose intolerance associated with adverse pregnancy outcomes and an increased risk of developing maternal type 2 diabetes later in life. The mechanisms underlying GDM development are not fully understood. We examined the pathophysiology of GDM through comprehensive metabolic profiling of maternal urine, using participants from a longitudinal cohort of normal pregnancies and pregnancies complicated by GDM.

METHODS

Based on ultra-performance liquid chromatography/hybrid quadrupole time-of-flight mass spectrometry, an untargeted metabolomics study was performed to explore the differences in the urinary metabolome of GDM cases and healthy controls over the course of pregnancy. Multilevel statistical approaches were employed to address the complex metabolomic data obtained from a longitudinal cohort.

RESULTS

The results indicated that tryptophan and purine metabolism was associated with GDM. The tryptophan-kynurenine pathway was activated in the GDM subjects before placental hormones or the fetoplacental unit could have produced any physiological effect. Hypoxanthine, xanthine, xanthosine, and 1-methylhypoxanthine were all elevated in the urine metabolome of subjects with GDM. Catabolism of purine nucleosides leads ultimately to the production of uric acid, which discriminated the subjects with GDM from controls.

CONCLUSIONS

The results support the notion that GDM may be a predisposed condition, or prediabetic state, which is manifested during pregnancy. This challenges the conventional view of the pathogenesis of GDM, which assumes placental hormones are the major causes of insulin resistance in GDM.

Multilevel pharmacokinetics-driven modeling of metabolomics data

INTRODUCTION

Multilevel modeling is a quantitative statistical method to investigate variability and relationships between variables of interest, taking into account population structure and dependencies. It can be used for prediction, data reduction and causal inference from experiments and observational studies allowing for more efficient elucidation of knowledge.

OBJECTIVES

In this study we introduced the concept of multilevel pharmacokinetics (PK)-driven modelling for large-sample, unbalanced and unadjusted metabolomics data comprising nucleoside and creatinine concentration measurements in urine of healthy and cancer patients.

METHODS

A Bayesian multilevel model was proposed to describe the nucleoside and creatinine concentration ratio considering age, sex and health status as covariates. The predictive performance of the proposed model was summarized via area under the ROC, sensitivity and specificity using external validation.

RESULTS

Cancer was associated with an increase in methylthioadenosine/creatinine excretion rate by a factor of 1.42 (1.09-2.03) which constituted the highest increase among all nucleosides. Age influenced nucleosides/creatinine excretion rates for all nucleosides in the same direction which was likely caused by a decrease in creatinine clearance with age. There was a small evidence of sex-related differences for methylthioadenosine. The individual a posteriori prediction of patient classification as area under the ROC with 5th and 95th percentile was 0.57(0.5-0.67) with sensitivity and specificity of 0.59(0.42-0.76) and 0.57(0.45-0.7), respectively suggesting limited usefulness of 13 nucleosides/creatinine urine concentration measurements in predicting disease in this population.

CONCLUSION

Bayesian multilevel pharmacokinetics-driven modeling in metabolomics may be useful in understanding the data and may constitute a new tool for searching towards potential candidates of disease indicators.

Noninvasive Recognition and Biomarkers of Early Allergic Asthma in Cats Using Multivariate Statistical Analysis of NMR Spectra of Exhaled Breath Condensate

Asthma is prevalent in children and cats, and needs means of noninvasive diagnosis. We sought to distinguish noninvasively the differences in 53 cats before and soon after induction of allergic asthma, using NMR spectra of exhaled breath condensate (EBC). Statistical pattern recognition was improved considerably by preprocessing the spectra with probabilistic quotient normalization and glog transformation. Classification of the 106 preprocessed spectra by principal component analysis and partial least squares with discriminant analysis (PLS-DA) appears to be impaired by variances unrelated to eosinophilic asthma. By filtering out confounding variances, orthogonal signal correction (OSC) PLS-DA greatly improved the separation of the healthy and early asthmatic states, attaining 94% specificity and 94% sensitivity in predictions. OSC enhancement of multi-level PLS-DA boosted the specificity of the prediction to 100%. OSC-PLS-DA of the normalized spectra suggest the most promising biomarkers of allergic asthma in cats to include increased acetone, metabolite(s) with overlapped NMR peaks near 5.8 ppm, and a hydroxyphenyl-containing metabolite, as well as decreased phthalate. Acetone is elevated in the EBC of 74% of the cats with early asthma. The noninvasive detection of early experimental asthma, biomarkers in EBC, and metabolic perturbation invite further investigation of the diagnostic potential in humans.

