Statistical Recoupling Prior to Significance Testing in Nuclear Magnetic Resonance Based Metabonomics

SPA-STOCSY: an automated tool for identifying annotated and non-annotated metabolites in high-throughput NMR spectra

MOTIVATION

Nuclear magnetic resonance spectroscopy (NMR) is widely used to analyze metabolites in biological samples, but the analysis requires specific expertise, it is time-consuming, and can be inaccurate. Here, we present a powerful automate tool, SPatial clustering Algorithm-Statistical TOtal Correlation SpectroscopY (SPA-STOCSY), which overcomes challenges faced when analyzing NMR data and identifies metabolites in a sample with high accuracy.

RESULTS

As a data-driven method, SPA-STOCSY estimates all parameters from the input dataset. It first investigates the covariance pattern among datapoints and then calculates the optimal threshold with which to cluster datapoints belonging to the same structural unit, i.e. the metabolite. Generated clusters are then automatically linked to a metabolite library to identify candidates. To assess SPA-STOCSY's efficiency and accuracy, we applied it to synthesized spectra and spectra acquired on Drosophila melanogaster tissue and human embryonic stem cells. In the synthesized spectra, SPA outperformed Statistical Recoupling of Variables (SRV), an existing method for clustering spectral peaks, by capturing a higher percentage of the signal regions and the close-to-zero noise regions. In the biological data, SPA-STOCSY performed comparably to the operator-based Chenomx analysis while avoiding operator bias, and it required <7 min of total computation time. Overall, SPA-STOCSY is a fast, accurate, and unbiased tool for untargeted analysis of metabolites in the NMR spectra. It may thus accelerate the use of NMR for scientific discoveries, medical diagnostics, and patient-specific decision making.

AVAILABILITY AND IMPLEMENTATION

The codes of SPA-STOCSY are available at https://github.com/LiuzLab/SPA-STOCSY.

Plasma metabolomics reveals major changes in carbohydrate, lipid, and protein metabolism of abruptly weaned beef calves

¹H NMR-based metabolomics was used to study the effect of abrupt weaning on the blood metabolome of beef calves. Twenty Angus calves (258 ± 5 kg BW; 5 to 6 months old) were randomly assigned to a non-weaned (NW) group that remained grazing with their dam or a weaned (W) group that underwent abrupt separation from their dam to a separate paddock on d 0 of the study. Body weight, behaviour, and blood samples for cortisol and metabolomics were measured at d 0, 1, 2, 7, and 14 of the study. On d 1 and 2, W calves spent less time grazing and ruminating, and more time vocalising and walking, had a greater concentration of cortisol, NEFA, 3-hydroxybutyrate, betaine, creatine, and phenylalanine, and lesser abundance of tyrosine (P < 0.05) compared to NW calves. Compared to NW calves at d 14, W calves had greater (P < 0.01) relative abundance of acetate, glucose, allantoin, creatinine, creatine, creatine phosphate, glutamate, 3-hydroxybutyrate, 3-hydroxyisobutyrate, and seven AA (alanine, glutamate, leucine, lysine, phenylalanine, threonine and valine) but lesser (P < 0.05) relative abundance of low density and very low-density lipids, and unsaturated lipids. Both PCA and OPLS-DA showed no clustering or discrimination between groups at d 0 and increasing divergence to d 14. Blood metabolomics is a useful tool to quantify the acute effects of stress in calves during the first 2 days after abrupt weaning, and longer-term changes in carbohydrate, lipid and protein metabolism due to nutritional changes from cessation of milk intake and greater reliance on forage intake.

SPA-STOCSY: An Automated Tool for Identification of Annotated and Non-Annotated Metabolites in High-Throughput NMR Spectra

Nuclear Magnetic Resonance (NMR) spectroscopy is widely used to analyze metabolites in biological samples, but the analysis can be cumbersome and inaccurate. Here, we present a powerful automated tool, SPA-STOCSY (Spatial Clustering Algorithm - Statistical Total Correlation Spectroscopy), which overcomes the challenges by identifying metabolites in each sample with high accuracy. As a data-driven method, SPA-STOCSY estimates all parameters from the input dataset, first investigating the covariance pattern and then calculating the optimal threshold with which to cluster data points belonging to the same structural unit, i.e. metabolite. The generated clusters are then automatically linked to a compound library to identify candidates. To assess SPA-STOCSY’s efficiency and accuracy, we applied it to synthesized and real NMR data obtained from Drosophila melanogaster brains and human embryonic stem cells. In the synthesized spectra, SPA outperforms Statistical Recoupling of Variables, an existing method for clustering spectral peaks, by capturing a higher percentage of the signal regions and the close-to-zero noise regions. In the real spectra, SPA-STOCSY performs comparably to operator-based Chenomx analysis but avoids operator bias and performs the analyses in less than seven minutes of total computation time. Overall, SPA-STOCSY is a fast, accurate, and unbiased tool for untargeted analysis of metabolites in the NMR spectra. As such, it might accelerate the utilization of NMR for scientific discoveries, medical diagnostics, and patient-specific decision making.

Bucket Fuser: Statistical Signal Extraction for 1D 1H NMR Metabolomic Data

Untargeted metabolomics is a promising tool for identifying novel disease biomarkers and unraveling underlying pathomechanisms. Nuclear magnetic resonance (NMR) spectroscopy is particularly suited for large-scale untargeted metabolomics studies due to its high reproducibility and cost effectiveness. Here, one-dimensional (1D) 1H NMR experiments offer good sensitivity at reasonable measurement times. Their subsequent data analysis requires sophisticated data preprocessing steps, including the extraction of NMR features corresponding to specific metabolites. We developed a novel 1D NMR feature extraction procedure, called Bucket Fuser (BF), which is based on a regularized regression framework with fused group LASSO terms. The performance of the BF procedure was demonstrated using three independent NMR datasets and was benchmarked against existing state-of-the-art NMR feature extraction methods. BF dynamically constructs NMR metabolite features, the widths of which can be adjusted via a regularization parameter. BF consistently improved metabolite signal extraction, as demonstrated by our correlation analyses with absolutely quantified metabolites. It also yielded a higher proportion of statistically significant metabolite features in our differential metabolite analyses. The BF algorithm is computationally efficient and it can deal with small sample sizes. In summary, the Bucket Fuser algorithm, which is available as a supplementary python code, facilitates the fast and dynamic extraction of 1D NMR signals for the improved detection of metabolic biomarkers.

Human and preclinical studies of the host-gut microbiome co-metabolite hippurate as a marker and mediator of metabolic health

OBJECTIVE

Gut microbial products are involved in regulation of host metabolism. In human and experimental studies, we explored the potential role of hippurate, a hepatic phase 2 conjugation product of microbial benzoate, as a marker and mediator of metabolic health.

DESIGN

In 271 middle-aged non-diabetic Danish individuals, who were stratified on habitual dietary intake, we applied ¹H-nuclear magnetic resonance (NMR) spectroscopy of urine samples and shotgun-sequencing-based metagenomics of the gut microbiome to explore links between the urine level of hippurate, measures of the gut microbiome, dietary fat and markers of metabolic health. In mechanistic experiments with chronic subcutaneous infusion of hippurate to high-fat-diet-fed obese mice, we tested for causality between hippurate and metabolic phenotypes.

RESULTS

In the human study, we showed that urine hippurate positively associates with microbial gene richness and functional modules for microbial benzoate biosynthetic pathways, one of which is less prevalent in the Bacteroides 2 enterotype compared with Ruminococcaceae or Prevotella enterotypes. Through dietary stratification, we identify a subset of study participants consuming a diet rich in saturated fat in which urine hippurate concentration, independently of gene richness, accounts for links with metabolic health. In the high-fat-fed mice experiments, we demonstrate causality through chronic infusion of hippurate (20 nmol/day) resulting in improved glucose tolerance and enhanced insulin secretion.

