Antibody Modeling and Structure Analysis. Application to biomedical problems.

Background The usefulness of antibodies and antibody derived artificial constructs in various medical and biochemical applications has made them a prime target for protein engineering, modelling, and structure analysis. The huge number of known antibody sequences, that far outpaces the number of solved structures, raises the need for reliable automatic methods of antibody structure prediction. Antibodies have a very characteristic molecular structure that is reflected in their modelling technique. Currently, the most accurate models are produced using a quite peculiar modelling strategy, developed among others by our group: the framework regions are modelled with a standard comparative modelling approach, whereas the hypervariable loops are predicted using the ad-hoc “canonical structure method”, historically based on expert analysis of the available antibody solved structures. More than thirty years passed since this modelling method was initially developed, nonetheless there is still a huge effort in the academic and pharmaceutical communities to improve its accuracy. The reason for this lies in several error sources in the current modelling process. First of all, given the large amount of available structures, it was impossible to manually update “canonical structure” classes and rules. Moreover, the lack of specific studies on the packing between the VL and the VH domains and on possible conformational changes occurring upon antigen binding was impairing the integration in the modelling techniques of such factors. Aim The general aim of this study is to carry out an extensive characterization and annotation of immunoglobulin molecules i.e. to deepen our understanding of the molecular basis of their specificity using a combination of bioinformatics sequence- and structure-based analysis. I carried out improvements to the antibody modelling protocols by revising the canonical structure definitions and by minimizing the errors arising from VL and the VH domain packing at the same time by taking care of the conformational changes occurring upon antigen binding. Results During the past years, we successfully improved the description of the structural repertoire of immunoglobulins with lambda light chains, which has both practical (design, engineering and humanization) and theoretical applications (improvement of the antibody modelling)[1]. Our large-scale analysis of the association of heavy and light chain variable domains in antibodies showed that there are essentially two different modes of interaction that can be identified by the presence of key amino acids in specific positions of the antibody sequences [2]. Interestingly, we also found that the different packing modes are related to the volume and type of recognized antigen. These findings are clearly relevant for the design of antibodies and of antibody libraries. The investigation of the antibody conformational changes upon antigen binding allowed us to identify sections on variable and constant regions that show significant flexibility when comparing the antigen bound/unbound forms of immunoglobulins. The results of all the above-mentioned analyses have been implemented in our in-house immunoglobulin structure prediction server (PIGS, automatic Prediction of ImmunoGlobulin Structure), thus helping to minimize the sources of errors in the current modelling process. Consequent to our results, we were asked to write a chapter in Encyclopaedia of Biophysics on antibody modelling [3]. A further step in the direction of improving the understanding of antibody recognition mechanisms was to put together all the annotations of immunoglobulins in a publicly available database. To this aim, we constructed a database of immunoglobulin sequences and integrated tools (DIGIT) [4], which is becoming an extensively used resource by the community. DIGIT stores sequences of annotated immunoglobulin variable domains and offers to the user several tools for searching and analysing them. Our experience in antibody modelling allowed us to approach two biomedical problems in collaboration with Prof. Arcaini (University of Pavia) and Prof. Fabio Ghiotto (University of Genova). More specifically, by applying the tools we developed and all our theoretical knowledge we successfully analysed the immunoglobulin repertoires of SMZL (splenic marginal zone lymphoma) and CLL (chronic lymphocytic leukaemia) patient data. Both the CLL and SMZL patients are known to have a biased usage of immunoglobulin (IG) heavy variable (IGHV) genes and stereotyped B-cell receptors (BCRs), used as a marker in disease prognosis. We extended these analyses by taking into account VL germlines, VL-VH pairing and structural information, thus giving a more detailed view of the immunoglobulin repertoire in terms of sequence, structure and function. Analysing the immunoglobulins of patients with CLL, we discovered statistically significant differences among immunoglobulins in patients with favourable and unfavourable prognosis. A paper describing this work has been submitted [5]. The poster describing the results of SMZL repertoire analysis was accepted at the 2012 American Society of Haematology (ASH) meeting and published as an abstract [6]. Reference: 1. Chailyan, A., P. Marcatili, et al. (2011). "Structural repertoire of immunoglobulin lambda light chains." Proteins 79(5): 1513-1524. 2. Chailyan, A., P. Marcatili, et al. (2011). "The association of heavy and light chain variable domains in antibodies: implications for antigen specificity." FEBS J 278(16): 2858-2866. 3. Marcatili P., A. Chailyan, D. Cirillo and A. Tramontano. Modelling of antibody structures. Encyclopaedia of Biophysics. Springer (2012). 4. Chailyan, A., A. Tramontano, et al. (2012). "A database of immunoglobulins with integrated tools: DIGIT." Nucleic Acids Res. doi:10.1093/nar/gkr806. 5. Marcatili P., F. Ghiotto, C. Tenca, A. Chailyan, A. N. Mazzarello, X. Yan, M. Colombo, E. Albesiano, D. Bagnara, G. Cutrona, F. Morabito, S. Bruno, M. Ferrarini, N. Chiorazzi, A. Tramontano, F. Fais. "Immunoglobulins produced by chronic lymphocytic leukaemia B cells show limited binding site structure variability." submitted 6. Marcatili P., S. Zibellini, S. Rattotti, A. Chailyan, M. Varettoni, L. Morello, E. Boveri, M. Lucioni, M. Bonfichi, M. Gotti, V. Fiaccadori, M. Paulli, A. Tramontano, L. Arcaini. "Hierarchical Clustering of B-Cell Receptor Structures in Splenic Marginal Zone Lymphoma", abstract, American Society of Haematology (ASH) meeting