Time-resolved metabolomics analysis of individual differences during the early stage of lipopolysaccharide-treated rats

Lipopolysaccharide (LPS) can lead to uncontrollable cytokine production and eventually cause fatal sepsis syndrome. Individual toxicity difference of LPS has been widely reported. In our study we observed that two thirds of the rats (24/36) died at a given dose of LPS, while the rest (12/36) survived. Tracking the dynamic metabolic change in survival and non-survival rats in the early stage may reveal new system information to understand the inter-individual variation in response to LPS. As the time-resolved datasets are very complex and no single method can elucidate the problem clearly and comprehensively, the static and dynamic metabolomics methods were employed in combination as cross-validation. Intriguingly, some common results have been observed. Lipids were the main different metabolites between survival and non-survival rats in pre-dose serum and in the early stage of infection with LPS. The LPS treatment led to S-adenosly-methionine and total cysteine individual difference in early stage, and subsequent significant perturbations in energy metabolism and oxidative stress. Furthermore, cytokine profiles were analyzed to identify potential biological associations between cytokines and specific metabolites. Our collective findings may provide some heuristic guidance for elucidating the underlying mechanism of individual difference in LPS-mediated disease.

Approaches to Sample Size Determination for Multivariate Data: Applications to PCA and PLS-DA of Omics Data

Sample size determination is a fundamental step in the design of experiments. Methods for sample size determination are abundant for univariate analysis methods, but scarce in the multivariate case. Omics data are multivariate in nature and are commonly investigated using multivariate statistical methods, such as principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA). No simple approaches to sample size determination exist for PCA and PLS-DA. In this paper we will introduce important concepts and offer strategies for (minimally) required sample size estimation when planning experiments to be analyzed using PCA and/or PLS-DA.

Weight loss predictability by plasma metabolic signatures in adults with obesity and morbid obesity of the DiOGenes study

OBJECTIVE

Aim is to predict successful weight loss by metabolic signatures at baseline and to identify which differences in metabolic status may underlie variations in weight loss success.

METHODS

In DiOGenes, a randomized, controlled trial, weight loss was induced using a low-calorie diet (800 kcal) for 8 weeks. Men (N = 236) and women (N = 431) as well as groups with overweight/obesity and morbid obesity were studied separately. The relation between the metabolic status before weight loss and weight loss was assessed by stepwise regression on multiple data sets, including anthropometric parameters, NMR-based plasma metabolites, and LC-MS-based plasma lipid species.

RESULTS

Maximally, 57% of the variation in weight loss success can be predicted by baseline parameters. The most powerful predictive models were obtained in subjects with morbid obesity. In these models, the metabolites most predictive for weight loss were acetoacetate, triacylglycerols, phosphatidylcholines, specific amino acids, and creatine and creatinine. This metabolic profile suggests that high energy metabolism activity results in higher amounts of weight loss.

CONCLUSIONS

Possible predictive (pre-diet) markers were found for amount of weight loss for specific subgroups.

Metabolomics profiling of pre-and post-anesthesia plasma samples of colorectal patients obtained via Ficoll separation

1H NMR spectroscopy was used to investigate the metabolic consequences of general anesthesia in the plasma of two groups of patients with diagnosis for non-metastatic colorectal cancer and metastatic colorectal cancer with liver-metastasis, respectively. Patients were treated with etomidate or propofol, two frequently used sedation agents. Plasma samples were obtained via Ficoll separation. Here, we demonstrated that this procedure introduces a number of limitations for NMR-based metabolomics studies, due to the appearance of spurious signals. Nevertheless, the comparison of the 1H NMR metabolomic profiles of patients treated with etomidate or propofol at equipotent dose ranges was still feasible and proved that both agents significantly decrease the plasma levels of several NMR-detectable metabolites. Consequently, samples collected during anesthesia are not suitable for metabolic profiling studies aimed at patient stratification, because interpersonal variability are reduced by the overall depression of metabolites levels. On the other hand, this study showed that plasma metabolomics could represent a valuable tool to monitor the effect of different sedation agents and/or the individual metabolic response to anesthesia, providing hints for an appropriate tuning of personalized sedation procedures. In our reference groups, the metabolomic signatures were slightly different in patients anesthetized with etomidate versus propofol. The importance of standardized collection procedures and availability of exhaustive metadata of the experimental design for the accurate evaluation of the significance of the observed changes in metabolites levels are critically discussed.

