Francisco Madrid-Gambin

Background: Sarcopenia and frailty are often overlooked in assessing kidney transplant (KT) candidates with chronic kidney disease (CKD), potentially leading to poor post-transplant outcomes. This study aimed to identify metabolites associated with frailty and sarcopenia in KT candidates from the FRAILMar study. Methods: Between June 2016 and June 2020, we evaluated frailty and sarcopenia in 173 KT candidates using the Physical Frailty Phenotype and EGWSOP-2 criteria, respectively. Seventy-five metabolic markers from targeted pathways, previously linked to CKD, sarcopenia or frailty, were measured in serum samples. These markers were analyzed using adjusted and weighted generalized linear models. Metabolomic data were integrated with multi-modal data, such as comorbidities, using a factor-based integration algorithm to identify metabolic phenotypes. Results: Increased metabolites related to energy metabolism and essential amino acids were associated with frailty, mainly Krebs cycle intermediates. Sarcopenic KT candidates showed lower levels of aromatic amino acids, and lower protein/muscle metabolism, energy metabolism and neurotransmission compared with non-sarcopenic patients. Unsupervised multi-modal integration revealed a high-risk metabolic phenotype characterized by the presence of sarcopenia, diabetes mellitus and low body mass index, with alterations in branched-chain amino acids and high activity of lactate dehydrogenase enzyme. Conclusions: Frailty and sarcopenia are common among KT candidates, and their metabolic status reveals notable disruptions in energy and amino acid metabolism. These findings highlight the value of a detailed metabolic assessment to more accurately evaluate patient health status prior to transplantation.

Introduction: There is evidence that sample treatment of blood-based biosamples may affect integral signals in nuclear magnetic resonance-based metabolomics. The presence of macromolecules in plasma/serum samples makes investigating low-molecular-weight metabolites challenging. It is particularly relevant in the targeted approach, in which absolute concentrations of selected metabolites are often quantified based on the area of integral signals. Since there are a few treatments of plasma/serum samples for quantitative analysis without a universally accepted method, this topic remains of interest for future research.Methods: In this work, targeted metabolomic profiling of 43 metabolites was performed on pooled plasma to compare four methodologies consisting of Carr-Purcell-Meiboom-Gill (CPMG) editing, ultrafiltration, protein precipitation with methanol, and glycerophospholipid solid-phase extraction (g-SPE) for phospholipid removal; prior to NMR metabolomics analysis. The effect of the sample treatments on the metabolite concentrations was evaluated using a permutation test of multiclass and pairwise Fisher scores.Results: Results showed that methanol precipitation and ultrafiltration had a higher number of metabolites with coefficient of variation (CV) values above 20%. G-SPE and CPMG editing demonstrated better precision for most of the metabolites analyzed. However, differential quantification performance between procedures were metabolite-dependent. For example, pairwise comparisons showed that methanol precipitation and CPMG editing were suitable for quantifying citrate, while g-SPE showed better results for 2-hydroxybutyrate and tryptophan.Discussion: There are alterations in the absolute concentration of various metabolites that are dependent on the procedure. Considering these alterations is essential before proceeding with the quantification of treatment-sensitive metabolites in biological samples for improving biomarker discovery and biological interpretations. The study demonstrated that g-SPE and CPMG editing are effective methods for removing proteins and phospholipids from plasma samples for quantitative NMR analysis of metabolites. However, careful consideration should be given to the specific metabolites of interest and their susceptibility to the sample treatment procedures. These findings contribute to the development of optimized sample preparation protocols for metabolomics studies using NMR spectroscopy.

Whether basal metabolic activity in sperm has any influence on their fertilising capacity has not been explored. Using the pig as a model, the present study investigated the relationship of energetic metabolism with sperm quality and function (assessed through computer-assisted sperm analysis and flow cytometry), and fertility (in vitro fertilisation (IVF) outcomes). In semen samples from 16 boars, levels of metabolites related to glycolysis, ketogenesis and Krebs cycle were determined through a targeted metabolomics approach using liquid chromatography-tandem mass spectrometry. High-quality sperm are associated to greater levels of glycolysis-derived metabolites, and oocyte fertilisation and embryo development are conditioned by the sperm metabolic status. Interestingly, glycolysis appears to be the preferred catabolic pathway of the sperm giving rise to greater percentages of embryos at day 6. In conclusion, this study shows that the basal metabolic activity of sperm influences their function, even beyond fertilisation.

Although looking into the sperm metabolome could help to estimate the reproductive performance, no study has investigated through which mechanisms might sperm metabolism influence fertility outcomes. In this context, the present work sought to interrogate the energy source preferred by pig sperm, as well as the relationship of energetic metabolism with sperm quality, function andin vitrofertilisation (IVF) outcomes. To this end, a targeted metabolomic approach was used to determine the levels of metabolites belonging to glycolysis, ketone bodies and polycarboxylic acids cycle. Here we show that high-quality sperm were associated to greater levels of glycolysis derived metabolites. Additionally, embryo development, but not oocyte fertilisation, was conditioned by sperm metabolic status. Specifically, glycolysis appeared to be the preferential catabolic pathway in sperm giving rise to greater IVF outcomes. This study shows that the basal metabolic activity of sperm influences their function, even beyond post-fertilisation events.

Summary: Nuclear magnetic resonance (NMR)-based metabolomics is widely used to obtain metabolic fingerprints of biological systems. While targeted workflows require previous knowledge of metabolites, prior to statistical analysis, untargeted approaches remain a challenge. Computational tools dealing with fully untargeted NMR-based metabolomics are still scarce or not user-friendly. Therefore, we developed AlpsNMR (Automated spectraL Processing System for NMR), an R package that provides automated and efficient signal processing for untargeted NMR metabolomics. AlpsNMR includes spectra loading, metadata handling, automated outlier detection, spectra alignment and peak-picking, integration and normalization. The resulting output can be used for further statistical analysis. AlpsNMR proved effective in detecting metabolite changes in a test case. The tool allows less experienced users to easily implement this workflow from spectra to a ready-to-use dataset in their routines. Availability and implementation: The AlpsNMR R package and tutorial is freely available to download from http://github.com/sipss/AlpsNMR under the MIT license. Supplementary information: Supplementary data are available at Bioinformatics online.

Functional tea beverages have emerged as a novel approach to achieving health benefits associated with tea. The use of metabolomics may improve the evaluation of their consumption and their effects. The current study aimed to explore the urinary signature of the exposure to a functional high-catechin tea (HCT) using untargeted NMR-based metabolomics. Ten volunteers participated in a crossover intervention study. Individuals consumed an HCT or a control beverage over a period of 28 days. Multilevel partial least squares discriminant analysis (ML-PLS-DA) was used for paired comparisons. A further crossover model was performed to assess the significant changes. The consumption of the HCT resulted in the excretion of theanine, epicatechin, pyrogallol sulfate, higher levels of 3-methyl-2-oxovalerate and succinate, as well as unknown compounds. In conclusion, the present work established novel urinary signatures of a functional drink. Such signatures may be potential biomarkers and/or reflect certain benefits of functional tea beverages.