Cristina Legido-Quigley

A top‐down systems biology view of microbiome‐mammalian metabolic interactions in a mouse model

Symbiotic gut microorganisms (microbiome) interact closely with the mammalian hosts metabolism and are important determinants of human health. Here, we decipher the complex metabolic effects of microbial manipulation, by comparing germfree mice colonized by a human baby flora (HBF) or a normal flora to conventional mice. We perform parallel microbiological profiling, metabolic profiling by 1H nuclear magnetic resonance of liver, plasma, urine and ileal flushes, and targeted profiling of bile acids by ultra performance liquid chromatography–mass spectrometry and short‐chain fatty acids in cecum by GC‐FID. Top‐down multivariate analysis of metabolic profiles reveals a significant association of specific metabotypes with the resident microbiome. We derive a transgenomic graph model showing that HBF flora has a remarkably simple microbiome/metabolome correlation network, impacting directly on the hosts ability to metabolize lipids: HBF mice present higher ileal concentrations of tauro‐conjugated bile acids, reduced plasma levels of lipoproteins but higher hepatic triglyceride content associated with depletion of glutathione. These data indicate that the microbiome modulates absorption, storage and the energy harvest from the diet at the systems level.

Evidence of altered phosphatidylcholine metabolism in Alzheimer's disease

Abberant lipid metabolism is implicated in Alzheimers disease (AD) pathophysiology, but the connections between AD and lipid metabolic pathways are not fully understood. To investigate plasma lipids in AD, a multiplatform screen (n = 35 by liquid chromatography-mass spectrometry and n = 35 by nuclear magnetic resonance) was developed, which enabled the comprehensive analysis of plasma from 3 groups (individuals with AD, individuals with mild cognitive impairment (MCI), and age-matched controls). This screen identified 3 phosphatidylcholine (PC) molecules that were significantly diminished in AD cases. In a subsequent validation study (n = 141), PC variation in a bigger sample set was investigated, and the same 3 PCs were found to be significantly lower in AD patients: PC 16:0/20:5 (p < 0.001), 16:0/22:6 (p < 0.05), and 18:0/22:6 (p < 0.01). A receiver operating characteristic (ROC) analysis of the PCs, combined with apolipoprotein E (ApoE) data, produced an area under the curve predictive value of 0.828. Confirmatory investigations into the background biochemistry indiciated no significant change in plasma levels of 3 additional PCs of similar structure, total choline containing compounds or total plasma omega fatty acids, adding to the evidence that specific PCs play a role in AD pathology.

A proposed metabolic strategy for monitoring disease progression in Alzheimer's disease

A specific, sensitive and essentially non‐invasive assay to diagnose and monitor Alzheimers disease (AD) would be valuable to both clinicians and medical researchers. The aim of this study was to perform a metabonomic statistical analysis on plasma fingerprints. Objectives were to investigate novel biomarkers indicative of AD, to consider the role of bile acids as AD biomarkers and to consider whether mild cognitive impairment (MCI) is a separate disease from AD. Samples were analysed by ultraperformance liquid chromatography–MS and resulting data sets were interpreted using soft‐independent modelling of class analogy statistical analysis methods. PCA models did not show any grouping of subjects by disease state. Partial least‐squares discriminant analysis (PLS‐DS) models yielded class separation for AD. However, as with earlier studies, model validation revealed a predictive power of Q2<0.5 and indicating their unsuitability for predicting disease state. Three bile acids were extracted from the data and quantified, up‐regulation was observed for MCI and AD patients. PLS‐DA did not support MCI being considered as a separate disease from AD with MCI patient metabolic profiles being significantly closer to AD patients than controls. This study suggested that further investigation into the lipid fraction of the metabolome may yield useful biomarkers for AD and metabolomic profiles could be used to predict disease state in a clinical setting.