A survey of modulation of gut microbiota by dietary polyphenols

Dietary polyphenols present in a broad range of plant foods have been related to beneficial health effects. This review aims to update the current information about the modulation of the gut microbiota by dietary phenolic compounds, from a perspective based on the experimental approaches used. After referring to general aspects of gut microbiota and dietary polyphenols, studies related to this topic are presented according to their experimental design: batch culture fermentations, gastrointestinal simulators, animal model studies, and human intervention studies. In general, studies evidence that dietary polyphenols may contribute to the maintenance of intestinal health by preserving the gut microbial balance through the stimulation of the growth of beneficial bacteria (i.e., lactobacilli and bifidobacteria) and the inhibition of pathogenic bacteria, exerting prebiotic-like effects. Combination of in vitro and in vivo models could help to understand the underlying mechanisms in the polyphenols-microbiota-host triangle and elucidate the implications of polyphenols on human health. From a technological point of view, supplementation with rich-polyphenolic stuffs (phenolic extracts, phenolic-enriched fractions, etc.) could be an effective option to improve health benefits of functional foods such as the case of dairy fermented foods.

Studies on Modulation of Gut Microbiota by Wine Polyphenols: From Isolated Cultures to Omic Approaches

Moderate consumption of wine seems to produce positive health effects derived from the occurrence of bioactive polyphenols. The gut microbiota is involved in the metabolism of phenolic compounds, and these compounds and/or their metabolites may modulate gut microbiota through the stimulation of the growth of beneficial bacteria and the inhibition of pathogenic bacteria. The characterization of bacterial metabolites derived from polyphenols is essential in order to understand their effects, including microbial modulation, and therefore to associate dietary intake with particular health effects. This review aims to summarize the current information about the two-way "wine polyphenols-gut microbiota" interaction, from a perspective based on the experimental and analytical designs used. The availability of advanced methods for monitoring bacterial communities, along with the combination of in vitro and in vivo models, could help to assess the metabolism of polyphenols in the human body and to monitor total bacterial communities, and, therefore, to elucidate the implications of diet on the modulation of microbiota for delivering health benefits.

Changes in urine headspace composition as an effect of strenuous walking

The present investigation uses proton transfer reaction mass spectrometry (PTR-MS) combined with multivariate and univariate statistical analyses to study potential biomarkers for altered metabolism in urine due to strenuous walking. Urine samples, in concurrence with breath and blood samples, were taken from 51 participants (23 controls, 11 type-1 diabetes, 17 type-2 diabetes) during the Dutch endurance walking event, the International Four Days Marches. Multivariate analysis allowed for discrimination of before and after exercise for all three groups (control, type-1 and type-2 diabetes) and on three out of 4 days. The analysis highlighted 12 molecular ions contributing to this discrimination. Of these, acetic acid in urine is identified as a significant marker for exercise effects induced by walking; an increase is observed as an effect of walking. Analysis of acetone concentration with univariate tools resulted in different information when compared to breath as a function of exercise, revealing an interesting effect of time over the 4 days. In breath, acetone provides an immediate snapshot of metabolism, whereas urinary acetone will result from longer term diffusion processes, providing a time averaged view of metabolism. The potential to use PTR-MS measurements of urine to monitor exercise effects is exhibited, and may be utilized to monitor subjects in mass participation exercise events.

Scaling in ANOVA-simultaneous component analysis

In omics research often high-dimensional data is collected according to an experimental design. Typically, the manipulations involved yield differential effects on subsets of variables. An effective approach to identify those effects is ANOVA-simultaneous component analysis (ASCA), which combines analysis of variance with principal component analysis. So far, pre-treatment in ASCA received hardly any attention, whereas its effects can be huge. In this paper, we describe various strategies for scaling, and identify a rational approach. We present the approaches in matrix algebra terms and illustrate them with an insightful simulated example. We show that scaling directly influences which data aspects are stressed in the analysis, and hence become apparent in the solution. Therefore, the cornerstone for proper scaling is to use a scaling factor that is free from the effect of interest. This implies that proper scaling depends on the effect(s) of interest, and that different types of scaling may be proper for the different effect matrices. We illustrate that different scaling approaches can greatly affect the ASCA interpretation with a real-life example from nutritional research. The principle that scaling factors should be free from the effect of interest generalizes to other statistical methods that involve scaling, as classification methods.

