by Keyword: metabolomics

Moussa, Dina G., Sharma, Ashok K., Mansour, Tamer A, Witthuhn, Bruce, Perdigão, Jorge, Rudney, Joel D., Aparicio, Conrado, Gomez, Andres, (2022). Functional signatures of ex-vivo dental caries onset Journal Of Oral Microbiology 14, 2123624

Bartova, S, Madrid-Gambin, F, Fernandez, L, Carayol, J, Meugnier, E, Segrestin, B, Delage, P, Vionnet, N, Boizot, A, Laville, M, Vidal, H, Marco, S, Hager, J, Moco, S, (2022). Grape polyphenols decrease circulating branched chain amino acids in overfed adults Front Nutr 9, 998044

Introduction and aimsDietary polyphenols have long been associated with health benefits, including the prevention of obesity and related chronic diseases. Overfeeding was shown to rapidly induce weight gain and fat mass, associated with mild insulin resistance in humans, and thus represents a suitable model of the metabolic complications resulting from obesity. We studied the effects of a polyphenol-rich grape extract supplementation on the plasma metabolome during an overfeeding intervention in adults, in two randomized parallel controlled clinical trials.MethodsBlood plasma samples from 40 normal weight to overweight male adults, submitted to a 31-day overfeeding (additional 50% of energy requirement by a high calorie-high fructose diet), given either 2 g/day grape polyphenol extract or a placebo at 0, 15, 21, and 31 days were analyzed (Lyon study). Samples from a similarly designed trial on females (20 subjects) were collected in parallel (Lausanne study). Nuclear magnetic resonance (NMR)-based metabolomics was conducted to characterize metabolome changes induced by overfeeding and associated effects from polyphenol supplementation. The clinical trials are registered under the numbers NCT02145780 and NCT02225457 atResultsChanges in plasma levels of many metabolic markers, including branched chain amino acids (BCAA), ketone bodies and glucose in both placebo as well as upon polyphenol intervention were identified in the Lyon study. Polyphenol supplementation counterbalanced levels of BCAA found to be induced by overfeeding. These results were further corroborated in the Lausanne female study.ConclusionAdministration of grape polyphenol-rich extract over 1 month period was associated with a protective metabolic effect against overfeeding in adults.

JTD Keywords: Branched chain amino acids, Grape polyphenols, Human trials, Metabolism, Metabolomics, Nmr, Obesity, Overfeeding

Madrid-Gambin F, Gomez-Gomez A, Busquets-Garcia A, Haro N, Marco S, Mason NL, Reckweg JT, Mallaroni P, Kloft L, van Oorsouw K, Toennes SW, de la Torre R, Ramaekers JG, Pozo OJ, (2022). Metabolomics and integrated network analysis reveal roles of endocannabinoids and large neutral amino acid balance in the ayahuasca experience Biomedicine & Pharmacotherapy 149, 112845

There has been a renewed interest in the potential use of psychedelics for the treatment of psychiatric conditions. Nevertheless, little is known about the mechanism of action and molecular pathways influenced by ayahuasca use in humans. Therefore, for the first time, our study aims to investigate the human metabolomics signature after consumption of a psychedelic, ayahuasca, and its connection with both the psychedelic-induced subjective effects and the plasma concentrations of ayahuasca alkaloids. Plasma samples of 23 individuals were collected both before and after ayahuasca consumption. Samples were analysed through targeted metabolomics and further integrated with subjective ratings of the ayahuasca experience (i.e., using the 5-Dimension Altered States of Consciousness Rating Scale [ASC]), and plasma ayahuasca-alkaloids using integrated network analysis. Metabolic pathways enrichment analysis using diffusion algorithms for specific KEGG modules was performed on the metabolic output. Compared to baseline, the consumption of ayahuasca increased N-acyl-ethanolamine endocannabinoids, decreased 2-acyl-glycerol endocannabinoids, and altered several large-neutral amino acids (LNAAs). Integrated network results indicated that most of the LNAAs were inversely associated with 9 out of the 11 subscales of the ASC, except for tryptophan which was positively associated. Several endocannabinoids and hexosylceramides were directly associated with the ayahuasca alkaloids. Enrichment analysis confirmed dysregulation in several pathways involved in neurotransmission such as serotonin and dopamine synthesis. In conclusion, a crosstalk between the circulating LNAAs and the subjective effects is suggested, which is independent of the alkaloid concentrations and provides insights into the specific metabolic fingerprint and mechanism of action underlying ayahuasca experiences. © 2022 The Authors