CONCLUSION

Our human and experimental studies show that a high urine hippurate concentration is a general marker of metabolic health, and in the context of obesity induced by high-fat diets, hippurate contributes to metabolic improvements, highlighting its potential as a mediator of metabolic health.

Statistical analysis in metabolic phenotyping

Metabolic phenotyping is an important tool in translational biomedical research. The advanced analytical technologies commonly used for phenotyping, including mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy, generate complex data requiring tailored statistical analysis methods. Detailed protocols have been published for data acquisition by liquid NMR, solid-state NMR, ultra-performance liquid chromatography (LC-)MS and gas chromatography (GC-)MS on biofluids or tissues and their preprocessing. Here we propose an efficient protocol (guidelines and software) for statistical analysis of metabolic data generated by these methods. Code for all steps is provided, and no prior coding skill is necessary. We offer efficient solutions for the different steps required within the complete phenotyping data analytics workflow: scaling, normalization, outlier detection, multivariate analysis to explore and model study-related effects, selection of candidate biomarkers, validation, multiple testing correction and performance evaluation of statistical models. We also provide a statistical power calculation algorithm and safeguards to ensure robust and meaningful experimental designs that deliver reliable results. We exemplify the protocol with a two-group classification study and data from an epidemiological cohort; however, the protocol can be easily modified to cover a wider range of experimental designs or incorporate different modeling approaches. This protocol describes a minimal set of analyses needed to rigorously investigate typical datasets encountered in metabolic phenotyping.

Chronic Kidney Disease Cohort Studies: A Guide to Metabolome Analyses

Kidney diseases still pose one of the biggest challenges for global health, and their heterogeneity and often high comorbidity load seriously hinders the unraveling of their underlying pathomechanisms and the delivery of optimal patient care. Metabolomics, the quantitative study of small organic compounds, called metabolites, in a biological specimen, is gaining more and more importance in nephrology research. Conducting a metabolomics study in human kidney disease cohorts, however, requires thorough knowledge about the key workflow steps: study planning, sample collection, metabolomics data acquisition and preprocessing, statistical/bioinformatics data analysis, and results interpretation within a biomedical context. This review provides a guide for future metabolomics studies in human kidney disease cohorts. We will offer an overview of important a priori considerations for metabolomics cohort studies, available analytical as well as statistical/bioinformatics data analysis techniques, and subsequent interpretation of metabolic findings. We will further point out potential research questions for metabolomics studies in the context of kidney diseases and summarize the main results and data availability of important studies already conducted in this field.

Oral pre-treatment with thiocyanate (SCN-) protects against myocardial ischaemia-reperfusion injury in rats

Despite improvements in revascularization after a myocardial infarction, coronary disease remains a major contributor to global mortality. Neutrophil infiltration and activation contributes to tissue damage, via the release of myeloperoxidase (MPO) and formation of the damaging oxidant hypochlorous acid. We hypothesized that elevation of thiocyanate ions (SCN⁻), a competitive MPO substrate, would modulate tissue damage. Oral dosing of rats with SCN⁻, before acute ischemia–reperfusion injury (30 min occlusion, 24 h or 4 week recovery), significantly reduced the infarct size as a percentage of the total reperfused area (54% versus 74%), and increased the salvageable area (46% versus 26%) as determined by MRI imaging. No difference was observed in fractional shortening, but supplementation resulted in both left-ventricle end diastolic and left-ventricle end systolic areas returning to control levels, as determined by echocardiography. Supplementation also decreased antibody recognition of HOCl-damaged myocardial proteins. SCN⁻ supplementation did not modulate serum markers of damage/inflammation (ANP, BNP, galectin-3, CRP), but returned metabolomic abnormalities (reductions in histidine, creatine and leucine by 0.83-, 0.84- and 0.89-fold, respectively), determined by NMR, to control levels. These data indicate that elevated levels of the MPO substrate SCN⁻, which can be readily modulated by dietary means, can protect against acute ischemia–reperfusion injury.

Changes in the blood metabolome of Wagyu crossbred steers with time in the feedlot and relationships with marbling

Wagyu crossbred steers (n = 167) were used to (1) compare the metabolome of individual animals at two distant time-points (days 196 and 432) in a feedlot (this corresponded to 272 and 36 days before slaughter); and (2) determine relationships between the metabolome and marbling, and the effect of days in the feedlot (time-points) on these relationships. ¹H NMR spectroscopy followed by standard recoupling of variables analysis produced 290 features or 'peaks' from which 38 metabolites were identified. There was a positive correlation between the relative concentration (RC) at days 196 and 432 for 35 of 38 metabolites (P > 0.05). The RC of 21 metabolites mostly involved in muscle energy and glucose metabolism increased (P < 0.05) from day 196 to 432, and the RC of 13 metabolites mostly involved in lipid metabolism decreased (P < 0.05). There were 14 metabolites correlated with marbling including metabolites involved in energy and fat metabolism (glucose, propionate, 3-hydroxybutyrate, lipids). The relationship between marbling and the RC of metabolites was affected by time-point, being positive for 3-hydroxybutyrate and acetate (P < 0.05) at day 432 but not at day 196. The findings indicate that the blood metabolome in Wagyu crossbred steers changes with time in a feedlot. Notwithstanding, the metabolome has potential to predict marbling in Wagyu. The ability to predict marbling from the blood metabolome appears to be influenced by days in a feedlot and presumably the stage of development towards a mature body conformation.

pJRES Binning Algorithm (JBA): a new method to facilitate the recovery of metabolic information from pJRES 1H NMR spectra

Motivation

Data processing is a key bottleneck for ¹H NMR-based metabolic profiling of complex biological mixtures, such as biofluids. These spectra typically contain several thousands of signals, corresponding to possibly few hundreds of metabolites. A number of binning-based methods have been proposed to reduce the dimensionality of 1 D ¹H NMR datasets, including statistical recoupling of variables (SRV). Here, we introduce a new binning method, named JBA (“pJRES Binning Algorithm”), which aims to extend the applicability of SRV to pJRES spectra.

Results

The performance of JBA is comprehensively evaluated using 617 plasma ¹H NMR spectra from the FGENTCARD cohort. The results presented here show that JBA exhibits higher sensitivity than SRV to detect peaks from low-abundance metabolites. In addition, JBA allows a more efficient removal of spectral variables corresponding to pure electronic noise, and this has a positive impact on multivariate model building

Availability and implementation

The algorithm is implemented using the MWASTools R/Bioconductor package.

Supplementary information

Supplementary data are available at Bioinformatics online.

Multi-platform NMR Study of Pluripotent Stem Cells Unveils Complementary Metabolic Signatures towards Differentiation

Stem cells, poised to revolutionize current medicine, stand as major workhorses for monitoring changes in cell fate. Characterizing metabolic phenotypes is key to monitor in differentiating cells transcriptional and epigenetic shifts at a functional level and provides a non-genetic means to control cell specification. Expanding the arsenal of analytical tools for metabolic profiling of cell differentiation is therefore of importance. Here, we describe the metabolome of whole pluripotent stem cells (PSCs) using high‐resolution magic angle spinning (HR-MAS), a non-destructive approach for Nuclear Magnetic Resonance (NMR) analysis. The integrated ¹H NMR analysis results in detection of metabolites of various groups, including energy metabolites, amino acids, choline derivatives and short chain fatty acids. It unveils new metabolites that discriminate PSCs from differentiated counterparts and directly measures substrates and co-factors of histone modifying enzymes, suggesting that NMR stands as a strategic technique for deciphering metabolic regulations of histone post-translational modifications. HR-MAS NMR analysis of whole PSCs complements the much used solution NMR of cell extracts. Altogether, our multi-platform NMR investigation provides a consolidated picture of PSC metabolic signatures and of metabolic pathways involved in differentiation.

