Quantitative authenticity testing of buffalo mozzarella via alpha(s1)-Casein using multiple reaction monitoring mass spectrometry

We address the detection and quantitation of bovine milk in 'buffalo' mozzarella cheese using multiple reaction monitoring (MRM) mass spectrometry (MS). Focussing on the abundant protein alpha(s1)-casein, present in both species but with 10 amino acid sequence differences, we extract a list of marker peptides specific to each species. 'Identical' peptides, exactly the same in both species, are used for relative quantitation of alpha(s1)-casein in each milk type, whereas 'similar' peptides, present in both species but differing typically by one amino acid, are used to demonstrate relative quantitation in binary cheese mixtures. In addition, we report a pilot survey of UK supermarket and restaurant products labelled as 'buffalo mozzarella', finding that 2/3 of restaurant meals and supermarket pizzas are either mislabelled or adulterated

Species Determination and Quantitation in Mixtures Using MRM Mass Spectrometry of Peptides Applied to Meat Authentication

We describe a simple protocol for identifying and quantifying the two components in binary mixtures of species possessing one or more similar proteins. Central to the method is the identification of 'corresponding proteins' in the species of interest, in other words proteins that are nominally the same but possess species-specific sequence differences. When subject to proteolysis, corresponding proteins will give rise to some peptides which are likewise similar but with species-specific variants. These are 'corresponding peptides'. Species-specific peptides can be used as markers for species determination, while pairs of corresponding peptides permit relative quantitation of two species in a mixture. The peptides are detected using multiple reaction monitoring (MRM) mass spectrometry, a highly specific technique that enables peptide-based species determination even in complex systems. In addition, the ratio of MRM peak areas deriving from corresponding peptides supports relative quantitation. Since corresponding proteins and peptides will, in the main, behave similarly in both processing and in experimental extraction and sample preparation, the relative quantitation should remain comparatively robust. In addition, this approach does not need the standards and calibrations required by absolute quantitation methods. The protocol is described in the context of red meats, which have convenient corresponding proteins in the form of their respective myoglobins. This application is relevant to food fraud detection: the method can detect 1% weight for weight of horse meat in beef. The corresponding protein, corresponding peptide (CPCP) relative quantitation using MRM peak area ratios gives good estimates of the weight for weight composition of a horse plus beef mixture

Search algorithms as a framework for the optimization of drug combinations

Combination therapies are often needed for effective clinical outcomes in the management of complex diseases, but presently they are generally based on empirical clinical experience. Here we suggest a novel application of search algorithms, originally developed for digital communication, modified to optimize combinations of therapeutic interventions. In biological experiments measuring the restoration of the decline with age in heart function and exercise capacity in Drosophila melanogaster, we found that search algorithms correctly identified optimal combinations of four drugs with only one third of the tests performed in a fully factorial search. In experiments identifying combinations of three doses of up to six drugs for selective killing of human cancer cells, search algorithms resulted in a highly significant enrichment of selective combinations compared with random searches. In simulations using a network model of cell death, we found that the search algorithms identified the optimal combinations of 6-9 interventions in 80-90% of tests, compared with 15-30% for an equivalent random search. These findings suggest that modified search algorithms from information theory have the potential to enhance the discovery of novel therapeutic drug combinations. This report also helps to frame a biomedical problem that will benefit from an interdisciplinary effort and suggests a general strategy for its solution.Comment: 36 pages, 10 figures, revised versio

A specific case in the classification of woods by FTIR and chemometric: discrimination of Fagales from Malpighiales

Fourier transform infrared (FTIR) spectroscopic data was used to classify wood samples from nine species within the Fagales and Malpighiales using a range of multivariate statistical methods. Taxonomic classification of the family Fagaceae and Betulaceae from Angiosperm Phylogenetic System Classification (APG II System) was successfully performed using supervised pattern recognition techniques. A methodology for wood sample discrimination was developed using both sapwood and heartwood samples. Ten and eight biomarkers emerged from the dataset to discriminate order and family, respectively. In the species studied FTIR in combination with multivariate analysis highlighted significant chemical differences in hemicelluloses, cellulose and guaiacyl (lignin) and shows promise as a suitable approach for wood sample classification

Ulcerative colitis and irritable bowel patients exhibit distinct abnormalities of the gut microbiota

<p>Abstract</p> <p>Background</p> <p>Previous studies suggest a link between gut microbiota and the development of ulcerative colitis (UC) and irritable bowel syndrome (IBS). Our aim was to investigate any quantitative differences in faecal bacterial compositions in UC and IBS patients compared to healthy controls, and to identify individual bacterial species that contribute to these differences.</p> <p>Methods</p> <p>Faecal microbiota of 13 UC patients, 11 IBS patients and 22 healthy volunteers were analysed by PCR-Denaturing Gradient Gel Electrophoresis (DGGE) using universal and Bacteroides specific primers. The data obtained were normalized using in-house developed statistical method and interrogated by multivariate approaches. The differentiated bands were excised and identified by sequencing the V3 region of the 16S rRNA genes.</p> <p>Results</p> <p>Band profiles revealed that number of predominant faecal bacteria were significantly different between UC, IBS and control group (p < 10^-4). By assessing the mean band numbers in UC (37 ± 5) and IBS (39 ± 6), compared to the controls (45 ± 3), a significant decrease in bacterial species is suggested (p = 0.01). There were no significant differences between IBS and UC. Biodiversity of the bacterial species was significantly lower in UC (μ = 2.94, σ = 0.29) and IBS patients (μ = 2.90, σ = 0.38) than controls (μ = 3.25, σ = 0.16; p = 0.01). Moreover, similarity indices revealed greater biological variability of predominant bacteria in UC and IBS compared to the controls (median Dice coefficients 76.1% (IQR 70.9 - 83.1), 73.8% (IQR 67.0 - 77.5) and 82.9% (IQR 79.1 - 86.7) respectively). DNA sequencing of discriminating bands suggest that the presence of <it>Bacteroides vulgatus, B. ovatus, B. uniformis</it>, and <it>Parabacteroides sp</it>. in healthy volunteers distinguishes them from IBS and UC patients. DGGE profiles of Bacteroides species revealed a decrease of Bacteroides community in UC relative to IBS and controls.</p> <p>Conclusion</p> <p>Molecular profiling of faecal bacteria revealed abnormalities of intestinal microbiota in UC and IBS patients, while different patterns of Bacteroides species loss in particular, were associated with UC and IBS.</p

Goal Commitment And Competition As Drivers For Group Productivity In Business Process Modeling

Many studies have looked at the factors that control the productivity of collaborative work. We claim that goal commitment and competition have a strong impact on group productivity in collaborative modelling. To substantiate this claim we first take a look at existing factor models to identify the factors that potentially mediate the effect on group productivity. We then investigate the relation between the factors with the help of controlled field experiments in five different organisations. We confirm the theoretical results with the help of structured equation modelling