Multi-Method Quantification of Acetyl-Coenzyme A and Further Acyl-Coenzyme A Species in Normal and Ischemic Rat Liver.

Thioesters of coenzyme A (CoA) carrying different acyl chains (acyl-CoAs) are central intermediates of many metabolic pathways and donor molecules for protein lysine acylation. Acyl-CoA species largely differ in terms of cellular concentrations and physico-chemical properties, rendering their analysis challenging. Here, we compare several approaches to quantify cellular acyl-CoA concentrations in normal and ischemic rat liver, using HPLC and LC-MS/MS for multi-acyl-CoA analysis, as well as NMR, fluorimetric and spectrophotometric techniques for the quantification of acetyl-CoAs. In particular, we describe a simple LC-MS/MS protocol that is suitable for the relative quantification of short and medium-chain acyl-CoA species. We show that ischemia induces specific changes in the short-chain acyl-CoA relative concentrations, while mild ischemia (1-2 min), although reducing succinyl-CoA, has little effects on acetyl-CoA, and even increases some acyl-CoA species upstream of the tricarboxylic acid cycle. In contrast, advanced ischemia (5-6 min) also reduces acetyl-CoA levels. Our approach provides the keys to accessing the acyl-CoA metabolome for a more in-depth analysis of metabolism, protein acylation and epigenetics.

Investigation of circulating metabolites associated with breast cancer risk by untargeted metabolomics: a case-control study nested within the French E3N cohort.

BACKGROUND: Perturbations in circulating metabolites prior to a breast cancer diagnosis are not well characterised. We aimed to gain more detailed knowledge to help understand and prevent the disease. METHODS: Baseline plasma samples from 791 breast cancer cases and 791 matched controls from the E3N (EPIC-France) cohort were profiled by nuclear magnetic resonance (NMR)-based untargeted metabolomics. Partial least-squares discriminant analysis (PLS-DA) models were built from NMR profiles to predict disease outcome, and odds ratios and false discovery rate (FDR)-adjusted CIs were calculated for 43 identified metabolites by conditional logistic regression. RESULTS: Breast cancer onset was predicted in the premenopausal subgroup with modest accuracy (AUC 0.61, 95% CI: 0.49-0.73), and 10 metabolites associated with risk, particularly histidine (OR = 1.70 per SD increase, FDR-adjusted CI 1.19-2.41), N-acetyl glycoproteins (OR = 1.53, FDR-adjusted CI 1.18-1.97), glycerol (OR = 1.55, FDR-adjusted CI 1.11-2.18) and ethanol (OR = 1.44, FDR-adjusted CI 1.05-1.97). No predictive capacity or significant metabolites were found overall or for postmenopausal women. CONCLUSIONS: Perturbed metabolism compared to controls was observed in premenopausal but not postmenopausal cases. Histidine and NAC have known involvement in inflammatory pathways, and the robust association of ethanol with risk suggests the involvement of alcohol intake.

Fast and ergonomic extraction of adherent mammalian cells for NMR-based metabolomics studies.

Cellular metabolomics has become key to elucidate mechanistic aspects in various fields such as cancerology or pharmacology, and is rapidly becoming a standard phenotyping tool accessible to the broad biological community. Acquisition of reliable spectroscopic datasets, such as nuclear magnetic resonance (NMR) spectra, to characterize biological systems depends on the elaboration of robust methods for cellular metabolites extraction. Previous studies have addressed many issues raised by these protocols, however with little pondering on ergonomic and practical aspects of the methods that impact their scalability, reproducibility and hence their suitability to high-throughput studies or their use by non-metabolomics experts. Here, we optimize a fast and ergonomic protocol for extraction of metabolites from adherent mammalian cells for NMR metabolomics studies. The proposed extraction protocol, including cell washing, metabolism quenching and actual extraction of intracellular metabolites, was first optimized on HeLa cells. Efficiency of the protocol, in its globality and for the different individual steps, was assessed by NMR quantification of 27 metabolites from cellular extracts. We show that a single PBS wash provides a seemly compromise between contamination from growth medium and leakage of intracellular metabolites. In HeLa cells, extraction using pure methanol, without cell scraping, recovered a higher amount of intracellular metabolites than the reference methanol/water/chloroform method with cell scraping, with yields varying across metabolite classes. Optimized and reference protocols were further tested on eight cell lines of miscellaneous nature, and inter-operator reproducibility was demonstrated. Our results stress the need for tailored extraction protocols and show that fast protocols minimizing time-consuming steps, without compromising extraction yields, are suitable for high-throughput metabolomics studies. Graphical abstract.