Diagnosis of Bovine Respiratory Disease in feedlot cattle using blood 1H NMR metabolomics

Current diagnosis methods for Bovine Respiratory Disease (BRD) in feedlots have a low diagnostic accuracy. The current study aimed to search for blood biomarkers of BRD using ¹H NMR metabolomics and determine their accuracy in diagnosing BRD. Animals with visual signs of BRD (n = 149) and visually healthy (non-BRD; n = 148) were sampled for blood metabolomics analysis. Lung lesions indicative of BRD were scored at slaughter. Non-targeted ¹H NMR metabolomics was used to develop predictive algorithms for disease classification using classification and regression trees. In the absence of a gold standard for BRD diagnosis, six reference diagnosis methods were used to define an animal as BRD or non-BRD. Sensitivity (Se) and specificity (Sp) were used to estimate diagnostic accuracy (Acc). Blood metabolomics demonstrated a high accuracy at diagnosing BRD when using visual signs of BRD (Acc = 0.85), however was less accurate at diagnosing BRD using rectal temperature (Acc = 0.65), lung auscultation score (Acc = 0.61) and lung lesions at slaughter as reference diagnosis methods (Acc = 0.71). Phenylalanine, lactate, hydroxybutyrate, tyrosine, citrate and leucine were identified as metabolites of importance in classifying animals as BRD or non-BRD. The blood metabolome classified BRD and non-BRD animals with high accuracy and shows potential for use as a BRD diagnosis tool.

Analysis of NMR Metabolomics Data

In this chapter, we summarize data preprocessing and data analysis strategies used for analysis of NMR data for metabolomics studies. Metabolomics consists of the analysis of the low molecular weight compounds in cells, tissues, or biological fluids, and has been used to reveal biomarkers for early disease detection and diagnosis, to monitor interventions, and to provide information on pathway perturbations to inform mechanisms and identifying targets. Metabolic profiling (also termed metabotyping) involves the analysis of hundreds to thousands of molecules using mainly state-of-the-art mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy technologies. While NMR is less sensitive than mass spectrometry, NMR does provide a wealth of complex and information rich metabolite data. NMR data together with the use of conventional statistics, modeling methods, and bioinformatics tools reveals biomarker and mechanistic information. A typical NMR spectrum, with up to 64k data points, of a complex biological fluid or an extract of cells and tissues consists of thousands of sharp signals that are mainly derived from small molecules. In addition, a number of advanced NMR spectroscopic methods are available for extracting information on high molecular weight compounds such as lipids or lipoproteins. There are numerous data preprocessing, data reduction, and analysis methods developed and evolving in the field of NMR metabolomics. Our goal is to provide an extensive summary of NMR data preprocessing and analysis strategies by providing examples and open source and commercially available analysis software and bioinformatics tools.

Relationship of the blood metabolome to subsequent carcass traits at slaughter in feedlot Wagyu crossbred steers

The aim of the present study was to determine the relationships between the blood metabolome and (1) carcass traits with a focus on intramuscular fat (marbling), and (2) the length of time cattle consumed a high-starch diet in feedlot cattle. Blood samples were obtained from 181 Wagyu-crossbred steers between 300-400 days before slaughter when carcass data was collected. ¹H NMR spectroscopy identified 35 metabolites with 7 positively associated with marbling (3-hydroxybutyrate, propionate, acetate, creatine, histidine, valine, and isoleucine; P ≤ 0.05). Subcutaneous rump fat thickness was positively associated with glucose, leucine and lipids (P ≤ 0.05) and negatively associated with anserine and arabinose (P ≤ 0.05). Carcass weight and growth rate were negatively associated with 3-hydroxybutyrate (P < 0.05), and growth rate was negatively associated with creatine (P < 0.05) and positively associated with aspartate (P < 0.05). Glucose and arginine showed a significant interaction between marbling and number of days animals consumed a high-starch diet (P < 0.05). Sire was the single variable with the largest effect on the relative concentration of metabolites and carcass and production traits. Blood metabolomics helps understand fat and muscle metabolism, and is associated with genotype, and carcass and production traits in cattle offering potential biomarkers suitable to select animals for management and genetic improvement.

Serum metabolic signatures of coronary and carotid atherosclerosis and subsequent cardiovascular disease

AIMS

To characterize serum metabolic signatures associated with atherosclerosis in the coronary or carotid arteries and subsequently their association with incident cardiovascular disease (CVD).

METHODS AND RESULTS

We used untargeted one-dimensional (1D) serum metabolic profiling by proton nuclear magnetic resonance spectroscopy (1H NMR) among 3867 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), with replication among 3569 participants from the Rotterdam and LOLIPOP studies. Atherosclerosis was assessed by coronary artery calcium (CAC) and carotid intima-media thickness (IMT). We used multivariable linear regression to evaluate associations between NMR features and atherosclerosis accounting for multiplicity of comparisons. We then examined associations between metabolites associated with atherosclerosis and incident CVD available in MESA and Rotterdam and explored molecular networks through bioinformatics analyses. Overall, 30 1H NMR measured metabolites were associated with CAC and/or IMT, P = 1.3 × 10-14 to 1.0 × 10-6 (discovery) and P = 5.6 × 10-10 to 1.1 × 10-2 (replication). These associations were substantially attenuated after adjustment for conventional cardiovascular risk factors. Metabolites associated with atherosclerosis revealed disturbances in lipid and carbohydrate metabolism, branched chain, and aromatic amino acid metabolism, as well as oxidative stress and inflammatory pathways. Analyses of incident CVD events showed inverse associations with creatine, creatinine, and phenylalanine, and direct associations with mannose, acetaminophen-glucuronide, and lactate as well as apolipoprotein B (P < 0.05).

CONCLUSION

Metabolites associated with atherosclerosis were largely consistent between the two vascular beds (coronary and carotid arteries) and predominantly tag pathways that overlap with the known cardiovascular risk factors. We present an integrated systems network that highlights a series of inter-connected pathways underlying atherosclerosis.

Association of Untargeted Urinary Metabolomics and Lung Cancer Risk Among Never-Smoking Women in China

IMPORTANCE

Chinese women have the highest rate of lung cancer among female never-smokers in the world, and the etiology is poorly understood.

OBJECTIVE

To assess the association between metabolomics and lung cancer risk among never-smoking women.

DESIGN, SETTING, AND PARTICIPANTS

This nested case-control study included 275 never-smoking female patients with lung cancer and 289 never-smoking cancer-free control participants from the prospective Shanghai Women's Health Study recruited from December 28, 1996, to May 23, 2000. Validated food frequency questionnaires were used for the collection of dietary information. Metabolomic analysis was conducted from November 13, 2015, to January 6, 2016. Data analysis was conducted from January 6, 2016, to November 29, 2018.

EXPOSURES

Untargeted ultra-high-performance liquid chromatography-tandem mass spectrometry and nuclear magnetic resonance metabolomic profiles were characterized using prediagnosis urine samples. A total of 39 416 metabolites were measured.

MAIN OUTCOMES AND MEASURES

Incident lung cancer.

RESULTS

Among the 564 women, those who developed lung cancer (275 participants; median [interquartile range] age, 61.0 [52-65] years) and those who did not develop lung cancer (289 participants; median [interquartile range] age, 62.0 [53-66] years) at follow-up (median [interquartile range] follow-up, 10.9 [9.0-11.7] years) were similar in terms of their secondhand smoke exposure, history of respiratory diseases, and body mass index. A peak metabolite, identified as 5-methyl-2-furoic acid, was significantly associated with lower lung cancer risk (odds ratio, 0.57 [95% CI, 0.46-0.72]; P < .001; false discovery rate = 0.039). Furthermore, this peak was weakly correlated with self-reported dietary soy intake (ρ = 0.21; P < .001). Increasing tertiles of this metabolite were associated with lower lung cancer risk (in comparison with first tertile, odds ratio for second tertile, 0.52 [95% CI, 0.34-0.80]; and odds ratio for third tertile, 0.46 [95% CI, 0.30-0.70]), and the association was consistent across different histological subtypes and follow-up times. Additionally, metabolic pathway analysis found several systemic biological alterations that were associated with lung cancer risk, including 1-carbon metabolism, nucleotide metabolism, oxidative stress, and inflammation.