Novel approaches to assess cellular interactions and their role in the pathology and treatment of lymphoproliferative disorders

Background: Migration and homing are essential to B-lymphocyte mediated immunity, and are driven by rapid, directed, and appropriate reorganisation of the actin cytoskeleton. Important observations have linked the cytoskeletal-rearrangements made by leukaemic B-lymphocytes of chronic lymphocytic leukaemia (CLL) to disease pathology. In particular, cytoskeletal alterations mediated by B-cell receptor (BCR) engagement or chemokine-binding are recognised to be central to the migration of CLL cells to lymphoid tissues, where they engage in the complex cellular and molecular interactions that underlie their survival, proliferation, and drug resistance. Further emphasising this importance, has been the observation that highly effective small molecule inhibitors that target key components of the BCR signalling machinery, such as Bruton’s tyrosine kinase (BTK), disrupt the migratory behaviour of CLL cells, and that this may, at least in part, underlie their clinical effect. Detailed characterisation of the highly dynamic cytoskeletal alterations in CLL may, therefore, inform novel therapeutic interventions, particularly for subsets with unmet clinical needs, such as those with mutations affecting the tumour protein P53 (TP53), ataxia telangiectasia mutated (ATM), or Notch receptor 1 (NOTCH1) genes, which are all more frequent in IGHV-unmutated disease. This work describes the development of techniques to characterise and quantify morphological responses to inhibitors, aiming to produce a quantitative description of cytoskeletal changes relating to specific signalling pathways, and to suggest rational drug combinations in the disorder. Methods: Primary CLL cells were cultured at high density with autologous T-lymphocytes and monocytes in the presence of specific signal inhibitors. The morphological responses of leukaemic cells were determined using a range of microscopic techniques, including scanning electron microscopy, and immunocytofluorescent detection of cytoskeletal and plasma membrane components. Cytoskeletal alterations were evaluated via computer-aided morphometric analyses of cell shape parameter, homotypic cellular interactions, and migration, generating a precise description of changes to the polymerised F-actin cytoskeleton and cell behaviour. Matrigel™ matrix models were combined with transmission electron microscopy to study cellular morphology within a 3D tumour microenvironment (TME)-like setting. Immunogold labelling of specific proteins within neoplastic lymphocytes was performed to allow visualisation of protein localisation changes in response to signal inhibition at the ultrastructure level. Results: This study tested inhibitors targeting different signalling pathways as a ‘proof of principle’ evaluation to determine whether the morphological and behavioural responses induced could be effectively distinguished from one another and quantitatively described. Inhibition of BTK by ibrutinib resulted in uniform populations of globular cells with retained polarity and, consequently, increased amoeboid motility. BTK blockage is recognised to impair integrin-mediated retention of leukaemic cells within tissue niches, leading to the observed peripheral blood lymphocytosis seen in CLL patients receiving ibrutinib. Reduced integrin-mediated motility was associated with impaired homotypic cellular interactions within IGHV-mutated cases specifically, indicating that this subgroup may have a greater dependency on elongated-type migration for permitting pro-survival cellular contact than their IGHV-unmutated counterparts. Disruption of Rho-associated protein kinase 1 (ROCK1) activity by Y-27632 lead to impaired actomyosin-mediated retraction of cytoskeletal processes. Loss of the ROCK1-induced cytoskeletal asymmetry required for effective cell migration resulted in reduced CLL cellular interactions; however, CXCL12-driven motility was attenuated in IGHV¬-mutated cases alone. The Abelson kinase 1 (ABL1) inhibitor imatinib caused CLL cells to acquire a globular phenotype with frequent microvilli, similar to that of B-lymphocytes isolated directly from the peripheral blood. Transient cellular interactions were markedly reduced by imatinib, whereas elongated-type motility, being a largely ABL1-independent process, was unaltered. The morphological and behavioural responses of CLL cells were compared to those observed in mantle cell lymphoma (MCL) cell lines. These cells lines, which were utilised as a surrogate model for BTK inhibitor sensitivity in CLL, demonstrated that the establishment of anterior-posterior morphology, mediated by the activity of ROCK1 and ABL1, is essential for effective trafficking of B-lymphocytes to protective niches, regardless of ibrutinib sensitivity. Blockage of NOCTH1 signalling by gamma-secretase inhibitors (GSIs) PF-03084014 and R04929097 resulted in varying morphological responses, possibly indicating differences in NOTCH1 activation between CLL cases. Despite chemotaxis being identified as a key NOTCH1-regulated process, CLL and NOTCH1-mutated MCL cells demonstrated enhanced directional transmigration with GSI treatments. MCL cell lines were utilised to model the effects of GSI sensitivity in CLL. In contrast, NOTCH1-unmutated MCL cells displayed unaltered migration with PF-03084014 pre-treatment, consistent with reports of low GSI sensitivity in MCL cells exhibiting unmutated NOTCH1, and reduced chemotaxis with R04929097. The developed 3D ex vivo culture system preserved CLL cell viability, migration, and dynamic cellular interactions, as demonstrated by flow cytometry and time-lapse live-cell imaging. Interrogation with transmission electron microscopy enabled high-resolution visualisation of cell morphology within a TME-like setting; however, further optimisation of immunogold labelling of effector proteins is required. Conclusion: Using novel imaged-based morphometric analyses, distinct signal inhibitor-induced cytoskeletal adaptations were identified in CLL B-lymphocytes. This approach may be applied to prognostically-defined subgroups or resistant cases to provide in-depth characterisation of morphological responses to novel therapeutic agents and to assess treatment responses within the TME. These observations, when combined with transcriptional data, may allow more effective combinational targeting of behavioural signatures unique to the patient and, thus, improve treatment outcomes in the disease.Plymouth and District Leukaemia Fun