In-Vial Dual Extraction for Direct LC-MS Analysis of Plasma for Comprehensive and Highly Reproducible Metabolic Fingerprinting

Metabolic fingerprinting of biological tissues has become an important area of research, particularly in the biomarker discovery field. Methods have inherent analytical variation, and new approaches are necessary to ensure that the vast numbers of intact metabolites present in biofluids are detected. Here, we describe an in-vial dual extraction (IVDE) method and a direct injection method that shows the total number of features recovered to be over 4500 from a single 20 μL plasma aliquot. By applying a one-step extraction consisting of a lipophilic and hydrophilic layer within a single vial insert, we showed that analytical variation was decreased. This was achieved by reducing sample preparation stages including procedures of drying and transfers. The two phases in the vial, upper and lower, underwent HPLC-QTOF analysis on individually customized LC gradients in both positive and negative ionization modes. A 60 min lipid profiling HPLC-QTOF method for the lipophilic phase was specifically developed, enabling the separation and putative identification of fatty acids, glycerolipids, glycerophospholipids, sphingolipids, and sterols. The aqueous phase of the extract underwent direct injection onto a 45 min gradient, enabling the detection of both polarities. The IVDE method was compared to two traditional extraction methods. The first method was a two-step ether evaporation and IPA resuspension, and the second method was a methanol precipitation typically used in fingerprinting studies. The IVDE provided a 378% increase in reproducible features when compared to evaporation and a 269% increase when compared to the precipitate and inject method. As a proof of concept, the method was applied to an animal model of diabetes. A 2-fold increase in discriminant metabolites was found when comparing diabetic and control rats with IVDE. These discriminant metabolites accounted for around 600 entities, out of which 388 were identified in available databases.

Fast and sensitive high performance liquid chromatography analysis of cosmetic creams for hydroquinone, phenol and six preservatives.

A fast and sensitive HPLC method for analysis of cosmetic creams for hydroquinone, phenol and six preservatives has been developed. The influence of sample preparation conditions and the composition of the mobile phase and elution mode were investigated to optimize the separation of the eight studied components. Final conditions were 60% methanol and 40% water (v/v) extraction of the cosmetic creams. A C18 column (100 mm × 2.1 mm) was used as the separation column and the mobile phase consisted of methanol and 0.05 mol/L ammonium formate in water (pH=3.0) with gradient elution. The results showed that complete separation of the eight studied components was achieved within 10 min, the linear ranges were 1.0-200 μg/mL for phenol, 0.1-150 μg/mL for sorbic acid, 2.0-200 μg/mL for benzoic acid, 0.5-200 μg/mL for hydroquinone, methyl paraben, ethyl paraben and propyl paraben, butyl paraben, and good linear correlation coefficient (≥0.9997) were obtained, the detection limit was in the range of 0.05-1.0 μg/mL, the average recovery was between 86.5% and 116.3%, and the relative standard deviation (RSD) was less than 5.0% (n=6). The method is easy, fast and sensitive, it can be employed to analyze component residues in cosmetic creams especially in a quality control setting.

Advances in separation science applied to metabonomics

Metabonomics focuses on metabolite profile changes in diverse living systems caused by a biological perturbation. These metabolite signatures can be achieved with techniques such as gas chromatography, high‐performance liquid chromatography (ultra‐high‐performance/pressure liquid chromatography and capHPLC), capillary electrophoresis, and capillary electrochromatography normally hyphenated with MS. In this review we present the latest developments of the abovementioned techniques applied in the field of metabonomics, with applications covering phytochemistry, toxicology and clinical research.

Plasma lipidomics analysis finds long chain cholesteryl esters to be associated with Alzheimer's disease.

