Alzheimer's disease (AD) is the most prevalent form of dementia with an estimated worldwide prevalence of over 30 million people, and its incidence is expected to increase dramatically with an increasing elderly population. Up until now, cerebrospinal fluid (CSF) has been the preferred sample to investigate central nervous system (CNS) disorders since its composition is directly related to metabolite production in the brain. In this work, a nontargeted metabolomic approach based on capillary electrophoresis-mass spectrometry (CE-MS) is developed to examine metabolic differences in CSF samples from subjects with different cognitive status related to AD progression. To do this, CSF samples from 85 subjects were obtained from patients with (i) subjective cognitive impairment (SCI, i.e. control group), (ii) mild cognitive impairment (MCI) which remained stable after a follow-up period of 2 years, (iii) MCI which progressed to AD within a 2-year time after the initial MCI diagnostic and, (iv) diagnosed AD. A prediction model for AD progression using multivariate statistical analysis based on CE-MS metabolomics of CSF samples was obtained using 73 CSF samples. Using our model, we were able to correctly classify 97-100% of the samples in the diagnostic groups. The prediction power was confirmed in a blind small test set of 12 CSF samples, reaching a 83% of diagnostic accuracy. The obtained predictive values were higher than those reported with classical CSF AD biomarkers (Aβ42 and tau) but need to be confirmed in larger samples cohorts. Choline, dimethylarginine, arginine, valine, proline, serine, histidine, creatine, carnitine, and suberylglycine were identified as possible disease progression biomarkers. Our results suggest that CE-MS metabolomics of CSF samples can be a useful tool to predict AD progression.

In this work, the contribution of carnosic acid (CA) and carnosol (CS), two major compounds present in rosemary, against colon cancer HT-29 cells proliferation is investigated using a comprehensive Foodomics approach. The Foodomics study reveals that CA induces transcriptional activation of genes that encode detoxifying enzymes and altered the expression of genes linked to transport and biosynthesis of terpenoids in the colon cancer cell line. Functional analysis highlighted the activation of the ROS metabolism and alteration of several genes involved in pathways describing oxidative degradation of relevant endogenous metabolites, providing new evidence about the transcriptional change induced by CA in HT-29 cells. Metabolomics analysis showed that the treatment with CA affected the intracellular levels of glutathione. Elevated levels of GSH provided additional evidence to transcriptomic results regarding chemopreventive response of cells to CA treatment. Moreover, the Foodomics approach was useful to establish the links between decreased levels of N-acetylputrescine and its degradation pathway at the gene level. The findings from this work and the predictions based on microarray data will help explore novel metabolic processes and potential signaling pathways to further elucidate the effect of CA in colon cancer cells.

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In this study, an analytical multiplatform is presented to carry out a broad metabolomic study on the anti-proliferative effect of dietary polyphenols on human colon cancer cells. CE, RP/UPLC, and HILIC/UPLC all coupled to TOF MS were combined to achieve a global metabolomic examination of the effect of dietary polyphenols on HT29 colon cancer cells. By the use of a nontargeted metabolomic approach, metabolites showing significant different expression after the polyphenols treatment were identified in colon cancer cells. It was demonstrated that this multianalytical platform provided extensive metabolic information and coverage due to its complementary nature. Differences observed in metabolic profiles from CE-TOF MS, RP/UPLC-TOF MS, and HILIC/UPLC-TOF MS can be mainly assigned to their different separation mechanisms without discarding the influence of the different tools used for data processing. Changes in glutathione metabolism with an enhanced reduced glutathione/oxidized glutathione (GSH/GSSG) ratio were detected in polyphenols-treated cells. Moreover, significant alterations in polyamines content with important implications in cancer proliferation were observed after the treatment with polyphenols. These results from metabolomics can explain the chemopreventive effect of the tested dietary polyphenols on colon cancer and may be of importance for future prevention and/or treatment of this disease.

In this work, the analysis of foods and food components using capillary electromigration methods is reviewed. The present work presents and discusses the main CE applications performed in Food Science and Technology including the new field of Foodomics, reviewing recent results on food quality and safety, nutritional value, storage, bioactivity, as well as applications of CE for monitoring food interactions and food processing. The CE analysis of a large variety of food-related molecules with different chemical properties, including amino acids, peptides, proteins, phenolic compounds, carbohydrates, DNA fragments, vitamins, toxins, pesticides, additives, and other minor compounds is described. The use of microchips, CE-MS, and chiral-CE in food analysis is also discussed as well as other current and foreseen trends in this area of research. Following the previous review by Castro-Puyana et al. (Electrophoresis, 2012, 33, 147–167), the current review covers the papers that were published from February 2011 to February 2013.

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In this work, a global Foodomics strategy has been applied to study the antiproliferative effect of dietary polyphenols from rosemary on two human leukemia lines, one showing a drug‐sensitive phenotype (K562), and another exhibiting a drug‐resistant phenotype (K562/R). To this aim, whole‐transcriptome microarray together with an MS‐based nontargeted analytical approach (via CE‐TOF MS and UPLC‐TOF MS) have been employed to carry out transcriptomics and metabolomics analyses, respectively. Functional enrichment analysis was done using ingenuity pathway analysis (IPA) software as a previous step for a reliable interpretation of transcriptomic and metabolomic profiles. Rosemary polyphenols altered the expression of approximately 1% of the genes covered by the whole transcriptome microarray in both leukemia cell lines. Overall, differences in the transcriptional induction of a number of genes encoding phase II detoxifying and antioxidant genes, as well as differences in the metabolic profiles observed in the two leukemia cell lines suggest that rosemary polyphenols may exert a differential chemopreventive effect in leukemia cells with different phenotypes. IPA predictions on transcription factor analysis highlighted inhibition of Myc transcription factor function by rosemary polyphenols, which may explain the observed antiproliferative effect of rosemary extract in the leukemia cells. Metabolomics analysis suggested that rosemary polyphenols affected differently the intracellular levels of some metabolites in two leukemia cell sublines. Integration of data obtained from transcriptomics and metabolomics platforms was attempted by overlaying datasets on canonical (defined) metabolic pathways using IPA software. This strategy enabled the identification of several differentially expressed genes in the metabolic pathways modulated by rosemary polyphenols providing more evidences on the effect of these compounds.

Faecal metabolome contains information on the metabolites found in the intestine, from which knowledge about the metabolic function of the gut microbiota can be obtained. Changes in the metabolomic profile of faeces reflect, among others, changes in the composition and activity of the intestinal microorganisms. In an effort to improve our understanding of the biological effects that phenolic compounds (including red wine polyphenols) exert at the gut level, in this foodomic study we have undertaken a metabolome characterization of human faeces after moderate consumption of red wine by healthy subjects for 4 weeks. Namely, a nontargeted metabolomic approach based on the use of UHPLC-TOF MS was developed to achieve the maximum metabolite information on 82 human faecal samples. After data processing and statistical analysis, 37 metabolites were related to wine intake, from which 20 could be tentatively or completely identified, including the following: (A) wine compounds, (B) microbial-derived metabolites of wine polyphenols, and (C) endogenous metabolites and/or others derived from other nutrient pathways. After wine consumption, faecal metabolome was fortified in flavan-3-ols metabolites. Also, of relevance was the down regulation of xanthine and bilirubin-derived metabolites such as urobilinogen and stercobilin after moderate wine consumption. As far as we know, this is the first study of the faecal metabolome after wine intake.