E. Kate Kemsley

Metabolomics of Fecal Extracts Detects Altered Metabolic Activity of Gut Microbiota in Ulcerative Colitis and Irritable Bowel Syndrome

(1)H NMR spectroscopy of aqueous fecal extracts has been used to investigate differences in metabolic activity of gut microbiota in patients with ulcerative colitis (UC) (n = 13), irritable bowel syndrome (IBS) (n = 10), and healthy controls (C) (n = 22). Up to four samples per individual were collected over 2 years giving a total of 124 samples. Multivariate discriminant analysis, based on NMR data from all three groups, was able to predict UC and C group membership with good sensitivity and specificity; classification of IBS samples was less successful and could not be used for diagnosis. Trends were detected toward increased taurine and cadaverine levels in UC with increased bile acid and decreased branched chain fatty acids in IBS relative to controls; changes in short chain fatty acids and amino acids were not significant. Previous PCR-denaturing gradient gel electrophoresis (PCR-DGGE) analysis of the same fecal material had shown alterations of the gut microbiota when comparing UC and IBS groups with controls. Hierarchical cluster analysis showed that DGGE profiles from the same individual were stable over time, but NMR spectra were more variable; canonical correlation analysis of NMR and DGGE data partly separated the three groups and revealed a correlation between the gut microbiota profile and metabolite composition.

Meat Authentication via Multiple Reaction Monitoring Mass Spectrometry of Myoglobin Peptides

A rapid multiple reaction monitoring (MRM) mass spectrometric method for the detection and relative quantitation of the adulteration of meat with that of an undeclared species is presented. Our approach uses corresponding proteins from the different species under investigation and corresponding peptides from those proteins, or CPCP. Selected peptide markers can be used for species detection. The use of ratios of MRM transition peak areas for corresponding peptides is proposed for relative quantitation. The approach is introduced by use of myoglobin from four meats: beef, pork, horse and lamb. Focusing in the present work on species identification, by use of predictive tools, we determine peptide markers that allow the identification of all four meats and detection of one meat added to another at levels of 1% (w/w). Candidate corresponding peptide pairs to be used for the relative quantification of one meat added to another have been observed. Preliminary quantitation data presented here are encouraging.

Prediction of Variability in CYP3A4 Induction Using a Combined 1H NMR Metabonomics and Targeted UPLC–MS Approach

The activity of Cytochrome P450 3A4 (CYP3A4) enzyme is associated with many adverse or poor therapeutic responses to drugs. We used (1)H NMR-based metabonomics to identify a metabolic signature associated with variation in induced CYP3A4 activity. A total of 301 female twins, aged 45--84, participated in this study. Each volunteer was administered a potent inducer of CYP3A4 (St. Johns Wort) for 14 days and the activity of CYP3A4 was quantified through the metabolism of the exogenously administered probe drug quinine sulfate (300 mg). Pre- and postintervention fasting urine samples were used to obtain metabolite profiles, using (1)H NMR spectroscopy, and were analyzed using UPLC--MS to obtain a marker for CYP3A4 induction, via the ratio of 3-hydroxyquinine to quinine (3OH-Q:Q). Multiple linear regression was used to build a predictive model for 3OH-Q:Q values based on the preintervention metabolite profiles. A combination of seven metabolites and seven covariates showed a strong (r = 0.62) relationship with log(3OH-Q:Q). This regression model demonstrated significant (p < 0.00001) predictive ability when applied to an independent validation set. Our results highlight the promise of metabonomics for predicting CYP3A4-mediated drug response.

Low-field 1H NMR spectroscopy for distinguishing between arabica and robusta ground roast coffees

Graphical abstract

Evaluation of multiple variate selection methods from a biological perspective: a nutrigenomics case study.