Constrained randomization and multivariate effect projections improve information extraction and biomarker pattern discovery in metabolomics studies involving dependent samples

Analytical drift is a major source of bias in mass spectrometry based metabolomics confounding interpretation and biomarker detection. So far, standard protocols for sample and data analysis have not been able to fully resolve this. We present a combined approach for minimizing the influence of analytical drift on multivariate comparisons of matched or dependent samples in mass spectrometry based metabolomics studies. The approach is building on a randomization procedure for sample run order, constrained to independent randomizations between and within dependent sample pairs (e.g. pre/post intervention). This is followed by a novel multivariate statistical analysis strategy allowing paired or dependent analyses of individual effects named OPLS-effect projections (OPLS-EP). We show, using simulated data that OPLS-EP gives improved interpretation over existing methods and that constrained randomization of sample run order in combination with an appropriate dependent statistical test increase the accuracy and sensitivity and decrease the false omission rate in biomarker detection. We verify these findings and prove the strength of the suggested approach in a clinical data set consisting of LC/MS data of blood plasma samples from patients before and after radical prostatectomy. Here OPLS-EP compared to traditional (independent) OPLS-discriminant analysis (OPLS-DA) on constrained randomized data gives a less complex model (3 versus 5 components) as well a higher predictive ability (Q2 = 0.80 versus Q2 = 0.55). We explain this by showing that paired statistical analysis detects 37 unique significant metabolites that were masked for the independent test due to bias, including analytical drift and inter-individual variation.

Towards the fecal metabolome derived from moderate red wine intake

Dietary polyphenols, including red wine phenolic compounds, are extensively metabolized during their passage through the gastrointestinal tract; and their biological effects at the gut level (i.e., anti-inflammatory activity, microbiota modulation, interaction with cells, among others) seem to be due more to their microbial-derived metabolites rather than to the original forms found in food. In an effort to improve our understanding of the biological effects that phenolic compounds exert at the gut level, this paper summarizes the changes observed in the human fecal metabolome after an intervention study consisting of a daily consumption of 250 mL of wine during four weeks by healthy volunteers (n = 33). It assembles data from two analytical approaches: (1) UPLC-ESI-MS/MS analysis of phenolic metabolites in fecal solutions (targeted analysis); and (2) UHPLC-TOF MS analysis of the fecal solutions (non-targeted analysis). Both approaches revealed statistically-significant changes in the concentration of several metabolites as a consequence of the wine intake. Similarity and complementarity between targeted and non-targeted approaches in the analysis of the fecal metabolome are discussed. Both strategies allowed the definition of a complex metabolic profile derived from wine intake. Likewise, the identification of endogenous markers could lead to new hypotheses to unravel the relationship between moderate wine consumption and the metabolic functionality of gut microbiota.

Pharmacometabolomics reveals that serotonin is implicated in aspirin response variability

While aspirin is generally effective for prevention of cardiovascular disease, considerable variation in drug response exists, resulting in some individuals displaying high on-treatment platelet reactivity. We used pharmacometabolomics to define pathways implicated in variation of response to treatment. We profiled serum samples from healthy subjects pre- and postaspirin (14 days, 81 mg/day) using mass spectrometry. We established a strong signature of aspirin exposure independent of response (15/34 metabolites changed). In our discovery (N = 80) and replication (N = 125) cohorts, higher serotonin levels pre- and postaspirin correlated with high, postaspirin, collagen-induced platelet aggregation. In a third cohort, platelets from subjects with the highest levels of serotonin preaspirin retained higher reactivity after incubation with aspirin than platelets from subjects with the lowest serotonin levels preaspirin (72 ± 8 vs. 61 ± 11%, P = 0.02, N = 20). Finally, ex vivo, serotonin strongly increased platelet reactivity after platelet incubation with aspirin (+20%, P = 4.9 × 10⁻⁴, N = 12). These results suggest that serotonin is implicated in aspirin response variability.

The food metabolome: a window over dietary exposure

The food metabolome is defined as the part of the human metabolome directly derived from the digestion and biotransformation of foods and their constituents. With >25,000 compounds known in various foods, the food metabolome is extremely complex, with a composition varying widely according to the diet. By its very nature it represents a considerable and still largely unexploited source of novel dietary biomarkers that could be used to measure dietary exposures with a high level of detail and precision. Most dietary biomarkers currently have been identified on the basis of our knowledge of food compositions by using hypothesis-driven approaches. However, the rapid development of metabolomics resulting from the development of highly sensitive modern analytic instruments, the availability of metabolite databases, and progress in (bio)informatics has made agnostic approaches more attractive as shown by the recent identification of novel biomarkers of intakes for fruit, vegetables, beverages, meats, or complex diets. Moreover, examples also show how the scrutiny of the food metabolome can lead to the discovery of bioactive molecules and dietary factors associated with diseases. However, researchers still face hurdles, which slow progress and need to be resolved to bring this emerging field of research to maturity. These limits were discussed during the First International Workshop on the Food Metabolome held in Glasgow. Key recommendations made during the workshop included more coordination of efforts; development of new databases, software tools, and chemical libraries for the food metabolome; and shared repositories of metabolomic data. Once achieved, major progress can be expected toward a better understanding of the complex interactions between diet and human health.