Metabolomic Approaches to Study Chemical Exposure-Related Metabolism Alterations in Mammalian Cell Cultures.

Biological organisms are constantly exposed to an immense repertoire of molecules that cover environmental or food-derived molecules and drugs, triggering a continuous flow of stimuli-dependent adaptations. The diversity of these chemicals as well as their concentrations contribute to the multiplicity of induced effects, including activation, stimulation, or inhibition of physiological processes and toxicity. Metabolism, as the foremost phenotype and manifestation of life, has proven to be immensely sensitive and highly adaptive to chemical stimuli. Therefore, studying the effect of endo- or xenobiotics over cellular metabolism delivers valuable knowledge to apprehend potential cellular activity of individual molecules and evaluate their acute or chronic benefits and toxicity. The development of modern metabolomics technologies such as mass spectrometry or nuclear magnetic resonance spectroscopy now offers unprecedented solutions for the rapid and efficient determination of metabolic profiles of cells and more complex biological systems. Combined with the availability of well-established cell culture techniques, these analytical methods appear perfectly suited to determine the biological activity and estimate the positive and negative effects of chemicals in a variety of cell types and models, even at hardly detectable concentrations. Metabolic phenotypes can be estimated from studying intracellular metabolites at homeostasis in vivo, while in vitro cell cultures provide additional access to metabolites exchanged with growth media. This article discusses analytical solutions available for metabolic phenotyping of cell culture metabolism as well as the general metabolomics workflow suitable for testing the biological activity of molecular compounds. We emphasize how metabolic profiling of cell supernatants and intracellular extracts can deliver valuable and complementary insights for evaluating the effects of xenobiotics on cellular metabolism. We note that the concepts and methods discussed primarily for xenobiotics exposure are widely applicable to drug testing in general, including endobiotics that cover active metabolites, nutrients, peptides and proteins, cytokines, hormones, vitamins, etc.

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.

Insights on the virulence of swine respiratory tract mycoplasmas through genome-scale metabolic modeling.

BACKGROUND: The respiratory tract of swine is colonized by several bacteria among which are three Mycoplasma species: Mycoplasma flocculare, Mycoplasma hyopneumoniae and Mycoplasma hyorhinis. While colonization by M. flocculare is virtually asymptomatic, M. hyopneumoniae is the causative agent of enzootic pneumonia and M. hyorhinis is present in cases of pneumonia, polyserositis and arthritis. The genomic resemblance among these three Mycoplasma species combined with their different levels of pathogenicity is an indication that they have unknown mechanisms of virulence and differential expression, as for most mycoplasmas. METHODS: In this work, we performed whole-genome metabolic network reconstructions for these three mycoplasmas. Cultivation tests and metabolomic experiments through nuclear magnetic resonance spectroscopy (NMR) were also performed to acquire experimental data and further refine the models reconstructed in silico. RESULTS: Even though the refined models have similar metabolic capabilities, interesting differences include a wider range of carbohydrate uptake in M. hyorhinis, which in turn may also explain why this species is a widely contaminant in cell cultures. In addition, the myo-inositol catabolism is exclusive to M. hyopneumoniae and may be an important trait for virulence. However, the most important difference seems to be related to glycerol conversion to dihydroxyacetone-phosphate, which produces toxic hydrogen peroxide. This activity, missing only in M. flocculare, may be directly involved in cytotoxicity, as already described for two lung pathogenic mycoplasmas, namely Mycoplasma pneumoniae in human and Mycoplasma mycoides subsp. mycoides in ruminants. Metabolomic data suggest that even though these mycoplasmas are extremely similar in terms of genome and metabolism, distinct products and reaction rates may be the result of differential expression throughout the species. CONCLUSIONS: We were able to infer from the reconstructed networks that the lack of pathogenicity of M. flocculare if compared to the highly pathogenic M. hyopneumoniae may be related to its incapacity to produce cytotoxic hydrogen peroxide. Moreover, the ability of M. hyorhinis to grow in diverse sites and even in different hosts may be a reflection of its enhanced and wider carbohydrate uptake. Altogether, the metabolic differences highlighted in silico and in vitro provide important insights to the different levels of pathogenicity observed in each of the studied species.