CONCLUSIONS AND RELEVANCE

This prospective study of the untargeted urinary metabolome and lung cancer among never-smoking women in China provides support for the hypothesis that soy-based metabolites are associated with lower lung cancer risk in never-smoking women and suggests that biological processes linked to air pollution may be associated with higher lung cancer risk in this population.

Metabolomic Profiling of Body Fluids in Mouse Models Demonstrates that Nuclear Magnetic Resonance Is a Putative Diagnostic Tool for the Presence of Thyroid Hormone Receptor α1 Mutations

Background: Resistance to thyroid hormone alpha (RTHα) is a rare genetic disease due to mutations in the THRA gene, which encodes thyroid hormone receptor alpha 1 (TRα1). Since its first description in 2012, 46 cases of RTHα have been reported worldwide, corresponding to 26 different mutations of TRα1. RTHα patients share some common symptoms with hypothyroid patients, without significant reduction in thyroid hormone level. The high variability of clinical features and the absence of reliable biochemical markers make the diagnosis of this disease difficult. Some of these mutations have been recently modeled in mice. Methods: In our study, we used four different mouse models heterozygous for frameshift mutations in the Thra gene. Two of them are very close to human mutations, while the two others have not yet been found in patients. We characterized the metabolic phenotypes of urine and plasma samples collected from these four animal models using an untargeted nuclear magnetic resonance (NMR)-based metabolomic approach. Results: Multivariate statistical analysis of the metabolomic profiles shows that biofluids of mice that carry human-like mutations can be discriminated from controls. Metabolic signatures associated with Thra mutations in urine and plasma are stable over time and clearly differ from the metabolic fingerprint of hypothyroidism in the mouse. Conclusion: Our results provide a proof-of-principle that easily accessible NMR-based metabolic fingerprints of biofluids could be used to diagnose RTHα in humans.

The metabolic fate and effects of 2-Bromophenol in male Sprague-Dawley rats

1. The metabolic fate and urinary excretion of 2-bromophenol, a phenolic metabolite of bromobenzene, was investigated in male Sprague-Dawley rats following single intraperitoneal doses at either 0, 100, or 200 mg/kg.2. Urine was collected for seven days and samples analysed using ¹H NMR spectroscopy, inductively coupled plasma (ICP)MS, and UPLC-MS.3. ¹H NMR spectroscopy of the urine samples showed that, at these doses, 2-bromophenol had little effect on endogenous metabolite profiles, supporting histopathology and clinical chemistry data, which showed no changes associated with the administration of 2-bromophenol in this study.4. The use of ICP-MS provided a means for the selective detection and quantification of bromine-containing species and showed that between 15 and 30% of the dose was excreted via the urine over 7 days of the study for both the 100 and 200 mg doses, respectively.5. The bulk of the excretion of Br-containing material had occurred by 8 h post administration. UPLC-MS of urine revealed a number of metabolites of 2-bromophenol, with 2-bromophenol glucuronide and 2-bromophenol sulphate identified as the major species. A number of minor hydroxylated metabolites were also detected as their glucuronide, sulphate, or O-methyl conjugates. There was no evidence for the production of reactive metabolites.

A Data Integration Multi-Omics Approach to Study Calorie Restriction-Induced Changes in Insulin Sensitivity

Background: The mechanisms responsible for calorie restriction (CR)-induced improvement in insulin sensitivity (IS) have not been fully elucidated. Greater insight can be achieved through deep biological phenotyping of subjects undergoing CR, and integration of big data. Materials and Methods: An integrative approach was applied to investigate associations between change in IS and factors from host, microbiota, and lifestyle after a 6-week CR period in 27 overweight or obese adults (ClinicalTrials.gov: NCT01314690). Partial least squares regression was used to determine associations of change (week 6 - baseline) between IS markers and lifestyle factors (diet and physical activity), subcutaneous adipose tissue (sAT) gene expression, metabolomics of serum, urine and feces, and gut microbiota composition. ScaleNet, a network learning approach based on spectral consensus strategy (SCS, developed by us) was used for reconstruction of biological networks. Results: A spectrum of variables from lifestyle factors (10 nutrients), gut microbiota (10 metagenomics species), and host multi-omics (metabolic features: 84 from serum, 73 from urine, and 131 from feces; and 257 sAT gene probes) most associated with IS were identified. Biological network reconstruction using SCS, highlighted links between changes in IS, serum branched chain amino acids, sAT genes involved in endoplasmic reticulum stress and ubiquitination, and gut metagenomic species (MGS). Linear regression analysis to model how changes of select variables over the CR period contribute to changes in IS, showed greatest contributions from gut MGS and fiber intake. Conclusion: This work has enhanced previous knowledge on links between host glucose homeostasis, lifestyle factors and the gut microbiota, and has identified potential biomarkers that may be used in future studies to predict and improve individual response to weight-loss interventions. Furthermore, this is the first study showing integration of the wide range of data presented herein, identifying 115 variables of interest with respect to IS from the initial input, consisting of 9,986 variables. Clinical Trial Registration: clinicaltrials.gov (NCT01314690).

Processing and Analysis of Untargeted Multicohort NMR Data

NMR data from large studies combining multiple cohorts is becoming common in large-scale metabolomics. The data size and combination of cohorts with diverse properties leads to special problems for data processing and analysis. These include alignment, normalization, detection and removal of outliers, presence of strong correlations, and the identification of unknowns. Nonetheless, these challenges can be addressed with suitable algorithms and techniques, leading to enhanced data sets ripe for further data mining.

Metabolic retroconversion of trimethylamine N-oxide and the gut microbiota

BACKGROUND

The dietary methylamines choline, carnitine, and phosphatidylcholine are used by the gut microbiota to produce a range of metabolites, including trimethylamine (TMA). However, little is known about the use of trimethylamine N-oxide (TMAO) by this consortium of microbes.

RESULTS

A feeding study using deuterated TMAO in C57BL6/J mice demonstrated microbial conversion of TMAO to TMA, with uptake of TMA into the bloodstream and its conversion to TMAO. Microbial activity necessary to convert TMAO to TMA was suppressed in antibiotic-treated mice, with deuterated TMAO being taken up directly into the bloodstream. In batch-culture fermentation systems inoculated with human faeces, growth of Enterobacteriaceae was stimulated in the presence of TMAO. Human-derived faecal and caecal bacteria (n = 66 isolates) were screened on solid and liquid media for their ability to use TMAO, with metabolites in spent media analysed by ¹H-NMR. As with the in vitro fermentation experiments, TMAO stimulated the growth of Enterobacteriaceae; these bacteria produced most TMA from TMAO. Caecal/small intestinal isolates of Escherichia coli produced more TMA from TMAO than their faecal counterparts. Lactic acid bacteria produced increased amounts of lactate when grown in the presence of TMAO but did not produce large amounts of TMA. Clostridia (sensu stricto), bifidobacteria, and coriobacteria were significantly correlated with TMA production in the mixed fermentation system but did not produce notable quantities of TMA from TMAO in pure culture.

CONCLUSIONS

Reduction of TMAO by the gut microbiota (predominantly Enterobacteriaceae) to TMA followed by host uptake of TMA into the bloodstream from the intestine and its conversion back to TMAO by host hepatic enzymes is an example of metabolic retroconversion. TMAO influences microbial metabolism depending on isolation source and taxon of gut bacterium. Correlation of metabolomic and abundance data from mixed microbiota fermentation systems did not give a true picture of which members of the gut microbiota were responsible for converting TMAO to TMA; only by supplementing the study with pure culture work and additional metabolomics was it possible to increase our understanding of TMAO bioconversions by the human gut microbiota.