A metabolomic perspective on coeliac disease

Metabolomics is an "omic" science that is now emerging with the purpose of elaborating a comprehensive analysis of the metabolome, which is the complete set of metabolites (i.e., small molecules intermediates) in an organism, tissue, cell, or biofluid. In the past decade, metabolomics has already proved to be useful for the characterization of several pathological conditions and offers promises as a clinical tool. A metabolomics investigation of coeliac disease (CD) revealed that a metabolic fingerprint for CD can be defined, which accounts for three different but complementary components: malabsorption, energy metabolism, and alterations in gut microflora and/or intestinal permeability. In this review, we will discuss the major advancements in metabolomics of CD, in particular with respect to the role of gut microbiome and energy metabolism.

Nutri-metabolomics: subtle serum metabolic differences in healthy subjects by NMR-based metabolomics after a short-term nutritional intervention with two tomato sauces

Postgenomics research and development is witnessing novel intersections of omics data intensive technology and applications in health and personalized nutrition. Chief among these is the nascent field of nutri-metabolomics that harnesses metabolomics platforms to discern person-to-person variations in nutritional responses. To this end, differences in the origin and ripening stage of fruits might have a strong impact on their phytochemical composition, and consequently, on their potential nutri-metabolomics effects on health. The objective of the present study was to evaluate the effects of a 4-week cross-over nutritional intervention on the metabolic status of 24 young healthy subjects. The intervention was carried out with two tomato sauces differing in their natural lycopene content, which was achieved by using tomatoes harvested at different times. Blood samples were drawn from each subject before and after each intervention period. Aqueous and lipid extracts from serum samples were analyzed by 1H-NMR metabolic profiling combined with analysis of variance simultaneous component analysis (ASCA) and multilevel simultaneous component analysis (MSCA). These methods allowed the interpretation of the variation induced by the main factors of the study design (sauce treatment and time). The levels of creatine, creatinine, leucine, choline, methionine, and acetate in aqueous extracts were increased after the intervention with the high-lycopene content sauce, while those of ascorbic acid, lactate, pyruvate, isoleucine, alanine were increased after the normal-lycopene content sauce. In conclusion, NMR-based metabolomics of aqueous and lipid extracts allowed the detection of different metabolic changes after the nutritional intervention. This outcome might partly be due to the different ripening state of the fruits used in production of the tomato sauces. The findings presented herein collectively attest to the emergence of the field of nutri-metabolomics as a novel subspecialty of postgenomics integrative biology.

Altered colonic mucosal Polyunsaturated Fatty Acid (PUFA) derived lipid mediators in ulcerative colitis: new insight into relationship with disease activity and pathophysiology

Objectives

Ulcerative colitis (UC) is a relapsing inflammatory disorder of unconfirmed aetiology, variable severity and clinical course, characterised by progressive histological inflammation and with elevation of eicosanoids which have a known pathophysiological role in inflammation. Therapeutic interventions targetting eicosanoids (5-aminosalicylates (ASA)) are effective first line and adjunctive treatments in mild-moderate UC for achieving and sustaining clinical remission. However, the variable clinical response to 5-ASA and frequent deterioration in response to cyclo-oxygenase (COX) inhibitors, has prompted an in depth simultaneous evaluation of multiple lipid mediators (including eicosanoids) within the inflammatory milieu in UC. We hypothesised that severity of inflammation is associated with alteration of lipid mediators, in relapsing UC.

Design

Study was case-control design. Mucosal lipid mediators were determined by LC-MS/MS lipidomics analysis on mucosal biopsies taken from patients attending outpatients with relapsing UC. Univariate and multivariate statistical analyses were used to investigate the association of mucosal lipid mediators, with the disease state and severity graded histologically.

Results

Levels of PGE₂, PGD₂, TXB₂, 5-HETE, 11-HETE, 12-HETE and 15-HETE are significantly elevated in inflamed mucosa and correlate with severity of inflammation, determined using validated histological scoring systems.

Conclusions

Our approach of capturing inflammatory mediator signature at different stages of UC by combining comprehensive lipidomics analysis and computational modelling could be used to classify and predict mild-moderate inflammation; however, predictive index is diminished in severe inflammation. This new technical approach could be developed to tailor drug treatments to patients with active UC, based on the mucosal lipid mediator profile.