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.

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.

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.

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 (1)H 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.

µHigh resolution-magic-angle spinning NMR spectroscopy for metabolic phenotyping of Caenorhabditis elegans.

Analysis of model organisms, such as the submillimeter-size Caenorhabditis elegans, plays a central role in understanding biological functions across species and in characterizing phenotypes associated with genetic mutations. In recent years, metabolic phenotyping studies of C. elegans based on (1)H high-resolution magic-angle spinning (HR-MAS) nuclear magnetic resonance (NMR) spectroscopy have relied on the observation of large populations of nematodes, requiring labor-intensive sample preparation that considerably limits high-throughput characterization of C. elegans. In this work, we open new platforms for metabolic phenotyping of C. elegans mutants. We determine rich metabolic profiles (31 metabolites identified) from samples of 12 individuals using a (1)H NMR microprobe featuring high-resolution magic-angle coil spinning (HR-MACS), a simple conversion of a standard HR-MAS probe to µHR-MAS. In addition, we characterize the metabolic variations between two different strains of C. elegans (wild-type vs slcf-1 mutant). We also acquire a NMR spectrum of a single C. elegans worm at 23.5 T. This study represents the first example of a metabolomic investigation carried out on a small number of submillimeter-size organisms, demonstrating the potential of NMR microtechnologies for metabolomics screening of small model organisms.

Batch profiling calibration for robust NMR metabonomic data analysis.

Metabonomic studies involve the analysis of large numbers of samples to identify significant changes in the metabolic fingerprints of biological systems, possibly with sufficient statistical power for analysis. While procedures related to sample preparation and spectral data acquisition generally include the use of independent sample batches, these might be sources of systematic variation whose effects should be removed to focus on phenotyping the relevant biological variability. In this work, we describe a grouped-batch profile (GBP) calibration strategy to adjust nuclear magnetic resonance (NMR) metabolomic data-sets for batch effects either introduced during NMR experiments or samples work-up. We show how this method can be applied to data calibration in the context of a large-scale NMR epidemiological study where quality control samples are available. We also illustrate the efficiency of a batch profile correction for NMR metabonomic investigation of cell extracts, where GBP can significantly improve the predictive power of multivariate statistical models for discriminant analysis of the cell infection status. The method is applicable to a broad range of NMR metabolomic/metabonomic cohort studies.

NOX2 control over energy metabolism plays a role in acute myeloid leukaemia prognosis and survival.

Acute myeloid leukaemia (AML) is a highly heterogeneous disease, however the therapeutic approaches have hardly changed in the last decades. Metabolism rewiring and the enhanced production of reactive oxygen species (ROS) are hallmarks of cancer. A deeper understanding of these features could be instrumental for the development of specific AML-subtypes treatments. NADPH oxidases (NOX), the only cellular system specialised in ROS production, are also involved in leukemic metabolism control. NOX2 shows a variable expression in AML patients, so patients can be classified based on such difference. Here we have analysed whether NOX2 levels are important for AML metabolism control. The lack of NOX2 in AML cells slowdowns basal glycolysis and oxidative phosphorylation (OXPHOS), along with the accumulation of metabolites that feed such routes, and a sharp decrease of glutathione. In addition, we found changes in the expression of 725 genes. Among them, we have discovered a panel of 30 differentially expressed metabolic genes, whose relevance was validated in patients. This panel can segregate AML patients according to CYBB expression, and it can predict patient prognosis and survival. In summary, our data strongly support the relevance of NOX2 for AML metabolism, and highlights the potential of our discoveries in AML prognosis.

