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.

Adattamento morfologico e variazione dell'espressione genica dopo pneumonectomia: osservazioni su modello sperimentale animale

Introduzione La pneumonectomia su modello animale potrebbe essere un’utile piattaforma di studio per approfondire i meccanismi della risposta compensatoria al danno polmonare. Scopo dello studio è determinare la presenza di variazioni morfologiche e di espressione del trascrittoma dopo pneumonectomia. Materiali e metodi Undici suini sono stati sottoposti a pneumonectomia sinistra. Sono stati eseguiti prelievi sito-specifici intraoperatori su polmone sinistro e successivamente confrontati con prelievi sito-specifici su polmone destro dopo eutanasia a 60 giorni. I prelievi degli animali con decorso regolare sono stati sottoposti a RNA-sequencing e successiva analisi computazionale per valutare il peso funzionale del singolo gene o di clusters di geni. Risultati Un animale è stato escluso per insorgenza di ernia diaframmatica. In 7/10 è stata riscontrata apertura della pleura mediastinica con parziale erniazione del polmone controlaterale e shift mediastinico. L’istologia ha mostrato dilatazione degli spazi aerei, rottura dei setti interalveolari, lieve infiammazione, assenza di fibrosi, stiramento radiale dei bronchi e riduzione del letto capillare. L’analisi di bulk RNA-sequencing ha identificato 553 geni espressi in modo differenziale (DEG)(P<0,001) tra pre e post-pneumonectomia. I primi 10 DEG up-regolati: Edn1, Areg, Havcr2, Gadd45g, Depp1, Cldn4, Atf3, Myc, Gadd45b, Socs3; i primi 10 geni down-regolati: Obscn, Cdkn2b, ENSSSCG00000015738, Prrt2, Amer1, Flrt3, Efnb2, Tox3, Znf793, Znf365. Tra i DEG è stata riscontrata una predominanza di geni specifici dei macrofagi. L’analisi di gene ontology basata su DAVID ha mostrato un significativo arricchimento della "via di segnalazione apoptotica estrinseca"(FDR q=7,60x10 -3), della via di “Risposta all’insulina”(FDR q=7,60x10 -3) ed un arricchimento di geni “Regolatori negativi del segnale DDX58/IFIH1”(FDR q=7.50x10 -4). Conclusioni Il presente studio conferma la presenza di variazioni macroscopiche e microscopiche fenotipiche dopo pneumonectomia. L’RNA sequencing e lo studio di genomica traslazionale hanno mostrato l’esistenza di geni singoli e di network di geni disregolati dopo pneumonectomia, prevalentemente in determinate popolazioni cellulari.

Deep learning and embeddings for problems of computational biology

The development of Next Generation Sequencing promotes Biology in the Big Data era. The ever-increasing gap between proteins with known sequences and those with a complete functional annotation requires computational methods for automatic structure and functional annotation. My research has been focusing on proteins and led so far to the development of three novel tools, DeepREx, E-SNPs&GO and ISPRED-SEQ, based on Machine and Deep Learning approaches. DeepREx computes the solvent exposure of residues in a protein chain. This problem is relevant for the definition of structural constraints regarding the possible folding of the protein. DeepREx exploits Long Short-Term Memory layers to capture residue-level interactions between positions distant in the sequence, achieving state-of-the-art performances. With DeepRex, I conducted a large-scale analysis investigating the relationship between solvent exposure of a residue and its probability to be pathogenic upon mutation. E-SNPs&GO predicts the pathogenicity of a Single Residue Variation. Variations occurring on a protein sequence can have different effects, possibly leading to the onset of diseases. E-SNPs&GO exploits protein embeddings generated by two novel Protein Language Models (PLMs), as well as a new way of representing functional information coming from the Gene Ontology. The method achieves state-of-the-art performances and is extremely time-efficient when compared to traditional approaches. ISPRED-SEQ predicts the presence of Protein-Protein Interaction sites in a protein sequence. Knowing how a protein interacts with other molecules is crucial for accurate functional characterization. ISPRED-SEQ exploits a convolutional layer to parse local context after embedding the protein sequence with two novel PLMs, greatly surpassing the current state-of-the-art. All methods are published in international journals and are available as user-friendly web servers. They have been developed keeping in mind standard guidelines for FAIRness (FAIR: Findable, Accessible, Interoperable, Reusable) and are integrated into the public collection of tools provided by ELIXIR, the European infrastructure for Bioinformatics.