Diagnostic Biosensors for Detection of Blood-Derived Biomarkers

Standard diagnostic tools used from patient samples, specifically from blood draws, require specialized equipment, personnel, and facilities. Conventional techniques can often be very laborious and time consuming due to required sample preparation. The evident delay from sample collection to a patient’s result immensely impacts their outcome. The aims of this research are to design diagnostic biosensors that decrease time-to-results, minimize reagent and sample handling, and incorporate automated simple optical transduction and user interfaces for the detection of blood-derived biomarkers. Specifically, four biosensing detection mechanisms performed on 3 different point-of-care platforms will be discussed. First is a static loop-mediated isothermal amplification (LAMP) of nucleic acid aqueous droplet on a silicone chip platform immersed in mineral oil. The target-of-interest is a nucleic acid sequence as a biomarker for antibiotic resistant bacteria. The biosensing technique used related changes in interfacial tension (IFT) at the water-oil interface by measuring the change in contact angle (geometrical-effects) over time. Initially the system was characterized as a linear response in relation to concentration of bacteria in a buffer system down to the limit of detection (LOD) of 100 CFU per uL. Subsequently, with the addition of bacterial infected blood sample models, the system became a binary assay (i.e. yes or no) as low as 10 CFU per uL within 10 min of reaction. Secondly, a two-layered, paper microfluidic chip was utilized to quantify cancer cells from a buffy coat sample matrix by two detection mechanisms: 1) on-chip particle enumeration via smartphone microscope and 2) capillary flow dynamics via smartphone video processing. The assay resulted in a LOD as low as 1 cell per uL for the on-chip imaging aspect of platform and 0.1 cell per uL for the capillary flow analysis within 13 to 22s post application of blood sample. Lastly, the same concepts previously described in the first platform utilizes changes in IFT due to amplicon presence in an aqueous solution immersed in mineral oil. An emulsion LAMP platform was investigated to determine the relation between angle-dependent light scatter intensity (based off Mie scatter theory) and nucleic acid amplification progression. The phenomenon attributing to changes in light scatter intensities is due to the interfacial changes occurring in the emulsion droplets, where amplicon amount increases the IFT decreases, resulting in smaller diameter emulsions. Changes in light scatter intensity within 3 min of the reaction shows statistical difference in comparison to no target control (NTC) for 10^3 CFU per uL of bacteria dosed into aqueous sample. These four detection mechanisms and three platforms offer but a few alternatives as biosensing methods for blood-derived diagnostic biosensors

