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Purpose: Long-term treatment-related toxicities, such as neurologic and metabolic toxicities, are major issues in breast cancer. We investigated the interest of metabolomic profiling to predict toxicities., Experimental Design: Untargeted high-resolution metabolomic profiles of 992 patients with estrogen receptor (ER)+/HER2- breast cancer from the prospective CANTO cohort were acquired (n = 1935 metabolites). A residual-based modeling strategy with discovery and validation cohorts was used to benchmark machine learning algorithms, taking into account confounding variables., Results: Adaptive Least Absolute Shrinkage and Selection (adaptive LASSO) has a good predictive performance, has limited optimism bias, and allows the selection of metabolites of interest for future translational research. The addition of low-frequency metabolites and nonannotated metabolites increases the predictive power. Metabolomics adds extra performance to clinical variables to predict various neurologic and metabolic toxicity profiles., Conclusions: Untargeted high-resolution metabolomics allows better toxicity prediction by considering environmental exposure, metabolites linked to microbiota, and low-frequency metabolites., (©2024 American Association for Cancer Research.)

Introduction: Accuracy of feature annotation and metabolite identification in biological samples is a key element in metabolomics research. However, the annotation process is often hampered by the lack of spectral reference data in experimental conditions, as well as logistical difficulties in the spectral data management and exchange of annotations between laboratories., Objectives: To design an open-source infrastructure allowing hosting both nuclear magnetic resonance (NMR) and mass spectra (MS), with an ergonomic Web interface and Web services to support metabolite annotation and laboratory data management., Methods: We developed the PeakForest infrastructure, an open-source Java tool with automatic programming interfaces that can be deployed locally to organize spectral data for metabolome annotation in laboratories. Standardized operating procedures and formats were included to ensure data quality and interoperability, in line with international recommendations and FAIR principles., Results: PeakForest is able to capture and store experimental spectral MS and NMR metadata as well as collect and display signal annotations. This modular system provides a structured database with inbuilt tools to curate information, browse and reuse spectral information in data treatment. PeakForest offers data formalization and centralization at the laboratory level, facilitating shared spectral data across laboratories and integration into public databases., Conclusion: PeakForest is a comprehensive resource which addresses a technical bottleneck, namely large-scale spectral data annotation and metabolite identification for metabolomics laboratories with multiple instruments. PeakForest databases can be used in conjunction with bespoke data analysis pipelines in the Galaxy environment, offering the opportunity to meet the evolving needs of metabolomics research. Developed and tested by the French metabolomics community, PeakForest is freely-available at https://github.com/peakforest ., (© 2022. The Author(s).)

Purpose: Dietary intakes are reflected in plasma by the presence of hundreds of exogenous metabolites and variations in endogenous metabolites. The exploration of diet-related plasma metabolic profiles could help to better understand the impact of overall diet on health. Our aim was to identify metabolomic signatures reflecting overall diet in women from the French general population., Methods: This cross-sectional study included 160 women in the SU.VI.MAX cohort with detailed dietary data (≥ 10 24-h dietary records) selected according to their level of adherence to the French dietary recommendations, represented by the validated score mPNNS-GS; 80 women from the 10th decile of the score were matched with 80 women from the 1st decile. Plasma metabolomic profiles were acquired using untargeted UPLC-QToF mass spectrometry analysis. The associations between metabolomic profiles and the mPNNG-GS, its components and Principal Component Analyses-derived dietary patterns were investigated using multivariable conditional logistic regression models and partial correlations., Results: Adherence to the dietary recommendations was positively associated with 3-indolepropionic acid and pipecolic acid (also positively associated with fruit and vegetable intake and a healthy diet)-2 metabolites linked to microbiota and inversely associated with lysophosphatidylcholine (LysoPC(17:1)), acylcarnitine C9:1 (also inversely associated with a healthy diet), acylcarnitine C11:1 and 2-deoxy-D-glucose. Increased plasma levels of piperine and Dihydro4mercapto-3(2H) furanone were observed in women who consumed a Western diet and a healthy diet, respectively. Ethyl-β-D-glucopyranoside was positively associated with alcohol intake. Plasma levels of LysoPC(17:1), cholic acid, phenylalanine-phenylalanine and phenylalanine and carnitine C9:1 decreased with the consumption of vegetable added fat, sweetened food, milk and dairy products and fruit and vegetable intakes, respectively., Conclusion: This study highlighted several metabolites from both host and microbial metabolism reflecting the long-term impact of the overall diet., Trial Registration: SU.VI.MAX, clinicaltrials.gov NCT00272428. Registered 3 January 2006, https://clinicaltrials.gov/show/NCT00272428.

Background: Diet has been recognized as a modifiable risk factor for breast cancer. Highlighting predictive diet-related biomarkers would be of great public health relevance to identify at-risk subjects. The aim of this exploratory study was to select diet-related metabolites discriminating women at higher risk of breast cancer using untargeted metabolomics., Methods: Baseline plasma samples of 200 incident breast cancer cases and matched controls, from a nested case-control study within the Supplémentation en Vitamines et Minéraux Antioxydants (SU.VI.MAX) cohort, were analyzed by untargeted LC-MS. Diet-related metabolites were identified by partial correlation with dietary exposures, and best predictors of breast cancer risk were then selected by Elastic Net penalized regression. The selection stability was assessed using bootstrap resampling., Results: 595 ions were selected as candidate diet-related metabolites. Fourteen of them were selected by Elastic Net regression as breast cancer risk discriminant ions. A lower level of piperine (a compound from pepper) and higher levels of acetyltributylcitrate (an alternative plasticizer to phthalates), pregnene-triol sulfate (a steroid sulfate), and 2-amino-4-cyano butanoic acid (a metabolite linked to microbiota metabolism) were observed in plasma from women who subsequently developed breast cancer. This metabolomic signature was related to several dietary exposures such as a "Western" dietary pattern and higher alcohol and coffee intakes., Conclusions: Our study suggested a diet-related plasma metabolic signature involving exogenous, steroid metabolites, and microbiota-related compounds associated with long-term breast cancer risk that should be confirmed in large-scale independent studies., Impact: These results could help to identify healthy women at higher risk of breast cancer and improve the understanding of nutrition and health relationship., (©2019 American Association for Cancer Research.)

