Evidenze molecolari del ruolo antiproliferativo e pro-apoptotico della clusterina in cellule epiteliali prostatiche umane

The Clusterin (CLU) gene produces different forms of protein products which vary in their biological properties and distribution within the cell. Both the extra- and intracellular CLU forms regulate cell proliferation and apoptosis. Dis-regulation of CLU expression occurs in many cancer types, including prostate cancer. The role that CLU plays in tumorigenesis is still unclear. We found that CLU over-expression inhibited cell proliferation and induced apoptosis in prostate cancer cells. Here we show that depletion of CLU affects the growth of PC-3 prostate cancer cells. Following siRNA, all protein products quickly disappeared, inducing cell cycle progression and higher expression of specific proliferation markers (i.e. H3 mRNA, PCNA and cyclins A, B1 and D) as detected by RT-qPCR and Western blot. Quite surprisingly, we also found that the turnover of CLU protein is very rapid and tightly regulated by ubiquitin–proteasome mediated degradation. Inhibition of protein synthesis by cycloheximide showed that CLU half-life is less than 2 hours. All CLU protein products were found poly-ubiquitinated by co-immuniprecipitation. Proteasome inhibition by MG132 caused stabilization and accumulation of all CLU protein products, strongly inducing the nuclear form of CLU (nCLU) and committing cells to caspase-dependent death. In conclusion, proteasome inhibition may induce prostate cancer cell death through accumulation of nCLU, a potential tumour suppressor factor.

Design and implementation of bioinformatics tools for large scale genome annotation

The continuous increase of genome sequencing projects produced a huge amount of data in the last 10 years: currently more than 600 prokaryotic and 80 eukaryotic genomes are fully sequenced and publically available. However the sole sequencing process of a genome is able to determine just raw nucleotide sequences. This is only the first step of the genome annotation process that will deal with the issue of assigning biological information to each sequence. The annotation process is done at each different level of the biological information processing mechanism, from DNA to protein, and cannot be accomplished only by in vitro analysis procedures resulting extremely expensive and time consuming when applied at a this large scale level. Thus, in silico methods need to be used to accomplish the task. The aim of this work was the implementation of predictive computational methods to allow a fast, reliable, and automated annotation of genomes and proteins starting from aminoacidic sequences. The first part of the work was focused on the implementation of a new machine learning based method for the prediction of the subcellular localization of soluble eukaryotic proteins. The method is called BaCelLo, and was developed in 2006. The main peculiarity of the method is to be independent from biases present in the training dataset, which causes the over‐prediction of the most represented examples in all the other available predictors developed so far. This important result was achieved by a modification, made by myself, to the standard Support Vector Machine (SVM) algorithm with the creation of the so called Balanced SVM. BaCelLo is able to predict the most important subcellular localizations in eukaryotic cells and three, kingdom‐specific, predictors were implemented. In two extensive comparisons, carried out in 2006 and 2008, BaCelLo reported to outperform all the currently available state‐of‐the‐art methods for this prediction task. BaCelLo was subsequently used to completely annotate 5 eukaryotic genomes, by integrating it in a pipeline of predictors developed at the Bologna Biocomputing group by Dr. Pier Luigi Martelli and Dr. Piero Fariselli. An online database, called eSLDB, was developed by integrating, for each aminoacidic sequence extracted from the genome, the predicted subcellular localization merged with experimental and similarity‐based annotations. In the second part of the work a new, machine learning based, method was implemented for the prediction of GPI‐anchored proteins. Basically the method is able to efficiently predict from the raw aminoacidic sequence both the presence of the GPI‐anchor (by means of an SVM), and the position in the sequence of the post‐translational modification event, the so called ω‐site (by means of an Hidden Markov Model (HMM)). The method is called GPIPE and reported to greatly enhance the prediction performances of GPI‐anchored proteins over all the previously developed methods. GPIPE was able to predict up to 88% of the experimentally annotated GPI‐anchored proteins by maintaining a rate of false positive prediction as low as 0.1%. GPIPE was used to completely annotate 81 eukaryotic genomes, and more than 15000 putative GPI‐anchored proteins were predicted, 561 of which are found in H. sapiens. In average 1% of a proteome is predicted as GPI‐anchored. A statistical analysis was performed onto the composition of the regions surrounding the ω‐site that allowed the definition of specific aminoacidic abundances in the different considered regions. Furthermore the hypothesis that compositional biases are present among the four major eukaryotic kingdoms, proposed in literature, was tested and rejected. All the developed predictors and databases are freely available at: BaCelLo http://gpcr.biocomp.unibo.it/bacello eSLDB http://gpcr.biocomp.unibo.it/esldb GPIPE http://gpcr.biocomp.unibo.it/gpipe

A clustering method for robust and reliable large scale functional and structural protein sequence annotation

Bioinformatics, in the last few decades, has played a fundamental role to give sense to the huge amount of data produced. Obtained the complete sequence of a genome, the major problem of knowing as much as possible of its coding regions, is crucial. Protein sequence annotation is challenging and, due to the size of the problem, only computational approaches can provide a feasible solution. As it has been recently pointed out by the Critical Assessment of Function Annotations (CAFA), most accurate methods are those based on the transfer-by-homology approach and the most incisive contribution is given by cross-genome comparisons. In the present thesis it is described a non-hierarchical sequence clustering method for protein automatic large-scale annotation, called “The Bologna Annotation Resource Plus” (BAR+). The method is based on an all-against-all alignment of more than 13 millions protein sequences characterized by a very stringent metric. BAR+ can safely transfer functional features (Gene Ontology and Pfam terms) inside clusters by means of a statistical validation, even in the case of multi-domain proteins. Within BAR+ clusters it is also possible to transfer the three dimensional structure (when a template is available). This is possible by the way of cluster-specific HMM profiles that can be used to calculate reliable template-to-target alignments even in the case of distantly related proteins (sequence identity < 30%). Other BAR+ based applications have been developed during my doctorate including the prediction of Magnesium binding sites in human proteins, the ABC transporters superfamily classification and the functional prediction (GO terms) of the CAFA targets. Remarkably, in the CAFA assessment, BAR+ placed among the ten most accurate methods. At present, as a web server for the functional and structural protein sequence annotation, BAR+ is freely available at http://bar.biocomp.unibo.it/bar2.0.

Experiments on Pro-Sociality and Environmental Issues

The thesis comprises three essays that use experimental methods, one about other-regarding motivations in economic behavior and the others on pro-social behavior in two environmental economics problems. The first chapter studies how the expectations of the others and the concern to maintain a balance between effort exerted and rewards obtained interact in shaping the behavior in a modified dictator game. We find that dictators condition their choices on recipients' expectations only when there is a high probability that the the recipient will not be compensated for her effort. Otherwise, dictators tend to balance the efforts and rewards of the recipients, irrespective of the recipients' expectations. In the second chapter, I investigate the problem of local opposition to large public projects (e.g. landfills, incinerators, etc.). In particular, the experiment shows how the uncertainty about the project's quality makes the community living in the host site skeptical about the project. I also test whether side-transfers and costly information disclosure can help to increase the efficiency. Both tools succesfully make the host more willing to accept the project, but they lead to the realization of different types of projects. The last chapter is an experiment on climate negotiations. To avoid the global warming, countries are called to cooperate in the abatement of their emissions. We study whether the dynamic aspect of the climate change makes cooperation across countries behaviorally more difficult. We also consider inequality across countries as a possible factor that hinders international cooperation.