Artificial Intelligence Predicted Overall Survival and Classified Mature B-Cell Neoplasms Based on Immuno-Oncology and Immune Checkpoint Panels

Artificial intelligence (AI) can identify actionable oncology biomarkers. This research integrates our previous analyses of non-Hodgkin lymphoma. We used gene expression and immunohistochemical data, focusing on the immune checkpoint, and added a new analysis of macrophages, including 3D rendering. The AI comprised machine learning (C5, Bayesian network, C&R, CHAID, discriminant analysis, KNN, logistic regression, LSVM, Quest, random forest, random trees, SVM, tree-AS, and XGBoost linear and tree) and artificial neural networks (multilayer perceptron and radial basis function). The series included chronic lymphocytic leukemia, mantle cell lymphoma, follicular lymphoma, Burkitt, diffuse large B-cell lymphoma, marginal zone lymphoma, and multiple myeloma, as well as acute myeloid leukemia and pan-cancer series. AI classified lymphoma subtypes and predicted overall survival accurately. Oncogenes and tumor suppressor genes were highlighted (MYC, BCL2, and TP53), along with immune microenvironment markers of tumor-associated macrophages (M2-like TAMs), T-cells and regulatory T lymphocytes (Tregs) (CD68, CD163, MARCO, CSF1R, CSF1, PD-L1/CD274, SIRPA, CD85A/LILRB3, CD47, IL10, TNFRSF14/HVEM, TNFAIP8, IKAROS, STAT3, NFKB, MAPK, PD-1/PDCD1, BTLA, and FOXP3), apoptosis (BCL2, CASP3, CASP8, PARP, and pathway-related MDM2, E2F1, CDK6, MYB, and LMO2), and metabolism (ENO3, GGA3). In conclusion, AI with immuno-oncology markers is a powerful predictive tool. Additionally, a review of recent literature was made

DEFINITION OF BIOLOGICAL RESPONSES THROUGH THE ANALYSIS OF GENE EXPRESSION PROFILES

The aim of this PhD project was the development of a pipeline for the analysis of expression data and a set of of different strategies to extract biological informations from micrarray experiments. The computational pipeline for processing raw microarray data (images) to define gene expression levels, to provide experiment quality assessment and significativity statistical tests, was implemented in R, using mostly Bioconductor packages. The first fase had as purpose the determination of the gene function combining experiments of silecing with the gene expression analysis. Caspase-2 is a member of a cystein-protease family that carry out important roles in the apoptosis and in the inflammation. Altough it is highly conserved from the evolutionary point of view, in the literature several contradictory results are found. Being expressed at high level during the neurological development and with a strong involvement in the apoptotic processes in the adult central nervous system, we decided to proceed with the silecing of the gene that codifies for this enzyme using glioblastoma cells, a very aggressive cerebral tumor. The comparative analysis of expression profiles of silenced cells respect to the control ones, highlighted the relation between CASP2 and genes involved in the cholesterol metabolism. Previuos studies have suggested for this enzime a role in the control of intracellular level of this metabolite. Therefor, we decided to use data stored in public databases in order to to extend the investigation, including all the other caspases and all the genes in same way connected to cholesterol. After we had obtained the data related to several different experiments, we went ahead with the computation of the correlation between expression levels and, then, based of these values, with the clustring analysis in order to see which among the caspases has the same corralational profile. After that, the analysis was expanded to normal brain and liver tissues, in order to know whether the situation observed in the patological condition is unique or if it can be overlayed to that present in normal tissues. In the second phase, I performed an analysis of expression data with a completely different purpose. The aim of this project was the definition of the signaling pathways and of the resistence mechanisms induced by the treatment of cancer cells obtained from patients affected by cronic lymphocytic leukemia and treated with a new category of ubiquitin proteasome system (UPS) inhibitors. Through the comparison of trascriptional profiles before and after the treatment, many genes connected with the drug action at cellular level, whose expression was altered by the UPS inhibitor, were identified. Furthermore, considering the difference in terms of responsiveness of the analized patients, we could determine some genes responsible of the different efficacy of the farmacological treatment