Acute and potentially persistent effects of scuba diving on the blood transcriptome of experienced divers

During scuba diving, the circulatory system is stressed by an elevated partial pressure of oxygen while the diver is submerged and by decompression-induced gas bubbles on ascent to the surface. This diving-induced stress may trigger decompression illness, but the majority of dives are asymptomatic. In this study we have mapped divers' blood transcriptomes with the aim of identifying genes, biological pathways, and cell types perturbed by the physiological stress in asymptomatic scuba diving. Ten experienced divers abstained from diving for >2 wk before performing a 3-day series of daily dives to 18 m depth for 47 min while breathing compressed air. Blood for microarray analysis was collected before and immediately after the first and last dives, and 10 matched nondivers provided controls for predive stationary transcriptomes. MetaCore GeneGo analysis of the predive samples identified stationary upregulation of genes associated with apoptosis, inflammation, and innate immune responses in the divers, most significantly involving genes in the TNFR1 pathway of caspase-dependent apoptosis, HSP60/HSP70 signaling via TLR4, and NF-κB-mediated transcription. Diving caused pronounced shifts in transcription patterns characteristic of specific leukocytes, with downregulation of genes expressed by CD8+ T lymphocytes and NK cells and upregulation of genes expressed by neutrophils, monocytes, and macrophages. Antioxidant genes were upregulated. Similar transient responses were observed after the first and last dive. The results indicate that sublethal oxidative stress elicits the myeloid innate immune system in scuba diving and that extensive diving may cause persistent change in pathways controlling apoptosis, inflammation, and innate immune responses.

Gender-dependent associations of metabolite profiles and body fat distribution in a healthy population with central obesity: towards metabolomics diagnostics

Obesity is a risk factor for cardiovascular diseases and type 2 diabetes especially when the fat is accumulated to central depots. Novel biomarkers are crucial to develop diagnostics for obesity and related metabolic disorders. We evaluated the associations between metabolite profiles (136 lipid components, 12 lipoprotein subclasses, 17 low-molecular-weight metabolites, 12 clinical markers) and 28 phenotype parameters (including different body fat distribution parameters such as android (A), gynoid (G), abdominal visceral (VAT), subcutaneous (SAT) fat) in 215 plasma/serum samples from healthy overweight men (n=32) and women (n=83) with central obesity. (Partial) correlation analysis and partial least squares (PLS) regression analysis showed that only specific metabolites were associated to A:G ratio, VAT, and SAT, respectively. These association patterns were gender dependent. For example, insulin, cholesterol, VLDL, and certain triacylglycerols (TG 54:1-3) correlated to VAT in women, while in men VAT was associated with TG 50:1-5, TG 55:1, phosphatidylcholine (PC 32:0), and VLDL ((X)L). Moreover, multiple regression analysis revealed that waist circumference and total fat were sufficient to predict VAT and SAT in women. In contrast, only VAT but not SAT could be predicted in men and only when plasma metabolites were included, with PC 32:0 being most strongly associated with VAT. These findings collectively highlight the potential of metabolomics in obesity and that gender differences need to be taken into account for novel biomarker and diagnostic discovery for obesity and metabolic disorders.

Metabolomics as a potential new approach for investigating human reproductive disorders

Metabolomics has been emerging for several years as a global chemical phenotyping approach offering fascinating descriptive capabilities for addressing life complexity. It facilitates the understanding of the mechanisms of biological and biochemical processes in complex systems and promises new insights into specific research questions. The objective of this study was to use for the first time a metabolomic approach based on liquid chromatography high resolution mass spectrometry for characterizing an alteration of the testicular function, namely impaired semen quality. Metabolomic fingerprints were generated from serum samples collected from Danish young men presenting low, intermediate, or high sperm concentrations. Serum metabolic profiles were found to be significantly different among the three groups of volunteers. The developed methodology permitted to correlate the studied clinical parameter (i.e., sperm concentration) with the metabolite profiles generated. Peptides related to the Protein Complement C3f were identified as putative markers associated with this clinical parameter. The biological interpretation and further robustness linked to this observation remain to be further investigated, in particular to address the inter- and intraindividual variabilities.

Data Analysis and Interpretation in Metabolomics

Metabolomics represents the new ‘omics’ approach of the functional genomics era. It consists in the identification and quantification of all small molecules, namely metabolites, in a given biological system. While metabolomics refers to the analysis of any possible biological system, metabonomics is specifically applied to disease and physiopathological situations. The data collected within these approaches is highly integrative of the other higher levels and is hence amenable to be explored with a top-down systems biology point of view. The aim of this chapter is to give a global view of the state of the art in metabolomics describing the two analytical techniques usually used to give rise to this kind of data, nuclear magnetic resonance, NMR, and mass spectrometry. In addition, the author will focus on the different data analysis tools that can be applied to such studies to extract information with special interest at the attempts to integrate metabolomics with other ‘omics’ approaches and its relevance in systems biology modeling.