Microbial-Host Co-metabolites Are Prodromal Markers Predicting Phenotypic Heterogeneity in Behavior, Obesity, and Impaired Glucose Tolerance

The influence of the gut microbiome on metabolic and behavioral traits is widely accepted, though the microbiome-derived metabolites involved remain unclear. We carried out untargeted urine ¹H-NMR spectroscopy-based metabolic phenotyping in an isogenic C57BL/6J mouse population (n = 50) and show that microbial-host co-metabolites are prodromal (i.e., early) markers predicting future divergence in metabolic (obesity and glucose homeostasis) and behavioral (anxiety and activity) outcomes with 94%–100% accuracy. Some of these metabolites also modulate disease phenotypes, best illustrated by trimethylamine-N-oxide (TMAO), a product of microbial-host co-metabolism predicting future obesity, impaired glucose tolerance (IGT), and behavior while reducing endoplasmic reticulum stress and lipogenesis in 3T3-L1 adipocytes. Chronic in vivo TMAO treatment limits IGT in HFD-fed mice and isolated pancreatic islets by increasing insulin secretion. We highlight the prodromal potential of microbial metabolites to predict disease outcomes and their potential in shaping mammalian phenotypic heterogeneity.

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High-fat diet drives phenotypic heterogeneity in metabolism and behavior

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Microbial metabolites, including methylamines, predict phenotypic heterogeneity

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TMAO attenuates ER stress and reduces lipogenesis in adipocytes

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TMAO improves insulin secretion and restores glucose tolerance in vivo

Bi-clustering of metabolic data using matrix factorization tools

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We propose a positive matrix factorization bi-clustering strategy for metabolic data.

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The approach automatically determines the number and composition of bi-clusters.

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We demonstrate its superior performance compared to other techniques.

Metabolic phenotyping technologies based on Nuclear Magnetic Spectroscopy (NMR) and Mass Spectrometry (MS) generate vast amounts of unrefined data from biological samples. Clustering strategies are frequently employed to provide insight into patterns of relationships between samples and metabolites. Here, we propose the use of a non-negative matrix factorization driven bi-clustering strategy for metabolic phenotyping data in order to discover subsets of interrelated metabolites that exhibit similar behaviour across subsets of samples. The proposed strategy incorporates bi-cross validation and statistical segmentation techniques to automatically determine the number and structure of bi-clusters. This alternative approach is in contrast to the widely used conventional clustering approaches that incorporate all molecular peaks for clustering in metabolic studies and require a priori specification of the number of clusters. We perform the comparative analysis of the proposed strategy with other bi-clustering approaches, which were developed in the context of genomics and transcriptomics research. We demonstrate the superior performance of the proposed bi-clustering strategy on both simulated (NMR) and real (MS) bacterial metabolic data.

Metabolomic analysis reveals altered metabolic pathways in a rat model of gastric carcinogenesis

Gastric cancer (GC) is one of the most malignant tumors with a poor prognosis. Alterations in metabolic pathways are inextricably linked to GC progression. However, the underlying molecular mechanisms remain elusive. We performed NMR-based metabolomic analysis of sera derived from a rat model of gastric carcinogenesis, revealed significantly altered metabolic pathways correlated with the progression of gastric carcinogenesis. Rats were histologically classified into four pathological groups (gastritis, GS; low-grade gastric dysplasia, LGD; high-grade gastric dysplasia, HGD; GC) and the normal control group (CON). The metabolic profiles of the five groups were clearly distinguished from each other. Furthermore, significant inter-metabolite correlations were extracted and used to reconstruct perturbed metabolic networks associated with the four pathological stages compared with the normal stage. Then, significantly altered metabolic pathways were identified by pathway analysis. Our results showed that oxidative stress-related metabolic pathways, choline phosphorylation and fatty acid degradation were continually disturbed during gastric carcinogenesis. Moreover, amino acid metabolism was perturbed dramatically in gastric dysplasia and GC. The GC stage showed more changed metabolite levels and more altered metabolic pathways. Two activated pathways (glycolysis; glycine, serine and threonine metabolism) substantially contributed to the metabolic alterations in GC. These results lay the basis for addressing the molecular mechanisms underlying gastric carcinogenesis and extend our understanding of GC progression.

Fast Metabolite Identification in Nuclear Magnetic Resonance Metabolomic Studies: Statistical Peak Sorting and Peak Overlap Detection for More Reliable Database Queries

A lot of time is spent by researchers in the identification of metabolites in NMR-based metabolomic studies. The usual metabolite identification starts employing public or commercial databases to match chemical shifts thought to belong to a given compound. Statistical total correlation spectroscopy (STOCSY), in use for more than a decade, speeds the process by finding statistical correlations among peaks, being able to create a better peak list as input for the database query. However, the (normally not automated) analysis becomes challenging due to the intrinsic issue of peak overlap, where correlations of more than one compound appear in the STOCSY trace. Here we present a fully automated methodology that analyzes all STOCSY traces at once (every peak is chosen as driver peak) and overcomes the peak overlap obstacle. Peak overlap detection by clustering analysis and sorting of traces (POD-CAST) first creates an overlap matrix from the STOCSY traces, then clusters the overlap traces based on their similarity and finally calculates a cumulative overlap index (COI) to account for both strong and intermediate correlations. This information is gathered in one plot to help the user identify the groups of peaks that would belong to a single molecule and perform a more reliable database query. The simultaneous examination of all traces reduces the time of analysis, compared to viewing STOCSY traces by pairs or small groups, and condenses the redundant information in the 2D STOCSY matrix into bands containing similar traces. The COI helps in the detection of overlapping peaks, which can be added to the peak list from another cross-correlated band. POD-CAST overcomes the generally overlooked and underestimated presence of overlapping peaks and it detects them to include them in the search of all compounds contributing to the peak overlap, enabling the user to accelerate the metabolite identification process with more successful database queries and searching all tentative compounds in the sample set.

Evolution of Newborns' Urinary Metabolomic Profiles According to Age and Growth

Improving the management of neonatal diseases and prevention of chronic diseases in adulthood requires a better comprehension of the complex maturational processes associated with newborns' development. Urine-based metabolomic studies play a promising role in the fields of pediatrics and neonatology, relying on simple and noninvasive collection procedures while integrating a variety of factors such as genotype, nutritional state, lifestyle, and diseases. Here, we investigate the influence of age, weight, height, and gender on the urine metabolome during the first 4 months of life. Untargeted analysis of urine was carried out by ¹H-Nuclear Magnetic Resonance (NMR) spectroscopy for 90 newborns under 4 months of age, and free of metabolic, nephrologic, or urologic diseases. Supervised multivariate statistical analysis of the metabolic profiles revealed metabolites significantly associated with age, weight, and height, respectively. The tremendous growth occurring during the neonatal period is associated with specific modifications of newborns' metabolism. Conversely, gender appears to have no impact on the urine metabolome during early infancy. These results allow a deeper understanding of newborns' metabolic maturation and underline potential confounding factors in newborns' metabolomics studies. We emphasize the need to systematically and precisely report children age, height, and weight that impact urine metabolic profiles of infants.

IL-7 Restores T Lymphocyte Immunometabolic Failure in Septic Shock Patients through mTOR Activation

T lymphocyte alterations are central to sepsis pathophysiology, whereas related mechanisms remain poorly understood. We hypothesized that metabolic alterations could play a role in sepsis-induced T lymphocyte dysfunction. Samples from septic shock patients were obtained at day 3 and compared with those from healthy donors. T cell metabolic status was evaluated in the basal condition and after T cell stimulation. We observed that basal metabolic content measured in lymphocytes by nuclear magnetic resonance spectroscopy was altered in septic patients. Basal ATP concentration, oxidative phosphorylation (OXPHOS), and glycolysis pathways in T cells were decreased as well. After stimulation, T lymphocytes from patients failed to induce glycolysis, OXPHOS, ATP production, GLUT1 expression, glucose entry, and proliferation to similar levels as controls. This was associated with significantly altered mTOR, but not Akt or HIF-1α, activation and only minor AMPKα phosphorylation dysfunction. IL-7 treatment improved mTOR activation, GLUT1 expression, and glucose entry in septic patients' T lymphocytes, leading to their enhanced proliferation. mTOR activation was central to this process, because rapamycin systematically inhibited the beneficial effect of recombinant human IL-7. We demonstrate the central role of immunometabolism and, in particular, mTOR alterations in the pathophysiology of sepsis-induced T cell alterations. Our results support the rationale for targeting metabolism in sepsis with recombinant human IL-7 as a treatment option.