The fate of mitochondria during platelet activation.

Blood platelets undergo several successive motor-driven reorganizations of the cytoskeleton when they are recruited to an injured part of a vessel. These reorganizations take place during the platelet activation phase, the spreading process on the injured vessel or between fibrin fibers of the forming clot, and during clot retraction. All these steps require a lot of energy, especially the retraction of the clot when platelets develop strong forces similar to those of muscle cells. Platelets can produce energy through glycolysis and mitochondrial respiration. However, although resting platelets have only 5 to 8 individual mitochondria, they produce adenosine triphosphate predominantly via oxidative phosphorylation. Activated, spread platelets show an increase in size compared with resting platelets, and the question arises as to where the few mitochondria are located in these larger platelets. Using expansion microscopy, we show that the number of mitochondria per platelet is increased in spread platelets. Live imaging and focused ion beam-scanning electron microscopy suggest that a mitochondrial fission event takes place during platelet activation. Fission is Drp1 dependent because Drp1-deficient platelets have fused mitochondria. In nucleated cells, mitochondrial fission is associated with a shift to a glycolytic phenotype, and using clot retraction assays, we show that platelets have a more glycolytic energy production during clot retraction and that Drp1-deficient platelets show a defect in clot retraction.

Predictive toxicology using systemic biology and liver microfluidic "on chip" approaches: application to acetaminophen injury.

We have analyzed transcriptomic, proteomic and metabolomic profiles of hepatoma cells cultivated inside a microfluidic biochip with or without acetaminophen (APAP). Without APAP, the results show an adaptive cellular response to the microfluidic environment, leading to the induction of anti-oxidative stress and cytoprotective pathways. In presence of APAP, calcium homeostasis perturbation, lipid peroxidation and cell death are observed. These effects can be attributed to APAP metabolism into its highly reactive metabolite, N-acetyl-p-benzoquinone imine (NAPQI). That toxicity pathway was confirmed by the detection of GSH-APAP, the large production of 2-hydroxybutyrate and 3-hydroxybutyrate, and methionine, cystine, and histidine consumption in the treated biochips. Those metabolites have been reported as specific biomarkers of hepatotoxicity and glutathione depletion in the literature. In addition, the integration of the metabolomic, transcriptomic and proteomic collected profiles allowed a more complete reconstruction of the APAP injury pathways. To our knowledge, this work is the first example of a global integration of microfluidic biochip data in toxicity assessment. Our results demonstrate the potential of that new approach to predictive toxicology.

Identification of Modulators of the C. elegans Aryl Hydrocarbon Receptor and Characterization of Transcriptomic and Metabolic AhR-1 Profiles.

The Aryl hydrocarbon Receptor (AhR) is a xenobiotic sensor in vertebrates, regulating the metabolism of its own ligands. However, no ligand has been identified to date for any AhR in invertebrates. In C. elegans, the AhR ortholog, AHR-1, displays physiological functions. Therefore, we compared the transcriptomic and metabolic profiles of worms expressing AHR-1 or not and investigated the putative panel of chemical AHR-1 modulators. The metabolomic profiling indicated a role for AHR-1 in amino acids, carbohydrates, and fatty acids metabolism. The transcriptional profiling in neurons expressing AHR-1, identified 95 down-regulated genes and 76 up-regulated genes associated with neuronal and metabolic functions in the nervous system. A gene reporter system allowed us to identify several AHR-1 modulators including bacterial, dietary, or environmental compounds. These results shed new light on the biological functions of AHR-1 in C. elegans and perspectives on the evolution of the AhR functions across species.

