Clara Ibáñez

Toward a Predictive Model of Alzheimer’s Disease Progression Using Capillary Electrophoresis–Mass Spectrometry Metabolomics

Alzheimers 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.

Global Foodomics strategy to investigate the health benefits of dietary constituents.

A global methodology, called Foodomics, which allows carrying out a comprehensive evaluation of the health benefits of food ingredients is presented in this work. The new methodology is based on the combination of several analytical platforms and data processing for Transcriptomics, Proteomics and Metabolomics studies, allowing the determination of changes induced by food ingredients at molecular level. Both, the whole methodological development and its potential are presented through the investigation of a case study following a hypothesis-free strategy. Namely, the chemopreventive effect of polyphenols from rosemary was examined on the total gene, protein and metabolite expression in human HT29 colon cancer cells. Conclusions on the bioactivity of polyphenols against colon cancer cells based on the results from each single platform (Transcriptomics, Proteomics or Metabolomics) are compared with the conclusions based on the integration of the whole results from the three platforms, corroborating the interest of using a global integrative strategy as Foodomics. To our knowledge, although many papers and reviews have been published on this topic, this is the first time that Transcriptomics, Proteomics and Metabolomics platforms are put together to study the health benefits from dietary ingredients against colon cancer cells at gene, protein and metabolite level. Advantages, drawbacks and current challenges of this global analytical strategy are discussed in this work. The results from our study provide new insights on the biological mechanisms involved in the cancer risk reduction properties of dietary constituents.

Metabolomics, peptidomics and proteomics applications of capillary electrophoresis-mass spectrometry in Foodomics: A review

In the current post-genomic era, Foodomics has been defined as a discipline that studies food and nutrition through the application of advanced omics approaches. Foodomics involves the use of genomics, transcriptomics, epigenetics, proteomics, peptidomics, and/or metabolomics to investigate food quality, safety, traceability and bioactivity. In this context, capillary electrophoresis-mass spectrometry (CE-MS) has been applied mainly in food proteomics, peptidomics and metabolomics. The aim of this review work is to present an overview of the most recent developments and applications of CE-MS as analytical platform for Foodomics, covering the relevant works published from 2008 to 2012. The review provides also information about the integration of several omics approaches in the new Foodomics field.

CE/LC-MS multiplatform for broad metabolomic analysis of dietary polyphenols effect on colon cancer cells proliferation

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.

Recent advances in the application of capillary electromigration methods for food analysis and Foodomics: CE and CEC

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.

A new metabolomic workflow for early detection of Alzheimer's disease.

Alzheimers disease (AD) is the most prevalent cause of dementia among older people. Although AD probably starts 20-30 years before first clinical symptoms become noticeable, nowadays it cannot be diagnosed accurately in its early stages. In this work, we present a new MS-based metabolomic approach based on the use of ultra-high performance liquid chromatography-time-of-flight mass spectrometry (UHPLC-TOF MS) to investigate cerebrospinal fluid (CSF) samples from patients with different AD stages. With the aim to obtain wide metabolome coverage two different chromatographic separation modes, namely reversed phase (RP) and hydrophilic interaction chromatography (HILIC), were used. RP/UHPLC-MS and HILIC/UHPLC-MS methods were optimized and applied to analyze CSF samples from 75 patients related to AD progression. Significant metabolic differences in CSF samples from subjects with different cognitive status related to AD progression were detected using this methodology, obtaining a group of potential biomarkers together with a classification model by means of a multivariate statistical analysis. The proposed model predicted the development of AD with an accuracy of 98.7% and specificity and sensitivity values above of 95%.

Effect of dietary polyphenols on K562 leukemia cells: A Foodomics approach

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.

Analysis of chiral amino acids in cerebrospinal fluid samples linked to different stages of Alzheimer disease

Chiral micellar electrokinetic chromatography with laser‐induced fluorescence detection (chiral‐MEKC‐LIF) was used to investigate D‐ and L‐amino acid contents in cerebrospinal fluid (CSF) samples related to different Alzheimer disease (AD) stages. CSF samples were taken from (i) control subjects (S1 pool), (ii) subjects showing a mild cognitive impairment who remained stable (S2 pool), (iii) subjects showing an mild cognitive impairment that progressed to AD (S3 pool) and (iv) subjects diagnosed with AD (S4 pool). The optimized procedure only needed 10 μL of CSF and it included sample cleaning, derivatization with FITC and chiral‐MEKC‐LIF separation. Eighteen standard amino acids were baseline separated with efficiencies up to 703 000 plates/m, high sensitivity (LODs in the nM range) and good resolution (values ranging from 2.6 to 9.5). Using this method, L‐Arg, L‐Leu, L‐Gln, γ‐aminobutyric acid, L‐Ser, D‐Ser, L‐Ala, Gly, L‐Lys, L‐Glu and L‐Asp were detected in all the CSF samples. S3 and S4 samples (i.e. AD subjects) showed significant lower amounts of L‐Arg L‐Lys, L‐Glu and L‐Asp compared to the non‐AD S1 and S2 samples, showing in the S4 group the lowest amounts of L‐Arg L‐Lys, L‐Glu and L‐Asp. Moreover, γ‐aminobutyric acid was significantly higher in AD subjects with the highest amount also found for S4. No significant differences were observed for the rest of amino acids including D‐Ser. Based on the obtained chiral‐MEKC‐LIF data, it was possible to correctly classify all the samples into the four groups. These results demonstrate that the use of enantioselective procedures as the one developed in this work can provide some new light on the investigations of AD, including the discovery of new biomarkers related to different stages of AD.

Is metabolomics reachable? Different purification strategies of human colon cancer cells provide different CE-MS metabolite profiles

In this work, four different metabolite purification approaches are investigated prior to metabolomics of human HT29 colon cancer cells. Namely, methanol deproteinization, ultrafiltration and two SPE methods using C18 and polymer‐based cartridges were studied. The extracts were characterized via a metabolomic approach based on the application of CE TOF MS (CE‐MS). CE‐MS analysis time was less than 20 min per sample and allowed the simultaneous and reproducible analysis of more than 80 metabolites in a single run with a minimum consumption of sample and reagents. Metabolome analysis revealed in some cases important differences among the studied metabolite purification procedures. No significant differences were observed in the metabolite profile using C18 and polymer‐based cartridges, or between ultrafiltration and methanol deproteinization. However, important differences were observed in the metabolomic profiles obtained from SPE and methanol deproteinization samples. These results demonstrate the crucial role of the metabolite purification strategy in metabolomics since it can bias (and in some cases mislead) the conclusions achieved by the metabolomic study.

Comprehensive foodomics study on the mechanisms operating at various molecular levels in cancer cells in response to individual rosemary polyphenols.

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.