Profilo genomico e di espressione dell'Osteosarcoma. Identificazione dei geni con alto grado di correlazione tra numero di copie ed espressione

To identify the regions of recurrent copy number abnormality in osteosarcoma and their effect on gene expression, we performed an integrated genome-wide high-resolution array CGH (aCGH) and gene expression profiling analysis on 40 human OS tissues and 12 OS cell lines. This analysis identified several recurrent chromosome regions that contain genes that show a gene dosage effect on gene expression. A further search, performed on those genes that were over-expressed and localized in the frequently amplified chromosomal regions, greatly reduced the number of candidate genes while their characterization using gene ontology (GO) analysis suggests the importance of the deregulation of the G1-to-S phase in the development of the disease. We also identified frequent deletions on 3q in the vicinity of LSAMP and performed a fine mapping analysis of the breakpoints. We precisely mapped the breakpoints in several instances and demonstrated that the majority do not involve the LSAMP gene itself, and that they appear to form by a process of non-homologous end joining. In addition, aCGH analysis revealed frequent gains of IGF1R that were highly correlated with its protein level. Blockade of IGF1R in OS cell lines with high copy number gain led to growth inhibition suggesting that IGF1R may be a viable drug target in OS, particularly in patients with copy number driven overexpression of this receptor.

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.