Territorial origin of olive oil: representing georeferenced maps of olive oils by NMR profiling

Proton NMR profiling is nowadays a consolidated technique for the identification of geographical origin of food samples. The common approach consists in correlating NMR spectra of food samples to their territorial origin by multivariate classification statistical algorithms. In the present work, we illustrate an alternative perspective to exploit territorial information, contained in the NMR spectra, which is based on the implementation of a geographic information system (GIS). Nuclear magnetic resonance spectra are used to build a GIS map permitting the identification of territorial regions having strong similarities in the chemical content of the produced food (terroir units). These terroir units can, in turn, be used as input for labeling samples to be analyzed by traditional classification methods. In this work, we describe the methods and the algorithms that permit to produce GIS maps from NMR profiles and apply the described method to the analysis of the geographical distribution of olive oils in an Italian region. In particular, we analyzed by ¹ H NMR up to 98 georeferenced olive oil samples produced in the Abruzzo Italian region. By using the first principal component of the NMR variables selected according to the Moran test, we produced a GIS map, in which we identified two regions incidentally corresponding to the provinces of Teramo and Pescara. We then labeled the samples according to the province of provenience and built an LDA model that provides a classification ability up to 99% . A comparison between the variables selected in the geostatistics and classification steps is finally performed. Copyright © 2016 John Wiley & Sons, Ltd.

Longitudinal serum metabolomics evaluation of trastuzumab and everolimus combination as pre-operative treatment for HER-2 positive breast cancer patients

The mammalian target of rapamycin complex 1 (mTORC1) is an attractive target for HER-2 positive breast cancer therapy because of its key role in protein translation regulation, cell growth and metabolism. We present here a metabolomic investigation exploring the impact of mTOR inhibition on serum metabolic profiles from patients with non-metastatic breast cancer overexpressing HER-2.

Baseline, treatment-related and post-treatment serum samples were analyzed for 79 patients participating in the French clinical trial RADHER, in which randomized patients with HER-2 positive breast cancer received either trastuzumab alone (arm T) or a trastuzumab and everolimus combination (arm T+E). Longitudinal series of NMR serum metabolic profiles were exploited to investigate treatment effects on the patients metabolism over time, in both group.

Trastuzumab and everolimus combination induces faster changes in patients metabolism than trastuzumab alone, visible after only one week of treatment as well as a residual effect detectable up to three weeks after ending the treatment. These metabolic fingerprints highlight the involvement of several metabolic pathways reflecting a systemic effect, particularly on the liver and visceral fat. Comparison of serum metabolic profiles between the two arms shows that everolimus, an mTORC1 inhibitor, is responsible for host metabolism modifications observed in arm T+E.

In HER-2 positive breast cancer, our metabolomic approach confirms a fast and persistent host metabolism modification caused by mTOR inhibition.

Serum polyunsaturated fatty acid metabolites as useful tool for screening potential biomarker of colorectal cancer

The biomarker identification of cancer is benefit for early detection and less invasion. Polyunsaturated fatty acid (PUFA) metabolite as inflammatory mediators can affect progression and treatment of cancer. In this work, the serum was collected from colorectal cancer patients and healthy volunteers, and then we tested the change of serum PUFA metabolites in both of them by ultra-high performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS). Of the 158 PUFA and their metabolites, we found that abnormal change of 2, 3-dinor-8-iso-PGF2α, 19-HETE and 12-keto-LTB4 from arachidonic acid were observed in colorectal cancer patients. Meanwhile, 9-HODE and 13-HODE from linoleic acid were significant lower in colorectal cancer patients. Our data suggested that some PUFA metabolites might be used as a potential biomarker of colorectal cancer, which might provide assistance in clinical diagnosis and treatment.

A Systematic Evaluation of Blood Serum and Plasma Pre-Analytics for Metabolomics Cohort Studies

The recent thriving development of biobanks and associated high-throughput phenotyping studies requires the elaboration of large-scale approaches for monitoring biological sample quality and compliance with standard protocols. We present a metabolomic investigation of human blood samples that delineates pitfalls and guidelines for the collection, storage and handling procedures for serum and plasma. A series of eight pre-processing technical parameters is systematically investigated along variable ranges commonly encountered across clinical studies. While metabolic fingerprints, as assessed by nuclear magnetic resonance, are not significantly affected by altered centrifugation parameters or delays between sample pre-processing (blood centrifugation) and storage, our metabolomic investigation highlights that both the delay and storage temperature between blood draw and centrifugation are the primary parameters impacting serum and plasma metabolic profiles. Storing the blood drawn at 4 °C is shown to be a reliable routine to confine variability associated with idle time prior to sample pre-processing. Based on their fine sensitivity to pre-analytical parameters and protocol variations, metabolic fingerprints could be exploited as valuable ways to determine compliance with standard procedures and quality assessment of blood samples within large multi-omic clinical and translational cohort studies.

Prolonged Mechanical Circumferential Stretch Induces Metabolic Changes in Rat Inferior Vena Cava

OBJECTIVE/BACKGROUND

Circumferential stretch on the vein wall has been suggested as a potential etiological factor in the development of varicose veins. However, the influence of vein wall stretch on vein metabolism has not yet been explored. The aim of this study was to investigate the effect of short and prolonged mechanical stretch on vein wall metabolism.

METHODS

Circular segments of inferior vena cava from male Sprague-Dawley rats were exposed to normal 0.5-g (nonstretched) or high 2-g (stretched) tension for short (4 h) or prolonged (18 h) duration (five vein segments per group). Contraction response to phenylephrine (10^-5 M) and KCl (96 mM) was elicited to observe the effect of circumferential stretch on vein function. The polar and organic metabolites in vein tissue were extracted using a bilayer extraction method. Aqueous and organic extracts were analyzed using nuclear magnetic resonance spectroscopy and ultra performance liquid chromatography coupled to mass spectrometry, respectively. Data acquired from both analytical platforms were analyzed using mathematical modeling.

RESULTS

Increased concentrations of valine (p = .02) and choline (p = .03) metabolites and triglyceride moieties (p = .03) were observed in veins stretched for 18 h compared with the nonstretched/18 h group.

DISCUSSION

Increased concentrations of branched chain amino acid valine and cell membrane constituent choline indicate increased muscle breakdown and increased metabolism of membrane phospholipids under stretch in an ex-vivo model. Increased intensities of triglyceride moieties in stretched vein segments for 18 h suggest that high pressure may induce an inflammatory response.

CONCLUSION

This study has shown that prolonged mechanical circumferential stretch (18 h) alters the metabolic profile of rat inferior vena cava.

Power Analysis and Sample Size Determination in Metabolic Phenotyping

Estimation of statistical power and sample size is a key aspect of experimental design. However, in metabolic phenotyping, there is currently no accepted approach for these tasks, in large part due to the unknown nature of the expected effect. In such hypothesis free science, neither the number or class of important analytes nor the effect size are known a priori. We introduce a new approach, based on multivariate simulation, which deals effectively with the highly correlated structure and high-dimensionality of metabolic phenotyping data. First, a large data set is simulated based on the characteristics of a pilot study investigating a given biomedical issue. An effect of a given size, corresponding either to a discrete (classification) or continuous (regression) outcome is then added. Different sample sizes are modeled by randomly selecting data sets of various sizes from the simulated data. We investigate different methods for effect detection, including univariate and multivariate techniques. Our framework allows us to investigate the complex relationship between sample size, power, and effect size for real multivariate data sets. For instance, we demonstrate for an example pilot data set that certain features achieve a power of 0.8 for a sample size of 20 samples or that a cross-validated predictivity QY(2) of 0.8 is reached with an effect size of 0.2 and 200 samples. We exemplify the approach for both nuclear magnetic resonance and liquid chromatography-mass spectrometry data from humans and the model organism C. elegans.

