Molecular interactions: metal ions and protein chaperones in the urease system from Helicobacter pylori

Although nickel is a toxic metal for living organisms in its soluble form, its importance in many biological processes recently emerged. In this view, the investigation of the nickel-dependent enzymes urease and [NiFe]-hydrogenase, especially the mechanism of nickel insertion into their active sites, represent two intriguing case studies to understand other analogous systems and therefore to lead to a comprehension of the nickel trafficking inside the cell. Moreover, these two enzymes have been demonstrated to ensure survival and colonization of the human pathogen H. pylori, the only known microorganism able to proliferate in the gastric niche. The right nickel delivering into the urease active site requires the presence of at least four accessory proteins, UreD, UreE, UreF and UreG. Similarly, analogous process is principally mediated by HypA and HypB proteins in the [NiFe]-hydrogenase system. Indeed, HpHypA and HpHypB also have been proposed to act in the activation of the urease enzyme from H. pylori, probably mobilizing nickel ions from HpHypA to the HpUreE-HpUreG complex. A complete comprehension of the interaction mechanism between the accessory proteins and the crosstalk between urease and hydrogenase accessory systems requires the determination of the role of each protein chaperone that strictly depends on their structural and biochemical properties. The availability of HpUreE, HpUreG and HpHypA proteins in a pure form is a pre-requisite to perform all the subsequent protein characterizations, thus their purification was the first aim of this work. Subsequently, the structural and biochemical properties of HpUreE were investigated using multi-angle and quasi-elastic light scattering, as well as NMR and circular dichroism spectroscopy. The thermodynamic parameters of Ni2+ and Zn2+ binding to HpUreE were principally established using isothermal titration calorimetry and the importance of key histidine residues in the process of binding metal ions was studied using site-directed mutagenesis. The molecular details of the HpUreE-HpUreG and HpUreE-HpHypA protein-protein assemblies were also elucidated. The interaction between HpUreE and HpUreG was investigated using ITC and NMR spectroscopy, and the influence of Ni2+ and Zn2+ metal ions on the stabilization of this association was established using native gel electrophoresis, light scattering and thermal denaturation scanning followed by CD spectroscopy. Preliminary HpUreE-HpHypA interaction studies were conducted using ITC. Finally, the possible structural architectures of the two protein-protein assemblies were rationalized using homology modeling and docking computational approaches. All the obtained data were interpreted in order to achieve a more exhaustive picture of the urease activation process, and the correlation with the accessory system of the hydrogenase enzyme, considering the specific role and activity of the involved protein players. A possible function for Zn2+ in the chaperone network involved in Ni2+ trafficking and urease activation is also envisaged.

Effect of estrogen suppression on lung function in pulmonary Lymphangioleiomyomatosis

Background: Lymphangioleiomyomatosis (LAM), a rare progressive disease, is characterized by the proliferation of abnormal smooth muscle cells (LAM cells) in the lung, which leads to cystic parenchymal destruction and progressive respiratory failure. Estrogen receptors are present in LAM cells. LAM affects almost exclusively women of childbearing age. These findings, along with reports of disease progression during pregnancy or treatment with exogenous estrogens, have led to the assumption that hormonal factors play an important role in the pathogenesis of LAM. So, various therapies aim at preventing estrogen receptors (ER) by lowering circulating estrogen levels, by trying to block ER activity, or by attempting to lower ER expression in LAM. Prior experience have yielded conflicting results. Objective: The goal of this study was to evaluate, retrospectively, the effect of estrogen suppression in 21 patients with LAM. Design: We evaluated hormonal assays, pulmonary function tests and gas-exchange at baseline and after 12, 24 and 36 months after initiating hormonal manipulation. Results: The mean yearly rates of decline in FEV1 and DLCO are lower than those observed in prior studies and just DLCO decline was statistically significant. We also found an improvement of mean value of FVC and PaO2. Conclusions: Estrogen suppression appears to prevent decline in lung function in LAM.

