NMR, Metabonomics and Molecular Profiles: Applications to the Quality Assessment of Foodstuffs

Nuclear Magnetic Resonance (NMR) is a branch of spectroscopy that is based on the fact that many atomic nuclei may be oriented by a strong magnetic field and will absorb radiofrequency radiation at characteristic frequencies. The parameters that can be measured on the resulting spectral lines (line positions, intensities, line widths, multiplicities and transients in time-dependent experi-ments) can be interpreted in terms of molecular structure, conformation, molecular motion and other rate processes. In this way, high resolution (HR) NMR allows performing qualitative and quantitative analysis of samples in solution, in order to determine the structure of molecules in solution and not only. In the past, high-field NMR spectroscopy has mainly concerned with the elucidation of chemical structure in solution, but today is emerging as a powerful exploratory tool for probing biochemical and physical processes. It represents a versatile tool for the analysis of foods. In literature many NMR studies have been reported on different type of food such as wine, olive oil, coffee, fruit juices, milk, meat, egg, starch granules, flour, etc using different NMR techniques. Traditionally, univariate analytical methods have been used to ex-plore spectroscopic data. This method is useful to measure or to se-lect a single descriptive variable from the whole spectrum and , at the end, only this variable is analyzed. This univariate methods ap-proach, applied to HR-NMR data, lead to different problems due especially to the complexity of an NMR spectrum. In fact, the lat-ter is composed of different signals belonging to different mole-cules, but it is also true that the same molecules can be represented by different signals, generally strongly correlated. The univariate methods, in this case, takes in account only one or a few variables, causing a loss of information. Thus, when dealing with complex samples like foodstuff, univariate analysis of spectra data results not enough powerful. Spectra need to be considered in their wholeness and, for analysing them, it must be taken in consideration the whole data matrix: chemometric methods are designed to treat such multivariate data. Multivariate data analysis is used for a number of distinct, differ-ent purposes and the aims can be divided into three main groups: • data description (explorative data structure modelling of any ge-neric n-dimensional data matrix, PCA for example); • regression and prediction (PLS); • classification and prediction of class belongings for new samples (LDA and PLS-DA and ECVA). The aim of this PhD thesis was to verify the possibility of identify-ing and classifying plants or foodstuffs, in different classes, based on the concerted variation in metabolite levels, detected by NMR spectra and using the multivariate data analysis as a tool to inter-pret NMR information. It is important to underline that the results obtained are useful to point out the metabolic consequences of a specific modification on foodstuffs, avoiding the use of a targeted analysis for the different metabolites. The data analysis is performed by applying chemomet-ric multivariate techniques to the NMR dataset of spectra acquired. The research work presented in this thesis is the result of a three years PhD study. This thesis reports the main results obtained from these two main activities: A1) Evaluation of a data pre-processing system in order to mini-mize unwanted sources of variations, due to different instrumental set up, manual spectra processing and to sample preparations arte-facts; A2) Application of multivariate chemiometric models in data analy-sis.

Advanced applications of X-ray Absorption Spectroscopy to the study of protein metal sites.

We present a study of the metal sites of different proteins through X-ray Absorption Fine Structure (XAFS) spectroscopy. First of all, the capabilities of XAFS analysis have been improved by ab initio simulation of the near-edge region of the spectra, and an original analysis method has been proposed. The method subsequently served ad a tool to treat diverse biophysical problems, like the inhibition of proton-translocating proteins by metal ions and the matrix effect exerted on photosynthetic proteins (the bacterial Reaction Center, RC) by strongly dehydrate sugar matrices. A time-resolved study of Fe site of RC with μs resolution has been as well attempted. Finally, a further step aimed to improve the reliability of XAFS analysis has been performed by calculating the dynamical parameters of the metal binding cluster by means of DFT methods, and the theoretical result obtained for MbCO has been successfully compared with experimental data.

Use of bioassays and biomarkers in Daphnia magna to assess the effect of pharmaceutical residuals in freshwater ecosystems

Widespread occurrence of pharmaceuticals residues has been reported in aquatic ecosystems. However, their toxic effects on aquatic biota remain unclear. Generally, the acute toxicity has been assessed in laboratory experiments, while chronic toxicity studies have rarely been performed. Of importance appears also the assessment of mixture effects, since pharmaceuticals never occur in waters alone. The aim of the present work is to evaluate acute and chronic toxic response in the crustacean Daphnia magna exposed to single pharmaceuticals and mixtures. We tested fluoxetine, a SSRI widely prescribed as antidepressant, and propranolol, a non selective β-adrenergic receptor-blocking agent used to treat hypertension. Acute immobilization and chronic reproduction tests were performed according to OECD guidelines 202 and 211, respectively. Single chemicals were first tested separately. Toxicity of binary mixtures was then assessed using a fixed ratio experimental design with concentrations based on Toxic Units. The conceptual model of Concentration Addition was adopted in this study, as we assumed that the mixture effect mirrors the sum of the single substances for compounds having similar mode of action. The MixTox statistical method was applied to analyze the experimental results. Results showed a significant deviation from CA model that indicated antagonism between chemicals in both the acute and the chronic mixture tests. The study was integrated assessing the effects of fluoxetine on a battery of biomarkers. We wanted to evaluate the organism biological vulnerability caused by low concentrations of pharmaceutical occurring in the aquatic environment. We assessed the acetylcholinesterase and glutathione s-transferase enzymatic activities and the malondialdehyde production. No treatment induced significant alteration of biomarkers with respect to the control. Biological assays and the MixTox model application proved to be useful tools for pharmaceutical risk assessment. Although promising, the application of biomarkers in Daphnia magna needs further elucidation.