Akkermansia muciniphila and improved metabolic health during a dietary intervention in obesity: relationship with gut microbiome richness and ecology

OBJECTIVE

Individuals with obesity and type 2 diabetes differ from lean and healthy individuals in their abundance of certain gut microbial species and microbial gene richness. Abundance of Akkermansia muciniphila, a mucin-degrading bacterium, has been inversely associated with body fat mass and glucose intolerance in mice, but more evidence is needed in humans. The impact of diet and weight loss on this bacterial species is unknown. Our objective was to evaluate the association between faecal A. muciniphila abundance, faecal microbiome gene richness, diet, host characteristics, and their changes after calorie restriction (CR).

DESIGN

The intervention consisted of a 6-week CR period followed by a 6-week weight stabilisation diet in overweight and obese adults (N=49, including 41 women). Faecal A. muciniphila abundance, faecal microbial gene richness, diet and bioclinical parameters were measured at baseline and after CR and weight stabilisation.

RESULTS

At baseline A. muciniphila was inversely related to fasting glucose, waist-to-hip ratio and subcutaneous adipocyte diameter. Subjects with higher gene richness and A. muciniphila abundance exhibited the healthiest metabolic status, particularly in fasting plasma glucose, plasma triglycerides and body fat distribution. Individuals with higher baseline A. muciniphila displayed greater improvement in insulin sensitivity markers and other clinical parameters after CR. These participants also experienced a reduction in A. muciniphila abundance, but it remained significantly higher than in individuals with lower baseline abundance. A. muciniphila was associated with microbial species known to be related to health.

CONCLUSIONS

A. muciniphila is associated with a healthier metabolic status and better clinical outcomes after CR in overweight/obese adults. The interaction between gut microbiota ecology and A. muciniphila warrants further investigation.

TRIAL REGISTRATION NUMBER

NCT01314690.

A statistical framework to model the meeting-in-the-middle principle using metabolomic data: application to hepatocellular carcinoma in the EPIC study

Metabolomics is a potentially powerful tool for identification of biomarkers associated with lifestyle exposures and risk of various diseases. This is the rationale of the 'meeting-in-the-middle' concept, for which an analytical framework was developed in this study. In a nested case-control study on hepatocellular carcinoma (HCC) within the European Prospective Investigation into Cancer and nutrition (EPIC), serum (1)H nuclear magnetic resonance (NMR) spectra (800 MHz) were acquired for 114 cases and 222 matched controls. Through partial least square (PLS) analysis, 21 lifestyle variables (the 'predictors', including information on diet, anthropometry and clinical characteristics) were linked to a set of 285 metabolic variables (the 'responses'). The three resulting scores were related to HCC risk by means of conditional logistic regressions. The first PLS factor was not associated with HCC risk. The second PLS metabolomic factor was positively associated with tyrosine and glucose, and was related to a significantly increased HCC risk with OR = 1.11 (95% CI: 1.02, 1.22, P = 0.02) for a 1SD change in the responses score, and a similar association was found for the corresponding lifestyle component of the factor. The third PLS lifestyle factor was associated with lifetime alcohol consumption, hepatitis and smoking, and had negative loadings on vegetables intake. Its metabolomic counterpart displayed positive loadings on ethanol, glutamate and phenylalanine. These factors were positively and statistically significantly associated with HCC risk, with 1.37 (1.05, 1.79, P = 0.02) and 1.22 (1.04, 1.44, P = 0.01), respectively. Evidence of mediation was found in both the second and third PLS factors, where the metabolomic signals mediated the relation between the lifestyle component and HCC outcome. This study devised a way to bridge lifestyle variables to HCC risk through NMR metabolomics data. This implementation of the 'meeting-in-the-middle' approach finds natural applications in settings characterised by high-dimensional data, increasingly frequent in the omics generation.

A serum metabolomic fingerprint of bevacizumab and temsirolimus combination as first-line treatment of metastatic renal cell carcinoma

BACKGROUND

Renal cell carcinoma is one of the most chemoresistant cancers, and its metastatic form requires administration of targeted therapies based on angiogenesis or mTOR inhibitors. Understanding how these treatments impact the human metabolism is essential to predict the host response and adjust personalised therapies. We present a metabolomic investigation of serum samples from patients with metastatic RCC (mRCC) to identify metabolic signatures associated with targeted therapies.

METHODS

Pre-treatment and serial on-treatment sera were available for 121 patients participating in the French clinical trial TORAVA, in which 171 randomised patients with mRCC received a bevacizumab and temsirolimus combination (experimental arm A) or a standard treatment: either sunitinib (B) or interferon-α+bevacizumab (C). Metabolic profiles were obtained using nuclear magnetic resonance spectroscopy and compared on-treatment or between treatments.

RESULTS

Multivariate statistical modelling discriminates serum profiles before and after several weeks of treatment for arms A and C. The combination A causes faster changes in patient metabolism than treatment C, detectable after only 2 weeks of treatment. Metabolites related to the discrimination include lipids and carbohydrates, consistently with the known RCC metabolism and side effects of the drugs involved. Comparison of the metabolic profiles for the three arms shows that temsirolimus, an mTOR inhibitor, is responsible for the faster host metabolism modification observed in the experimental arm.

CONCLUSIONS

In mRCC, metabolomics shows a faster host metabolism modification induced by a mTOR inhibitor as compared with standard treatments. These results should be confirmed in larger cohorts and other cancer types.

Metabolomic profiles of hepatocellular carcinoma in a European prospective cohort

BACKGROUND

Hepatocellular carcinoma (HCC), the most prevalent form of liver cancer, is difficult to diagnose and has limited treatment options with a low survival rate. Aside from a few key risk factors, such as hepatitis, high alcohol consumption, smoking, obesity, and diabetes, there is incomplete etiologic understanding of the disease and little progress in identification of early risk biomarkers.

METHODS

To address these aspects, an untargeted nuclear magnetic resonance metabolomic approach was applied to pre-diagnostic serum samples obtained from first incident, primary HCC cases (n = 114) and matched controls (n = 222) identified from amongst the participants of a large European prospective cohort.

RESULTS

A metabolic pattern associated with HCC risk comprised of perturbations in fatty acid oxidation and amino acid, lipid, and carbohydrate metabolism was observed. Sixteen metabolites of either endogenous or exogenous origin were found to be significantly associated with HCC risk. The influence of hepatitis infection and potential liver damage was assessed, and further analyses were made to distinguish patterns of early or later diagnosis.

CONCLUSION

Our results show clear metabolic alterations from early stages of HCC development with application for better etiologic understanding, prevention, and early detection of this increasingly common cancer.

