NMR Based Foodomics to Investigate the Digestibility of Protein-Rich Food Products

Nowadays, in developed countries, the excessive food intake, in conjunction with a decreased physical activity, has led to an increase in lifestyle-related diseases, such as obesity, cardiovascular diseases, type -2 diabetes, a range of cancer types and arthritis. The socio-economic importance of such lifestyle-related diseases has encouraged countries to increase their efforts in research, and many projects have been initiated recently in research that focuses on the relationship between food and health. Thanks to these efforts and to the growing availability of technologies, the food companies are beginning to develop healthier food. The necessity of rapid and affordable methods, helping the food industries in the ingredient selection has stimulated the development of in vitro systems that simulate the physiological functions to which the food components are submitted when administrated in vivo. One of the most promising tool now available appears the in vitro digestion, which aims at predicting, in a comparative way among analogue food products, the bioaccessibility of the nutrients of interest.. The adoption of the foodomics approach has been chosen in this work to evaluate the modifications occurring during the in vitro digestion of selected protein-rich food products. The measure of the proteins breakdown was performed via NMR spectroscopy, the only techniques capable of observing, directly in the simulated gastric and duodenal fluids, the soluble oligo- and polypeptides released during the in vitro digestion process. The overall approach pioneered along this PhD work, has been discussed and promoted in a large scientific community, with specialists networked under the INFOGEST COST Action, which recently released a harmonized protocol for the in vitro digestion. NMR spectroscopy, when used in tandem with the in vitro digestion, generates a new concept, which provides an additional attribute to describe the food quality: the comparative digestibility, which measures the improvement of the nutrients bioaccessibility.

Probiotics for the prevention/treatment of human diseases and ecological study of the intestinal microbiota

The interest in human intestinal microbiota has increased in the last 20 years and significant advances have been achieved with regard to its composition and functions. The gut microbiota contributes to the maintenance of the host health status and, since alterations in the gut microbiota have been involved in the pathogenesis/progression of some diseases, several studies have focused on the manipulation of its composition. Probiotics are a strategy to maintain/restore the correct balance of gut microbial population and to prevent/treat diseases. The aim of this thesis was to explore the possibility of probiotic supplementation for the prevention/treatment of human diseases and the related study of the intestinal microbial environment. After reviewing studies concerning the use of Bifidobacterium breve as probiotic in paediatric diseases, the effectiveness of a probiotic formulation consisting of two strains of B. breve was assessed in paediatric subjects for the prevention or alleviation of gastrointestinal disorders, including coeliac disease and paediatric obesity. As the emerging role of gut microbiota in neurological diseases, the intestinal microbial environment in amyotrophic lateral sclerosis patients compared to healthy controls and the effects of a probiotic administration were examined. Considering the role of viruses in shaping gut microbiota, gut bacteriophages and bacterial community of preterm infants were investigated. The results evidenced differences in gut microbial composition of healthy controls and diseased subjects in coeliac and amyotrophic lateral sclerosis patients. The probiotic approach was effective in restoring the microbial composition in the former, whereas, in the latter, the influence was focused only on some microbial groups. The probiotic intervention was effective in improving the glyco-insulinemic profile in obese children and in preventing gastrointestinal disorders in healthy newborns. The study of the bacterial and phage composition in preterm infants suggested a transkingdom interplay between bacteria and viruses with a reciprocal influence on their composition.

Mathematical methods to analyze and interpret calcium signals of astrocytes

Astrocytes are the most numerous glial cell type in the mammalian brain and permeate the entire CNS interacting with neurons, vasculature, and other glial cells. Astrocytes display intracellular calcium signals that encode information about local synaptic function, distributed network activity, and high-level cognitive functions. Several studies have investigated the calcium dynamics of astrocytes in sensory areas and have shown that these cells can encode sensory stimuli. Nevertheless, only recently the neuro-scientific community has focused its attention on the role and functions of astrocytes in associative areas such as the hippocampus. In our first study, we used the information theory formalism to show that astrocytes in the CA1 area of the hippocampus recorded with 2-photon fluorescence microscopy during spatial navigation encode spatial information that is complementary and synergistic to information encoded by nearby "place cell" neurons. In our second study, we investigated various computational aspects of applying the information theory formalism to astrocytic calcium data. For this reason, we generated realistic simulations of calcium signals in astrocytes to determine optimal hyperparameters and procedures of information measures and applied them to real astrocytic calcium imaging data. Calcium signals of astrocytes are characterized by complex spatiotemporal dynamics occurring in subcellular parcels of the astrocytic domain which makes studying these cells in 2-photon calcium imaging recordings difficult. However, current analytical tools which identify the astrocytic subcellular regions are time consuming and extensively rely on user-defined parameters. Here, we present Rapid Astrocytic calcium Spatio-Temporal Analysis (RASTA), a novel machine learning algorithm for spatiotemporal semantic segmentation of 2-photon calcium imaging recordings of astrocytes which operates without human intervention. We found that RASTA provided fast and accurate identification of astrocytic cell somata, processes, and cellular domains, extracting calcium signals from identified regions of interest across individual cells and populations of hundreds of astrocytes recorded in awake mice.