JTD Keywords: anxiety, dimethyltryptamine, integrative network analysis, metabolism, metabolomics, psychedelics, rats, subjective effects, system, tryptophan, Ayahuasca, Dimethyltryptamine, Integrative network analysis, Metabolomics, Psychedelics, Serotonin 5-ht2a, Subjective effects

Mallafré‐muro C, Llambrich M, Cumeras R, Pardo A, Brezmes J, Marco S, Gumà J, (2021). Comprehensive volatilome and metabolome signatures of colorectal cancer in urine: A systematic review and meta‐analysis Cancers 13,

To increase compliance with colorectal cancer screening programs and to reduce the recommended screening age, cheaper and easy non‐invasiveness alternatives to the fecal immunochemical test should be provided. Following the PRISMA procedure of studies that evaluated the metabolome and volatilome signatures of colorectal cancer in human urine samples, an exhaustive search in PubMed, Web of Science, and Scopus found 28 studies that met the required criteria. There were no restrictions on the query for the type of study, leading to not only colorectal cancer samples versus control comparison but also polyps versus control and prospective studies of surgical effects, CRC staging and comparisons of CRC with other cancers. With this systematic review, we identified up to 244 compounds in urine samples (3 shared compounds between the volatilome and metabolome), and 10 of them were relevant in more than three articles. In the meta-analysis, nine studies met the criteria for inclusion, and the results combining the case‐control and the pre‐/post‐surgery groups, eleven compounds were found to be relevant. Four upregulated metabolites were identified, 3‐hydroxybutyric acid, L‐dopa, L‐histidinol, and N1, N12‐ diacetylspermine and seven downregulated compounds were identified, pyruvic acid, hydroquinone, tartaric acid, and hippuric acid as metabolites and butyraldehyde, ether, and 1,1,6‐ trimethyl‐1,2‐dihydronaphthalene as volatiles.

JTD Keywords: biomarkers, breast, chromatography, colorectal cancer, diagnosis, markers, meta-analysis, metabolomics, metabonomics, n-1,n-12-diacetylspermine, nucleosides, systematic review, urine, validation, volatilomics, Colorectal cancer, Early-stage, Metabolomics, Meta‐analysis, Systematic review, Urine, Volatilomics

Rodríguez-Pérez, R., Fernández, L., Marco, S., (2018). Overoptimism in cross-validation when using partial least squares-discriminant analysis for omics data: a systematic study Analytical and Bioanalytical Chemistry 410, (23), 5981-5992

Advances in analytical instrumentation have provided the possibility of examining thousands of genes, peptides, or metabolites in parallel. However, the cost and time-consuming data acquisition process causes a generalized lack of samples. From a data analysis perspective, omics data are characterized by high dimensionality and small sample counts. In many scenarios, the analytical aim is to differentiate between two different conditions or classes combining an analytical method plus a tailored qualitative predictive model using available examples collected in a dataset. For this purpose, partial least squares-discriminant analysis (PLS-DA) is frequently employed in omics research. Recently, there has been growing concern about the uncritical use of this method, since it is prone to overfitting and may aggravate problems of false discoveries. In many applications involving a small number of subjects or samples, predictive model performance estimation is only based on cross-validation (CV) results with a strong preference for reporting results using leave one out (LOO). The combination of PLS-DA for high dimensionality data and small sample conditions, together with a weak validation methodology is a recipe for unreliable estimations of model performance. In this work, we present a systematic study about the impact of the dataset size, the dimensionality, and the CV technique used on PLS-DA overoptimism when performance estimation is done in cross-validation. Firstly, by using synthetic data generated from a same probability distribution and with assigned random binary labels, we have obtained a dataset where the true classification rate (CR) is 50%. As expected, our results confirm that internal validation provides overoptimistic estimations of the classification accuracy (i.e., overfitting). We have characterized the CR estimator in terms of bias and variance depending on the internal CV technique used and sample to dimensionality ratio. In small sample conditions, due to the large bias and variance of the estimator, the occurrence of extremely good CRs is common. We have found that overfitting peaks when the sample size in the training subset approaches the feature vector dimensionality minus one. In these conditions, the models are neither under- or overdetermined with a unique solution. This effect is particularly intense for LOO and peaks higher in small sample conditions. Overoptimism is decreased beyond this point where the abundance of noisy produces a regularization effect leading to less complex models. In terms of overfitting, our study ranks CV methods as follows: Bootstrap produces the most accurate estimator of the CR, followed by bootstrapped Latin partitions, random subsampling, K-Fold, and finally, the very popular LOO provides the worst results. Simulation results are further confirmed in real datasets from mass spectrometry and microarrays.

JTD Keywords: Metabolomics, Mass spectrometry, Microarrays, Chemometrics, Data analysis, Classification, Method validation