Production of bioactive peptides through sequencial action of Yarrowia lipolytica proteases and chemical glycation

This PhD thesis is aimed at studying the suitability of proteases realised by Yarrowia lipolytica to hydrolyse proteins of different origins available as industrial food by-products. Several strains of Y. lipolytica have been screened for the production of extracellular proteases by zymography. On the basis of the results some strains released only a protease having a MW of 37 kDa, which corresponds to the already reported acidic protease, while other produced prevalently or only a protease with a MW higher than 200 kDa. The proteases have been screened for their "cold attitude" on gelatin, gluten and skim milk. This property can be relevant from a biotechnological point of view in order to save energy consumption during industrial processes. Most of the strains used were endowed with proteolytic activity at 6 °C on all the three proteins. The proteolytic breakdown profiles of the proteins, detected at 27 °C, were different related to the specific strains of Y. lipolytica. The time course of the hydrolysis, tested on gelatin, affected the final bioactivities of the peptide mixtures produced. In particular, an increase in both the antioxidant and antimicrobial activities was detected when the protease of the strain Y. lipolytica 1IIYL4A was used. The final part of this work was focused on the improvement of the peptides bioactivities through a novel process based on the production of glycopeptides. Firstly, the main reaction parameters were optimized in a model system, secondly a more complex system, based on gluten hydrolysates, was taken into consideration to produce glycopeptides. The presence of the sugar moiety reduced the hydrophobicity of the glycopeptides, thus affecting the final antimicrobial activity which was significantly improved. The use of this procedure could be highly effective to modify peptides and can be employed to create innovative functional peptides using a mild temperature process.

Physico-chemical and microstructural properties of food dispersions

The macroscopic properties of oily food dispersions, such as rheology, mechanical strength, sensory attributes (e.g. mouth feel, texture and even flavour release) and as well as engineering properties are strongly determined by their microstructure, that is considered a key parameter in the understanding of the foods behaviour . In particular the rheological properties of these matrices are largely influenced by their processing techniques, particle size distribution and composition of ingredients. During chocolate manufacturing, mixtures of sugar, cocoa and fat are heated, cooled, pressurized and refined. These steps not only affect particle size reduction, but also break agglomerates and distribute lipid and lecithin-coated particles through the continuous phase, this considerably modify the microstructure of final chocolate. The interactions between the suspended particles and the continuous phase provide information about the existing network and consequently can be associated to the properties and characteristics of the final dispersions. Moreover since the macroscopic properties of food materials, are strongly determined by their microstructure, the evaluation and study of the microstructural characteristics, can be very important for a through understanding of the food matrices characteristics and to get detailed information on their complexity. The aim of this study was investigate the influence of formulation and each process step on the microstructural properties of: chocolate type model systems, dark milk and white chocolate types, and cocoa creams. At the same time the relationships between microstructural changes and the resulting physico-chemical properties of: chocolate type dispersions model systems dark milk and white chocolate were investigated.

Differential expression analysis for sequence count data via mixtures of negative binomials

The recent advent of Next-generation sequencing technologies has revolutionized the way of analyzing the genome. This innovation allows to get deeper information at a lower cost and in less time, and provides data that are discrete measurements. One of the most important applications with these data is the differential analysis, that is investigating if one gene exhibit a different expression level in correspondence of two (or more) biological conditions (such as disease states, treatments received and so on). As for the statistical analysis, the final aim will be statistical testing and for modeling these data the Negative Binomial distribution is considered the most adequate one especially because it allows for "over dispersion". However, the estimation of the dispersion parameter is a very delicate issue because few information are usually available for estimating it. Many strategies have been proposed, but they often result in procedures based on plug-in estimates, and in this thesis we show that this discrepancy between the estimation and the testing framework can lead to uncontrolled first-type errors. We propose a mixture model that allows each gene to share information with other genes that exhibit similar variability. Afterwards, three consistent statistical tests are developed for differential expression analysis. We show that the proposed method improves the sensitivity of detecting differentially expressed genes with respect to the common procedures, since it is the best one in reaching the nominal value for the first-type error, while keeping elevate power. The method is finally illustrated on prostate cancer RNA-seq data.