The life sciences are currently being transformed by an unprecedented wave of developments in molecular analysis, which include important advances in instrumental analysis as well as biocomputing. In light of the central role played by metabolism in nutrition, metabolomics is rapidly being established as a key analytical tool in human nutritional studies. Consequently, an increasing number of nutritionists integrate metabolomics into their study designs. Within this dynamic landscape, the potential of nutritional metabolomics (nutrimetabolomics) to be translated into a science, which can impact on health policies, still needs to be realized. A key element to reach this goal is the ability of the research community to join, to collectively make the best use of the potential offered by nutritional metabolomics. This article, therefore, provides a methodological description of nutritional metabolomics that reflects on the state-of-the-art techniques used in the laboratories of the Food Biomarker Alliance (funded by the European Joint Programming Initiative "A Healthy Diet for a Healthy Life" (JPI HDHL)) as well as points of reflections to harmonize this field. It is not intended to be exhaustive but rather to present a pragmatic guidance on metabolomic methodologies, providing readers with useful "tips and tricks" along the analytical workflow., (© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

GC/MS-based metabolomics is a powerful tool for metabolic phenotyping and biomarker discovery from body biofluids. In this chapter, we describe an untargeted metabolomic approach for plasma/serum and fecal water sample profiling. It describes a multistep procedure, from sample preparation, oximation/silylation derivatization, and data acquisition using GC/QToF to data processing consisting in data extraction and identification of metabolites.

The objective was to compare the metabolic responses of high-level national swimmers to threshold or polarised training. 22 swimmers (n = 12 males and 10 females) participated in a 28-week cross-over intervention study consisting of 2 × 6 period weeks of training. Swimmers were assigned randomly to either training group for the first period: polarised (POL) (81% in energetic zone 1: blood lactate [La]b ≤ 2 mmol.L−1; 4% in zone 2: 2 mmol.L−1 <[La]b ≤ 4 mmol.L−1; 15% in zone 3: [La]b > 4 mmol.L−1) or threshold (THR) (65%/25%/10%). Before and after each training period, urine samples were collected for non-targeted metabolomics analysis. Mixed model analysis was performed on metabolomics data including fatigue class factors and/or training and/or interaction. Ion intensities of 6-keto-decanoylcarnitine (+31%), pregnanediol-3-glucuronide (+81%), P-cresol sulphate (+18%) were higher in the threshold group (P < 0.05) indicating higher glycogenic depletion and inflammation without alteration of the neuroendocrine stress axis. 4-phenylbutanic acid sulphate was 200% higher in less fatigued swimmers (P < 0.01) linking the anti-inflammatory activity at the cell membrane level to the subjective perception of fatigue. This research suggests the importance of replenishing glycogen stores and reducing inflammation during high thresholds training loads. [ABSTRACT FROM AUTHOR]

There is scarce information on whether inhibition of rumen methanogenesis induces metabolic changes on the host ruminant. Understanding these possible changes is important for the acceptance of methane-reducing practices by producers. In this study we explored the changes in plasma profiles associated with the reduction of methane emissions. Plasma samples were collected from lactating primiparous Holstein cows fed the same diet with (Treated, n = 12) or without (Control, n = 13) an anti-methanogenic feed additive for six weeks. Daily methane emissions (CH4, g/d) were reduced by 23% in the Treated group with no changes in milk production, feed intake, body weight, and biochemical indicators of health status. Plasma metabolome analyses were performed using untargeted [nuclear magnetic resonance (NMR) and liquid chromatography-mass spectrometry (LC–MS)] and targeted (LC–MS/MS) approaches. We identified 48 discriminant metabolites. Some metabolites mainly of microbial origin such as dimethylsulfone, formic acid and metabolites containing methylated groups like stachydrine, can be related to rumen methanogenesis and can potentially be used as markers. The other discriminant metabolites are produced by the host or have a mixed microbial-host origin. These metabolites, which increased in treated cows, belong to general pathways of amino acids and energy metabolism suggesting a systemic non-negative effect on the animal. [ABSTRACT FROM AUTHOR]

Lack of reliable peak detection impedes automated analysis of large-scale gas chromatography-mass spectrometry (GC-MS) metabolomics datasets. Performance and outcome of individual peak-picking algorithms can differ widely depending on both algorithmic approach and parameters, as well as data acquisition method. Therefore, comparing and contrasting between algorithms is difficult. Here we present a workflow for improved peak picking (WiPP), a parameter optimising, multi-algorithm peak detection for GC-MS metabolomics. WiPP evaluates the quality of detected peaks using a machine learning-based classification scheme based on seven peak classes. The quality information returned by the classifier for each individual peak is merged with results from different peak detection algorithms to create one final high-quality peak set for immediate down-stream analysis. Medium- and low-quality peaks are kept for further inspection. By applying WiPP to standard compound mixes and a complex biological dataset, we demonstrate that peak detection is improved through the novel way to assign peak quality, an automated parameter optimisation, and results in integration across different embedded peak picking algorithms. Furthermore, our approach can provide an impartial performance comparison of different peak picking algorithms. WiPP is freely available on GitHub (https://github.com/bihealth/WiPP) under MIT licence. [ABSTRACT FROM AUTHOR]