There is an urgent need for the identification of Alzheimer’s disease (AD) biomarkers. Studies have now suggested the promising use of associations with blood metabolites as functional intermediate phenotypes in biomedical and pharmaceutical research. The aim of this study was to use lipidomics to identify a battery of plasma metabolite molecules that could predict AD patients from controls. We performed a comprehensive untargeted lipidomic analysis, using ultra-performance liquid chromatography/mass spectrometry on plasma samples from 35 AD patients, 40 elderly controls and 48 individuals with mild cognitive impairment (MCI) and used multivariate analysis methods to identify metabolites associated with AD status. A combination of 10 metabolites could discriminate AD patients from controls with 79.2% accuracy (81.8% sensitivity, 76.9% specificity and an area under curve of 0.792) in a novel test set. Six of the metabolites were identified as long chain cholesteryl esters (ChEs) and were reduced in AD (ChE 32:0, odds ratio (OR)=0.237, 95% confidence interval (CI)=0.10–0.48, P=4.19E−04; ChE 34:0, OR=0.152, 95% CI=0.05–0.37, P=2.90E−04; ChE 34:6, OR=0.126, 95% CI=0.03–0.35, P=5.40E−04; ChE 32:4, OR=0.056, 95% CI=0.01–0.24, P=6.56E−04 and ChE 33:6, OR=0.205, 95% CI=0.06–0.50, P=2.21E−03, per (log2) metabolite unit). The levels of these metabolites followed the trend control>MCI>AD. We, additionally, found no association between cholesterol, the precursor of ChE and AD. This study identified new ChE molecules, involved in cholesterol metabolism, implicated in AD, which may help identify new therapeutic targets; although, these findings need to be replicated in larger well-phenotyped cohorts.

Quantification of the sensitivity increase of a micro-high-performance liquid chromatography-electrospray ionization mass spectrometry system with decreasing column diameter

This study details the sensitivity achieved with capillary columns when used with a micro-HPLC-electrospray ionization MS system. It is comprised of two sections, the first is the comparative study of three columns, one of narrow-bore diameter and two of capillary diameter. The second section compares three columns of decreasing diameter in the capillary scale. All the experiments achieved enhanced sensitivity using capillary columns. The increase in the experimental MS response ranged from -20% to +20% compared to the UV experimental response when decreasing the internal diameter of the columns used. When comparing the experimental MS response to the maximum theoretical UV response achievable, the increase in response ranged from 40 to 50%.

Development of a fast method for direct analysis of intact synthetic insulins in human plasma: the large peptide challenge

BACKGROUND Intact insulins are difficult to analyze by LC-MS/MS due to nonspecific binding and poor sensitivity, solubility and fragmentation. This work aims to provide a simpler, faster LC-MS method and focuses on solving the above issues. RESULTS A novel charged-surface chromatographic column produced peak widths for insulin that were significantly narrower than traditional C18 columns when using formic acid as mobile phase. Mass spectral fragments m/z >700 provided greater specificity, significantly reducing endogenous background. Detection limits in human plasma were 0.2 ng/ml for insulin glargine, glulisine and detemir, and 0.5 ng/ml for insulin aspart. Average accuracy for standard curve and QC samples was 93.4%. CONCLUSION A simple SPE LC-MS analysis was developed for direct, simultaneous quantification of insulin glargine, detemir, aspart and glulisine.

Metabolic fingerprinting of Schistosoma mansoni infection in mice urine with capillary electrophoresis.

Schistosoma mansoni infection in mice has been fingerprinted using CE to study the capabilities of this technique as a diagnostic tool for this parasitic disease. Two modes of separation were used in generating the electrophoretic data, with each untreated urine sample the following methods were applied: (i) a fused‐silica capillary, operating with an applied potential of 18 kV, in micellar EKC (MEKC) and (ii) a polyacrylamide‐coated capillary, operating with an applied potential of −20 kV under zonal CZE conditions. By combining normal and reverse polarities in the data treatment we have extracted more information from the samples, which is a better approach for CE metabolomics. The traditional problems associated with variability in electrophoretic peak migration times for analytes were countered by using a dynamic programming algorithm for the electropherograms alignment. Principal component analyses of these aligned electropherograms and partial least square discriminant analysis (PLS‐DA) data are shown to provide a valuable means of rapid and sample classification. This approach may become an important tool for the identification of biomarkers, diagnosis and disease surveillance.