Dysregulation of Lipid Metabolism Serves as A Link Between Alzheimer's and Cardiovascular Disease, As Witnessed in A Cross-Sectional Study.

Cardiovascular risk factors and established cardiovascular disease (CVD) increase the risk of suffering dementia of the Alzheimer's type (DAT). Here, we set out to define specific molecular profiles of CVD in patients with DAT to better understand its relationship, to unravel the mechanisms underlying the high risk of developing DAT in CVD patients and to define new markers of early disease. Plasma samples from patients with DAT, with and without CVD, were analyzed through a multiomics approach, with integration of metabolomics and proteomics datasets using the OmicsNet web-based tool. Metabolomics results showed an enrichment in lipids and lipid-like molecules. Similarly, the most significant cluster identified through proteomics was formed by 5 proteins related to lipoprotein and cholesterol metabolism. After integration and functional enrichment, glycerolipid metabolism, fatty acid degradation and sphingolipid metabolism were among the most significant functions. Finally, differential expression of ABCA1 and APOH proteins was verified, in an independent cohort also including controls and patients with CVD alone. Both proteins positively correlated with phospho-Tau (181), a classical hallmark of DAT. Different molecular profiles exist in patients with DAT, with and without CVD, with exacerbated alterations in patients in which DAT and CVD co-exist. This information may help to define biomarkers like ABCA1 and APOH that identify patients with cardiovascular dysfunction that are at high risk of developing DAT. Such markers will allow more personalized interventions to be selected, a further step towards precision medicine for individuals whose molecular profiles indicate a distinct response to the same management strategies.

Data Processing and Analysis in Mass Spectrometry-Based Metabolomics.

Metabolomics is the latest of the omics sciences. It attempts to measure and characterize metabolites-small chemical compounds <1500 Da-on cells, tissue, or biofluids, which are usually products of biological reactions. As metabolic reactions are closer to the phenotype, metabolomics has emerged as an attractive science for various areas of research, including personalized medicine. However, due to the complexity of data obtained and the absence of curated databases for metabolite identification, data processing is the major bottleneck in this area since most technicians lack the required bioinformatics expertise to process datasets in a reliable and fast manner. The aim of this chapter is to describe the available tools for data processing that makes an inexperienced researcher capable of obtaining reliable results without having to undergo through huge parametrization steps.

MetaboQC: A tool for correcting untargeted metabolomics data with mass spectrometry detection using quality controls.

Nowadays most metabolomic studies involve the analysis of large sets of samples to find a representative metabolite pattern associated to the factor under study. During a sequence of analyses the instrument signals can be subjected to the influence of experimental variability sources. Implementation of quality control (QC) samples to check the contribution of experimental variability is the most common approach in metabolomics. This practice is based on the filtration of molecular entities experiencing a variation coefficient higher than that measured in the QC data set. Although other robust correction algorithms have been proposed, none of them has provided an easy-to-use and easy-to-install tool capable of correcting experimental variability sources. In this research an R-package -the MetaboQC- has been developed to correct intra-day and inter-days variability using QCs analyzed within a pre-set sequence of experiments. MetaboQC has been tested in two data sets to assess the correction effects by comparing the metabolites variability before and after application of the proposed tool. As a result, the number of entities in QCs significantly different between days was reduced from 86% to 19% in the negative ionization mode and from 100% to 13% in the positive ionization mode. Furthermore, principal component analysis allowed detecting the filtration of instrumental variability associated to the injection order.

Comparison of extraction methods for exploitation of grape skin residues from ethanol distillation.

Four extraction techniques-namely, conventional maceration, ultrasound-assisted extraction, microwave-assisted extraction, and superheated liquid extraction (SHLE)-have been compared to evaluate their suitability to obtain valuable compounds from a raw material traditionally of scant interest: grape skin residues from ethanol-distillation. With this aim, red- and white-grape skins were separated from the rest of the pomace residue and subjected to extraction with 1:1 ethanol-acidic water as extractant in order to obtain the largest possible number of valuable compounds from this material, which has so far been used only as a heat source. The resulting extracts were characterized by the Folin-Ciocalteu and Ferric Reducing Antioxidant Power tests and by liquid chromatography-time-of-flight/mass spectrometry (LC-TOF/MS). The composition of the extracts under each of the optimal conditions was studied by LC-TOF/MS, and the information thus obtained compared by Venn diagrams. These diagrams, together with the extracted base peak chromatograms, were used to assess the optimal working conditions. Tentative identification of compounds was conducted using open-free databases. Grape skins from distillation industries are a source of compounds of interest for the food and nutraceutical fields.

Tentative identification of phenolic compounds in olive pomace extracts using liquid chromatography-tandem mass spectrometry with a quadrupole-quadrupole-time-of-flight mass detector.

The reuse of agronomical residues is a pending goal for sustainable agriculture. Particular residues in olive-oil-producing countries are leaves, wastewater, and olive pomace. Olive leaves and wastewaters have been previously characterized by isolation of the phenolic fraction. However, olive pomace has not yet been qualitatively characterized as a source of phenols. Olive pomace extracts were obtained using superheated solvent extraction using 50:50 (v/v) water/ethanol as a leaching mixture at 160 °C. The extracts were analyzed by liquid chromatography coupled to tandem mass spectrometry using a quadrupole-quadrupole-time-of-flight (QqTOF) hybrid mass analyzer (R = 25,000-45,000). Qualitative analysis was supported upon measurement of accurate masses for precursor and product ions as well as their isotopic distribution. Identification was focused on the main families of phenolic compounds present in extra virgin olive oil. The potential of this residue as a rich source of phenols with antioxidant properties has been proven.