Analytical strategies for studying stem cell metabolism

Owing to their capacity for self-renewal and pluripotency, stem cells possess untold potential for revolutionizing the field of regenerative medicine through the development of novel therapeutic strategies for treating cancer, diabetes, cardiovascular and neurodegenerative diseases. Central to developing these strategies is improving our understanding of biological mechanisms responsible for governing stem cell fate and self-renewal. Increasing attention is being given to the significance of metabolism, through the production of energy and generation of small molecules, as a critical regulator of stem cell functioning. Rapid advances in the field of metabolomics now allow for in-depth profiling of stem cells both in vitro and in vivo, providing a systems perspective on key metabolic and molecular pathways which influence stem cell biology. Understanding the analytical platforms and techniques that are currently used to study stem cell metabolomics, as well as how new insights can be derived from this knowledge, will accelerate new research in the field and improve future efforts to expand our understanding of the interplay between metabolism and stem cell biology.

mQTL.NMR: an integrated suite for genetic mapping of quantitative variations of (1)H NMR-based metabolic profiles

High-throughput (1)H nuclear magnetic resonance (NMR) is an increasingly popular robust approach for qualitative and quantitative metabolic profiling, which can be used in conjunction with genomic techniques to discover novel genetic associations through metabotype quantitative trait locus (mQTL) mapping. There is therefore a crucial necessity to develop specialized tools for an accurate detection and unbiased interpretability of the genetically determined metabolic signals. Here we introduce and implement a combined chemoinformatic approach for objective and systematic analysis of untargeted (1)H NMR-based metabolic profiles in quantitative genetic contexts. The R/Bioconductor mQTL.NMR package was designed to (i) perform a series of preprocessing steps restoring spectral dependency in collinear NMR data sets to reduce the multiple testing burden, (ii) carry out robust and accurate mQTL mapping in human cohorts as well as in rodent models, (iii) statistically enhance structural assignment of genetically determined metabolites, and (iv) illustrate results with a series of visualization tools. Built-in flexibility and implementation in the powerful R/Bioconductor framework allow key preprocessing steps such as peak alignment, normalization, or dimensionality reduction to be tailored to specific problems. The mQTL.NMR package is freely available with its source code through the Comprehensive R/Bioconductor repository and its own website ( http://www.ican-institute.org/tools/ ). It represents a significant advance to facilitate untargeted metabolomic data processing and quantitative analysis and their genetic mapping.

Sample size calculation in metabolic phenotyping studies

The number of samples needed to identify significant effects is a key question in biomedical studies, with consequences on experimental designs, costs and potential discoveries. In metabolic phenotyping studies, sample size determination remains a complex step. This is due particularly to the multiple hypothesis-testing framework and the top-down hypothesis-free approach, with no a priori known metabolic target. Until now, there was no standard procedure available to address this purpose. In this review, we discuss sample size estimation procedures for metabolic phenotyping studies. We release an automated implementation of the Data-driven Sample size Determination (DSD) algorithm for MATLAB and GNU Octave. Original research concerning DSD was published elsewhere. DSD allows the determination of an optimized sample size in metabolic phenotyping studies. The procedure uses analytical data only from a small pilot cohort to generate an expanded data set. The statistical recoupling of variables procedure is used to identify metabolic variables, and their intensity distributions are estimated by Kernel smoothing or log-normal density fitting. Statistically significant metabolic variations are evaluated using the Benjamini-Yekutieli correction and processed for data sets of various sizes. Optimal sample size determination is achieved in a context of biomarker discovery (at least one statistically significant variation) or metabolic exploration (a maximum of statistically significant variations). DSD toolbox is encoded in MATLAB R2008A (Mathworks, Natick, MA) for Kernel and log-normal estimates, and in GNU Octave for log-normal estimates (Kernel density estimates are not robust enough in GNU octave). It is available at http://www.prabi.fr/redmine/projects/dsd/repository, with a tutorial at http://www.prabi.fr/redmine/projects/dsd/wiki.

Statistical HOmogeneous Cluster SpectroscopY (SHOCSY): an optimized statistical approach for clustering of ¹H NMR spectral data to reduce interference and enhance robust biomarkers selection

We propose a novel statistical approach to improve the reliability of (1)H NMR spectral analysis in complex metabolic studies. The Statistical HOmogeneous Cluster SpectroscopY (SHOCSY) algorithm aims to reduce the variation within biological classes by selecting subsets of homogeneous (1)H NMR spectra that contain specific spectroscopic metabolic signatures related to each biological class in a study. In SHOCSY, we used a clustering method to categorize the whole data set into a number of clusters of samples with each cluster showing a similar spectral feature and hence biochemical composition, and we then used an enrichment test to identify the associations between the clusters and the biological classes in the data set. We evaluated the performance of the SHOCSY algorithm using a simulated (1)H NMR data set to emulate renal tubule toxicity and further exemplified this method with a (1)H NMR spectroscopic study of hydrazine-induced liver toxicity study in rats. The SHOCSY algorithm improved the predictive ability of the orthogonal partial least-squares discriminatory analysis (OPLS-DA) model through the use of "truly" representative samples in each biological class (i.e., homogeneous subsets). This method ensures that the analyses are no longer confounded by idiosyncratic responders and thus improves the reliability of biomarker extraction. SHOCSY is a useful tool for removing irrelevant variation that interfere with the interpretation and predictive ability of models and has widespread applicability to other spectroscopic data, as well as other "omics" type of data.

Metabolomics analysis uncovers that dietary restriction buffers metabolic changes associated with aging in Caenorhabditis elegans

Dietary restriction (DR) is one of the most universal means of extending lifespan. Yet, whether and how DR specifically affects the metabolic changes associated with aging is essentially unknown. Here, we present a comprehensive and unbiased picture of the metabolic variations that take place with age at the whole organism level in Caenorhabditis elegans by using ¹H high-resolution magic-angle spinning (HR-MAS) nuclear magnetic resonance (NMR) analysis of intact worms. We investigate metabolic variations potentially important for lifespan regulation by comparing the metabolic fingerprint of two previously described genetic models of DR, the long-lived eat-2(ad465) and slcf-1(tm2258) worms, as single mutants or in combination with a genetic suppressor of their lifespan phenotype. Our analysis shows that significant changes in metabolite profiles precede the major physiological decline that accompanies aging and that DR protects from some of those metabolic changes. More specifically, low phosphocholine (PCho) correlates with high life expectancy. A mutation in the tumor suppressor gene PTEN/DAF-18, which suppresses the beneficial effects of DR in both C. elegans and mammals, increases both PCho level and choline kinase expression. Furthermore, we show that choline kinase function in the intestine can regulate lifespan. This study highlights the relevance of NMR metabolomic approaches for identifying potential biomarkers of aging.

[Nuclear magnetic resonance based metabolic phenotyping for patient evaluations in operating rooms and intensive care units]

Metabolic phenotyping consists in the identification of subtle and coordinated metabolic variations associated with various pathophysiological stimuli. Different analytical methods, such as nuclear magnetic resonance, allow the simultaneous quantification of a large number of metabolites. Statistical analyses of these spectra thus lead to the discrimination between samples and the identification of a metabolic phenotype corresponding to the effect under study. This approach allows the extraction of candidate biomarkers and the recovery of perturbed metabolic networks, driving to the generation of biochemical hypotheses (pathophysiological mechanisms, diagnostic tests, therapeutic targets…). Metabolic phenotyping could be useful in anaesthesiology and intensive care medicine for the evaluation, monitoring or diagnosis of life-threatening situations, to optimise patient managements. This review introduces the physical and statistical fundamentals of NMR-based metabolic phenotyping, describes the work already achieved by this approach in anaesthesiology and intensive care medicine. Finally, potential areas of interest are discussed for the perioperative and intensive management of patients, from newborns to adults.

Metabolic phenotyping and systems biology approaches to understanding metabolic syndrome and fatty liver disease

Metabolic syndrome, a cluster of risk factors for type 2 diabetes mellitus and cardiovascular disease, is becoming an increasing global health concern. Insulin resistance is often associated with metabolic syndrome and also typical hepatic manifestations such as nonalcoholic fatty liver disease. Profiling of metabolic products (metabolic phenotyping or metabotyping) has provided new insights into metabolic syndrome and nonalcoholic fatty liver disease. Data from nuclear magnetic resonance spectroscopy and mass spectrometry combined with statistical modeling and top-down systems biology have allowed us to analyze and interpret metabolic signatures in terms of metabolic pathways and protein interaction networks and to identify the genomic and metagenomic determinants of metabolism. For example, metabolic phenotyping has shown that relationships between host cells and the microbiome affect development of the metabolic syndrome and fatty liver disease. We review recent developments in metabolic phenotyping and systems biology technologies and how these methodologies have provided insights into the mechanisms of metabolic syndrome and nonalcoholic fatty liver disease. We discuss emerging areas of research in this field and outline our vision for how metabolic phenotyping could be used to study metabolic syndrome and fatty liver disease.