Towards polypharmacokinetics: pharmacokinetics of multicomponent drugs and herbal medicines using a metabolomics approach

Determination of pharmacokinetics (PKs) of multicomponent pharmaceuticals and/or nutraceuticals (polypharmacokinetics, poly-PKs) is difficult due to the vast number of compounds present in natural products, their various concentrations across a wide range, complexity of their interactions, as well as their complex degradation dynamics in vivo. Metabolomics coupled with multivariate statistical tools that focus on the comprehensive analysis of small molecules in biofluids is a viable approach to address the challenges of poly-PK. This paper discusses recent advances in the characterization of poly-PK and the metabolism of multicomponent xenobiotic agents, such as compound drugs, dietary supplements, and herbal medicines, using metabolomics strategy. We propose a research framework that integrates the dynamic concentration profile of bioavailable xenobiotic molecules that result from in vivo absorption and hepatic and gut bacterial metabolism, as well as the human metabolic response profile. This framework will address the bottleneck problem in the pharmacological evaluation of multicomponent pharmaceuticals and nutraceuticals, leading to the direct elucidation of the pharmacological and molecular mechanisms of these compounds.

Six weeks of a polarized training-intensity distribution leads to greater physiological and performance adaptations than a threshold model in trained cyclists

This study was undertaken to investigate physiological adaptation with two endurance-training periods differing in intensity distribution. In a randomized crossover fashion, separated by 4 wk of detraining, 12 male cyclists completed two 6-wk training periods: 1) a polarized model [6.4 (±1.4 SD) h/wk; 80%, 0%, and 20% of training time in low-, moderate-, and high-intensity zones, respectively]; and 2) a threshold model [7.5 (±2.0 SD) h/wk; 57%, 43%, and 0% training-intensity distribution]. Before and after each training period, following 2 days of diet and exercise control, fasted skeletal muscle biopsies were obtained for mitochondrial enzyme activity and monocarboxylate transporter (MCT) 1 and 4 expression, and morning first-void urine samples were collected for NMR spectroscopy-based metabolomics analysis. Endurance performance (40-km time trial), incremental exercise, peak power output (PPO), and high-intensity exercise capacity (95% maximal work rate to exhaustion) were also assessed. Endurance performance, PPOs, lactate threshold (LT), MCT4, and high-intensity exercise capacity all increased over both training periods. Improvements were greater following polarized rather than threshold for PPO [mean (±SE) change of 8 (±2)% vs. 3 (±1)%, P < 0.05], LT [9 (±3)% vs. 2 (±4)%, P < 0.05], and high-intensity exercise capacity [85 (±14)% vs. 37 (±14)%, P < 0.05]. No changes in mitochondrial enzyme activities or MCT1 were observed following training. A significant multilevel, partial least squares-discriminant analysis model was obtained for the threshold model but not the polarized model in the metabolomics analysis. A polarized training distribution results in greater systemic adaptation over 6 wk in already well-trained cyclists. Markers of muscle metabolic adaptation are largely unchanged, but metabolomics markers suggest different cellular metabolic stress that requires further investigation.

A novel approach for biomarker selection and the integration of repeated measures experiments from two assays

BACKGROUND

High throughput 'omics' experiments are usually designed to compare changes observed between different conditions (or interventions) and to identify biomarkers capable of characterizing each condition. We consider the complex structure of repeated measurements from different assays where different conditions are applied on the same subjects.

RESULTS

We propose a two-step analysis combining a multilevel approach and a multivariate approach to reveal separately the effects of conditions within subjects from the biological variation between subjects. The approach is extended to two-factor designs and to the integration of two matched data sets. It allows internal variable selection to highlight genes able to discriminate the net condition effect within subjects. A simulation study was performed to demonstrate the good performance of the multilevel multivariate approach compared to a classical multivariate method. The multilevel multivariate approach outperformed the classical multivariate approach with respect to the classification error rate and the selection of relevant genes. The approach was applied to an HIV-vaccine trial evaluating the response with gene expression and cytokine secretion. The discriminant multilevel analysis selected a relevant subset of genes while the integrative multilevel analysis highlighted clusters of genes and cytokines that were highly correlated across the samples.

CONCLUSIONS

Our combined multilevel multivariate approach may help in finding signatures of vaccine effect and allows for a better understanding of immunological mechanisms activated by the intervention. The integrative analysis revealed clusters of genes, that were associated with cytokine secretion. These clusters can be seen as gene signatures to predict future cytokine response. The approach is implemented in the R package mixOmics (http://cran.r-project.org/) with associated tutorials to perform the analysis(a).

