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Supplemental material, sj-docx-1-ict-10.1177_1534735420977666 for 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 by Olivia Febvey-Combes, Elodie Jobard, Adrien Rossary, Vincent Pialoux, Aude-Marie Foucaut, Magali Morelle, Lidia Delrieu, Agnès Martin, Florence Caldefie-Chézet, Marina Touillaud, Sophie E. Berthouze, Houda Boumaza, Bénédicte Elena-Herrmann, Patrick Bachmann, Olivier Trédan, Marie-Paule Vasson and Béatrice Fervers in Integrative Cancer Therapies

International audience; 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 degrees 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.

International audience; BackgroundThe 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.MethodsIn 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.ResultsEven 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.ConclusionsWe 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.

International audience; Many advances in recent years, such as the use of targeted therapies, have enabled to improve the management of cancer patients. Understanding the effect of targeted therapies on the human metabolism is essential to predict the response to treatments and adjust personalized therapy. mTOR is an attractive target for cancer’s therapeutic intervention because of its key role in the regulation of protein translation, 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. We detail the metabolic signatures associated with the response to an HER-2 inhibitor standard treatment (trastuzumab), or a combined HER-2 and mTOR (everolimus) inhibitors therapy. Pre-treatment, on-treatment and post-treatment serum samples were available for 79 patients with HER-2 positive breast cancer from the French multicentre, randomized phase II trial RADHER. Fasting patients were randomized between two treatments: trastuzumab (group A) and a trastuzumab and everolimus combination (experimental group B). For each patient, clinicopathological data were recorded. 340 serum metabolic profiles were obtained using 1H NMR spectroscopy (800MHz). Statistical multivariate methods were exploited to build models for sample classification and extract group-specific metabolic signatures. ANOVA analysis and multiple testing corrections of the p-values were used to derive statistically significant associations of individual metabolites. The longitudinal series of serum samples available for each patient was exploited to investigate the effect of the combination B on the patient’s metabolism over time and to compare it with the standard reference treatment A. For each arm, serum metabolic profiles are compared before, over and after treatment. Similarly, the fingerprints are compared between treatments. Clear and significant O-PLS discrimination is observed only for the arm B between metabolic profiles at baseline and after one week of treatment (N: 57, R2Y = 0.404, Q2 = 0.199), after four weeks of treatment (N: 54, R2Y = 0.603, Q2 = 0.301), one week after the end of treatment (N: 53, R2Y = 0.734, Q2 = 0.569), three weeks after the end of treatment (N =51, R2Y = 0.767, Q2 = 0.493). The trastuzumab and everolimus combination causes faster changes in patient metabolism than standard treatment and a residual effect is also observed several weeks after ending of the treatment (up to 3 weeks). Analysis of metabolic fingerprints highlights the involvement of several metabolic pathways reflecting a systemic effect, particularly on the liver and visceral fat. Furthermore, comparison of the metabolic profiles between the two arms (either four or seven weeks after the beginning of the treatment) shows that everolimus, an mTOR inhibitor, is responsible for the host metabolism modification observed in the experimental arm

International audience; Metabolomics provides untargeted identification of all detectable low molecular-weight molecules by profiling without a priori the metabolic signatures of biological samples in connection to pathophysiological events. The goal of metabolomic studies is to identify relevant biomarkers or composite metabolic patterns associated with particular disease status. Urine is particularly suited for metabolomic analysis in newborns and children, due to its simple and non-invasive method of collection. Its biochemical composition is correlated to a number of factors such as genotype, gender, disease, nutritional state and age. The number of metabolomic studies in pediatrics is rising, but little is known concerning age-related changes in urine metabolic profiles and newborn metabolic maturation over time. The aim of this study was to investigate changes in urine metabolic profiles during the first four months of life using 1H-nuclear magneticresonance (NMR) spectroscopy combined with multivariate statistical analysis. Urine samples were collected from 91 newborns under 4 months old without nephrologic or urologic disease. The mean age was 68 days ± 24. The1H-NMR spectra were analyzed using Principal Component Analysis (PCA) and the effect of age on the urinary metabolite profile was observed even from this unsupervised analysis. Further analysis using Orthogonal Partial Least Squares (OPLS) methodology was performed and a model with good predictive power was calculated, allowing the identification of an age-related metabolic profile. We observed the most significant evolution between 2 and 3 months of life. Our results allow a deeper understanding of newborn metabolic maturation. They contribute to identifying potential confounding factors in the application of metabolomics in newborns.

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