Effects of an Exercise and Nutritional Intervention on Circulating Biomarkers and Metabolomic Profiling During Adjuvant Treatment for Localized Breast Cancer: Results From the PASAPAS Feasibility Randomized Controlled Trial.

PURPOSE: Exercise has been shown to improve physical and psychological conditions during cancer therapy, but mechanisms remain poorly understood. The purpose of the present study was to report the results of cancer-related biomarkers and metabolomics outcomes from the PASAPAS feasibility study. METHODS: In the PASAPAS randomized controlled trial, 61 women beginning adjuvant chemotherapy for localized breast cancer were randomized in a 6-month program of weekly aerobic exercises associated with nutritional counseling versus usual care with nutritional counseling. In the present analysis of 58 women for whom blood samples were available, first, circulating levels of biomarkers (ie, insulin, insulin-like growth factor 1, estradiol, adiponectin, leptin, interleukin-6, and tumor necrosis factor α) were measured at baseline and 6-month follow-up. Changes in biomarkers were compared between exercisers (n = 40) and controls (n = 18) using mixed-effect models. Second, serum metabolites were studied using an untargeted 1H nuclear magnetic resonance spectroscopy, and orthogonal partial least squares analyses were performed to discriminate exercisers and controls at baseline and at 6 months. RESULTS: Over the 6-month intervention, no statistically significant differences were observed between exercisers and controls regarding changes in biomarkers and metabolomic profiles. CONCLUSION: The present analysis of the PASAPAS feasibility trial did not reveal any improvement in circulating biomarkers nor identified metabolic signatures in exercisers versus controls during adjuvant breast cancer treatment. Larger studies preferably in women with poor physical activity level to avoid ceiling effect, testing different doses and types of exercise on additional biological pathways, could allow to clarify the mechanisms mediating beneficial effects of physical exercise during cancer treatment. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT01331772. Registered 8 April 2011, https://clinicaltrials.gov/ct2/show/NCT01331772?term=pasapas&rank=1.

Two-dimensional statistical recoupling for the identification of perturbed metabolic networks from NMR spectroscopy.

The development of Statistical Total Correlation Spectroscopy (STOCSY), a representation of the autocorrelation matrix of a spectral data set as a 2D pseudospectrum, has allowed more reliable assignment of one- and two-dimensional NMR spectra acquired from the complex mixtures that are usually used in metabolomics/metabonomics studies, thus, improving precise identification of candidate biomarkers contained in metabolic signatures computed by multivariate statistical analysis. However, the correlations obtained cannot always be interpreted in terms of connectivities between metabolites. In this study, we combine statistical recoupling of variables (SRV) and STOCSY to identify perturbed metabolite systems. The resulting Recoupled-STOCSY (R-STOCSY) method provides a 2D correlation landscape based on the SRV clusters representing physical, chemical, and biological entities. This enables the identification of correlations between distant clusters and extends the recoupling scheme of SRV, which was previously limited to the association of neighboring clusters. This allows the recovery of only meaningful correlations between metabolic signals and significantly enhances the interpretation of STOCSY. The method is validated through the measurement of the distances between the metabolites involved in these correlations, within the whole metabolic network, which shows that the average shortest path length is significantly shorter for the correlations detected in this new way compared to metabolite couples randomly selected from within the entire KEGG metabolic network. This enables the identification without any a priori knowledge of the perturbed metabolic network. The R-STOCSY completes the recoupling procedure between distant clusters, further reducing the high dimensionality of metabolomics/metabonomics data set and finally allows the identification of composite biomarkers, highlighting disruption of particular metabolic pathways within a global metabolic network. This allows the perturbed metabolic network to be extracted through NMR based metabolomics/metabonomics in an automated, and statistical manner.

Ab initio simulation of proton spin diffusion.

The many-body nature of the ubiquitous spin diffusion phenomenon makes it difficult to predict accurately from first principles. We show how the use of reduced Liouville spaces makes it possible to reproduce experimental proton spin diffusion measurements directly from crystalline geometry for powdered solids under magic-angle spinning.