Methods for Clearance Influence Analysis in Planar and Spatial Mechanisms

This doctoral dissertation presents a new method to asses the influence of clearancein the kinematic pairs on the configuration of planar and spatial mechanisms. The subject has been widely investigated in both past and present scientific literature, and is approached in different ways: a static/kinetostatic way, which looks for the clearance take-up due to the external loads on the mechanism; a probabilistic way, which expresses clearance-due displacements using probability density functions; a dynamic way, which evaluates dynamic effects like the actual forces in the pairs caused by impacts, or the consequent vibrations. This dissertation presents a new method to approach the problem of clearance. The problem is studied from a purely kinematic perspective. With reference to a given mechanism configuration, the pose (position and orientation) error of the mechanism link of interest is expressed as a vector function of the degrees of freedom introduced in each pair by clearance: the presence of clearance in a kinematic pair, in facts, causes the actual pair to have more degrees of freedom than the theoretical clearance-free one. The clearance-due degrees of freedom are bounded by the pair geometry. A proper modelling of clearance-affected pairs allows expressing such bounding through analytical functions. It is then possible to study the problem as a maximization problem, where a continuous function (the pose error of the link of interest) subject to some constraints (the analytical functions bounding clearance- due degrees of freedom) has to be maximize. Revolute, prismatic, cylindrical, and spherical clearance-affected pairs have been analytically modelled; with reference to mechanisms involving such pairs, the solution to the maximization problem has been obtained in a closed form.

Learning with Kernels on Graphs: DAG-based kernels, data streams and RNA function prediction.

In many application domains data can be naturally represented as graphs. When the application of analytical solutions for a given problem is unfeasible, machine learning techniques could be a viable way to solve the problem. Classical machine learning techniques are defined for data represented in a vectorial form. Recently some of them have been extended to deal directly with structured data. Among those techniques, kernel methods have shown promising results both from the computational complexity and the predictive performance point of view. Kernel methods allow to avoid an explicit mapping in a vectorial form relying on kernel functions, which informally are functions calculating a similarity measure between two entities. However, the definition of good kernels for graphs is a challenging problem because of the difficulty to find a good tradeoff between computational complexity and expressiveness. Another problem we face is learning on data streams, where a potentially unbounded sequence of data is generated by some sources. There are three main contributions in this thesis. The first contribution is the definition of a new family of kernels for graphs based on Directed Acyclic Graphs (DAGs). We analyzed two kernels from this family, achieving state-of-the-art results from both the computational and the classification point of view on real-world datasets. The second contribution consists in making the application of learning algorithms for streams of graphs feasible. Moreover,we defined a principled way for the memory management. The third contribution is the application of machine learning techniques for structured data to non-coding RNA function prediction. In this setting, the secondary structure is thought to carry relevant information. However, existing methods considering the secondary structure have prohibitively high computational complexity. We propose to apply kernel methods on this domain, obtaining state-of-the-art results.

Rigorous results in Spin Glasses and Monomer-Dimer systems

In this work I reported recent results in the field of Statistical Mechanics of Equilibrium, and in particular in Spin Glass models and Monomer Dimer models . We start giving the mathematical background and the general formalism for Spin (Disordered) Models with some of their applications to physical and mathematical problems. Next we move on general aspects of the theory of spin glasses, in particular to the Sherrington-Kirkpatrick model which is of fundamental interest for the work. In Chapter 3, we introduce the Multi-species Sherrington-Kirkpatrick model (MSK), we prove the existence of the thermodynamical limit and the Guerra's Bound for the quenched pressure together with a detailed analysis of the annealed and the replica symmetric regime. The result is a multidimensional generalization of the Parisi's theory. Finally we brie y illustrate the strategy of the Panchenko's proof of the lower bound. In Chapter 4 we discuss the Aizenmann-Contucci and the Ghirlanda-Guerra identities for a wide class of Spin Glass models. As an example of application, we discuss the role of these identities in the proof of the lower bound. In Chapter 5 we introduce the basic mathematical formalism of Monomer Dimer models. We introduce a Gaussian representation of the partition function that will be fundamental in the rest of the work. In Chapter 6, we introduce an interacting Monomer-Dimer model. Its exact solution is derived and a detailed study of its analytical properties and related physical quantities is performed. In Chapter 7, we introduce a quenched randomness in the Monomer Dimer model and show that, under suitable conditions the pressure is a self averaging quantity. The main result is that, if we consider randomness only in the monomer activity, the model is exactly solvable.