Genomics-based technologies produce large amounts of data. To interpret the results and identify the most important variates related to phenotypes of interest, various multivariate regression and variate selection methods are used. Although inspected for statistical performance, the relevance of multivariate models in interpreting biological data sets often remains elusive. We compare various multivariate regression and variate selection methods applied to a nutrigenomics data set in terms of performance, utility and biological interpretability. The studied data set comprised hepatic transcriptome (10,072 predictor variates) and plasma protein concentrations [2 dependent variates: Leptin (LEP) and Tissue inhibitor of metalloproteinase 1 (TIMP-1)] collected during a high-fat diet study in ApoE3Leiden mice. The multivariate regression methods used were: partial least squares “PLS”; a genetic algorithm-based multiple linear regression, “GA-MLR”; two least-angle shrinkage methods, “LASSO” and “ELASTIC NET”; and a variant of PLS that uses covariance-based variate selection, “CovProc.” Two methods of ranking the genes for Gene Set Enrichment Analysis (GSEA) were also investigated: either by their correlation with the protein data or by the stability of the PLS regression coefficients. The regression methods performed similarly, with CovProc and GA performing the best and worst, respectively (R-squared values based on “double cross-validation” predictions of 0.762 and 0.451 for LEP; and 0.701 and 0.482 for TIMP-1). CovProc, LASSO and ELASTIC NET all produced parsimonious regression models and consistently identified small subsets of variates, with high commonality between the methods. Comparison of the gene ranking approaches found a high degree of agreement, with PLS-based ranking finding fewer significant gene sets. We recommend the use of CovProc for variate selection, in tandem with univariate methods, and the use of correlation-based ranking for GSEA-like pathway analysis methods.

Acute Consumption of Flavan-3-ol-Enriched Dark Chocolate Affects Human Endogenous Metabolism

Flavan-3-ols and methylxanthines have potential beneficial effects on human health including reducing cardiovascular risk. We performed a randomized controlled crossover intervention trial to assess the acute effects of consumption of flavan-3-ol-enriched dark chocolate, compared with standard dark chocolate and white chocolate, on the human metabolome. We assessed the metabolome in urine and blood plasma samples collected before and at 2 and 6 h after consumption of chocolates in 42 healthy volunteers using a nontargeted metabolomics approach. Plasma samples were assessed and showed differentiation between time points with no further separation among the three chocolate treatments. Multivariate statistics applied to urine samples could readily separate the postprandial time points and distinguish between the treatments. Most of the markers responsible for the multivariate discrimination between the chocolates were of dietary origin. Interestingly, small but significant level changes were also observed for a subset of endogenous metabolites. 1H NMR revealed that flavan-3-ol-enriched dark chocolate and standard dark chocolate reduced urinary levels of creatinine, lactate, some amino acids, and related degradation products and increased the levels of pyruvate and 4-hydroxyphenylacetate, a phenolic compound of bacterial origin. This study demonstrates that an acute chocolate intervention can significantly affect human metabolism.

16-O-methylcafestol is present in ground roast Arabica coffees: Implications for authenticity testing

Highlights • Lipophilic extracts of ground roast Arabica coffees were authenticated by benchtop NMR.• Small amounts of esterified 16-O-methylcafestol were found in Arabica coffees.• The compound identity was confirmed by NMR and MS experiments.• 16-OMC remains a useful marker for non-Arabicas as these contain much higher amounts.• 6 out of 60 retail Arabicas contained significant amounts of non-Arabica species.

Detecting Forged Alcohol Non-invasively Through Vibrational Spectroscopy and Machine Learning

Alcoholic spirits are a common target for counterfeiting and adulteration, with potential costs to public health, the taxpayer and brand integrity. Current methods to authenticate spirits include examinations of superficial appearance and consistency, or require the tester to open the bottle and remove a sample. The former is inexact, while the latter is not suitable for widespread screening or for high-value spirits, which lose value once opened. We study whether non-invasive near infrared spectroscopy, in combination with traditional and time series classification methods, can correctly classify the alcohol content (a key factor in determining authenticity) of synthesised spirits sealed in real bottles. Such an experimental setup could allow for a portable, cheap to operate, and fast authentication device. We find that ethanol content can be classified with high accuracy, however methanol content proved difficult with the algorithms evaluated.