(1)H-NMR-based metabolomic analysis of the effect of moderate wine consumption on subjects with cardiovascular risk factors

Moderate wine consumption is associated with health-promoting activities. An H-NMR-based metabolomic approach was used to identify urinary metabolomic differences of moderate wine intake in the setting of a prospective, randomized, crossover, and controlled trial. Sixty-one male volunteers with high cardiovascular risk factors followed three dietary interventions (28 days): dealcoholized red wine (RWD) (272mL/day, polyphenol control), alcoholized red wine (RWA) (272mL/day) and gin (GIN) (100mL/day, alcohol control). After each period, 24-h urine samples were collected and analyzed by (1) H-NMR. According to the results of a one-way ANOVA, significant markers were grouped in four categories: alcohol-related markers (ethanol); gin-related markers; wine-related markers; and gut microbiota markers (hippurate and 4-hydroxphenylacetic acid). Wine metabolites were classified into two groups; first, metabolites of food metabolome: tartrate (RWA and RWD), ethanol, and mannitol (RWA); and second, biomarkers that relates to endogenous modifications after wine consumption, comprising branched-chain amino acid (BCAA) metabolite (3-methyl-oxovalerate). Additionally, a possible interaction between alcohol and gut-related biomarkers has been identified. To our knowledge, this is the first time that this approach has been applied in a nutritional intervention with red wine. The results show the capacity of this approach to obtain a comprehensive metabolome picture including food metabolome and endogenous biomarkers of moderate wine intake.

Metabolomics reveals the metabolic shifts following an intervention with rye bread in postmenopausal women--a randomized control trial

Background

Epidemiological studies have consistently shown that whole grain (WG) cereals can protect against the development of chronic diseases, but the underlying mechanism is not fully understood. Among WG products, WG rye is considered even more potent because of its unique discrepancy in postprandial insulin and glucose responses known as the rye factor. In this study, an NMR-based metabolomics approach was applied to study the metabolic effects of WG rye as a tool to determine the beneficial effects of WG rye on human health.

Methods

Thirty-three postmenopausal Finnish women with elevated serum total cholesterol (5.0-8.5 mmol/L) and BMI of 20–33 kg/m² consumed a minimum of 20% of their daily energy intake as high fiber WG rye bread (RB) or refined wheat bread (WB) in a randomized, controlled, crossover design with two 8-wk intervention periods separated by an 8-wk washout period. At the end of each intervention period, fasting serum was collected for NMR-based metabolomics and the analysis of cholesterol fractions. Multilevel partial least squares discriminant analysis was used for paired comparisons of multivariate data.

Results

The metabolomics analysis of serum showed lower leucine and isoleucine and higher betaine and N,N-dimethylglycine levels after RB than WB intake. To further investigate the metabolic effects of RB, the serum cholesterol fractions were measured. Total- and LDL-cholesterol levels were higher after RB intake than after WB (p<0.05).

Conclusions

This study revealed favorable shifts in branched amino acid and single carbon metabolism and an unfavorable shift in serum cholesterol levels after RB intake in postmenopausal women, which should be considered for evaluating health beneficial effects of rye products.

Current status on genome-metabolome-wide associations: an opportunity in nutrition research

Genome-wide association studies (GWASs) have become a very important tool to address the genetic origin of phenotypic variability, in particular associated with diseases. Nevertheless, these types of studies provide limited information about disease etiology and the molecular mechanisms involved. Recently, the incorporation of metabolomics into the analysis has offered novel opportunities for a better understanding of disease-related metabolic deregulation. The pattern emerging from this work is that gene-driven changes in metabolism are prevalent and that common genetic variations can have a profound impact on the homeostatic concentrations of specific metabolites. A particularly interesting aspect of this work takes into account interactions of environment and lifestyle with the genome and how this interaction translates into changes in the metabolome. For instance, the role of PYROXD2 in trimethylamine metabolism points to an interaction between host and microbiome genomes (host/microbiota). Often, these findings reveal metabolic deregulations, which could eventually be tuned with a nutritional intervention. Here we review the development of gene-metabolism association studies from a single-gene/single-metabolite to a genome-wide/metabolome-wide approach and highlight the conceptual changes associated with this ongoing transition. Moreover, we report some of our recent GWAS results on a cohort of 265 individuals from an ethnically diverse population that validate and refine previous findings on gene-urine metabolism interactions. Specifically, our results confirm the effect of PYROXD2 polymorphisms on trimethylamine metabolism and suggest that a previously reported association of N-acetylated compounds with the ALMS1/NAT8 locus is driven by SNPs in the ALMS1 gene.