Improving the accuracy of the structure prediction of the third hypervariable loop of the heavy chains of antibodies

Motivation: Antibodies are able to recognize a wide range of antigens through their complementary determining regions formed by six hypervariable loops. Predicting the 3D structure of these loops is essential for the analysis and reengineering of novel antibodies with enhanced affinity and specificity. The canonical structure model allows high accuracy prediction for five of the loops. The third loop of the heavy chain, H3, is the hardest to predict because of its diversity in structure, length and sequence composition.Results: We describe a method, based on the Random Forest automatic learning technique, to select structural templates for H3 loops among a dataset of candidates. These can be used to predict the structure of the loop with a higher accuracy than that achieved by any of the presently available methods. The method also has the advantage of being extremely fast and returning a reliable estimate of the model quality.Availability and implementation: The source code is freely available at http://www.biocomputing.it/H3Loopred/Contact: [email protected] Information: Supplementary data are available at Bioinformatics online

Cellular and personalized therapies in multiple myeloma with special emphasis on retargeted natural killer cells

Multiple myeloma (MM) is a clonal plasma cell malignancy accounting for approximately 10% of all hematological cancer cases. Despite considerable advances in MM management, which led to exceptional response and survival rates, patients still experience relapse and cure remains elusive. Personalized, antibody-based and cellbased immunotherapies have given new hope to patients with relapsed or refractory disease. The aim of this thesis was to investigate the potential of novel targeted treatments for MM, specifically those based on Natural Killer (NK) cells and patient stratification. Studies I and II focus on retargeting applications of Natural Killer (NK) cells. NK cells have emerged as a promising alternative to current T cell-based therapies, due to their potent effector functions, safer profile and possibility for use as off-the-shelf treatments. However, the immunosuppressive microenvironment of MM drives NK cell dysfunctionality which impacts the efficacy of adoptive NK cell therapy. To address this issue we have relied on chimeric receptors; a strategy that is proven to enhance the targeting potential of NK cells while improving the exertion of cytotoxicity. The first study centers around CD38; a protein that is highly expressed on the surface of myeloma cells. CD38-targeting with monoclonal antibodies, such as Daratumumab and Isatuximab, has revolutionized MM treatment, inducing durable responses in a fraction of patients. Targeting CD38 using Chimeric Antigen Receptor (CAR)-expressing NK has also been attempted. It is, however, met with feasibility challenges, such as the intrinsic CD38 expression on NK cells which may lead CAR-NK cells to perform fratricide. Here, we demonstrate an alternative approach by harnessing the CD38dim phenotype occurring during long-term cytokine stimulation of primary NK cells. Our findings show that the combination of a functional, affinity-optimized αCD38-CAR construct with a suitable NK cell expansion and activation protocol results in a promising immunotherapeutic strategy for MM. The second study aims to improve outcomes of adoptive NK cell therapy by converting the inhibitory signals, that NK cells receive from the PD1/ PD-L1 axis, to stimulating signals. For this purpose, we designed novel PD1-based chimeric switch receptors (PD1-CSRs) by fusing the PD1 ectodomain to the activating signaling domains of NKp46, DAP10, DAP12, and CD3ζ. The results show that PD1-CSR+ NK cells exert potent anti-tumor activity against PD-L1+ cancer cell lines and primary MM cells in vitro, laying the foundation for improved treatment of PD-L1+ tumors. The third study investigates the use of the BCL2 inhibitor Venetoclax in MM and ALamyloidosis patients harboring the t(11;14) genetic mutation. This clinical study concludes that treatment with a daily low-dose of Venetoclax is adequately safe and has promising efficacy. The study also identifies resistance mechanisms associated with t(11;14), such as the downregulation of IRF5 targeted genes, which can be further exploited by therapeutic interventions. Overall, the present doctoral thesis investigates novel approaches of NK cell-based immunotherapy and stratified chemotherapy for MM. The findings of these studies provide foundation for future research in the field and contribute to the expansion of current therapeutic options

Integrative approaches to high-throughput data in lymphoid leukemias (on transcriptomes, the whole-genome mutational landscape, flow cytometry and gene copy-number alterations)

Within this thesis I developed a new approach for the analysis and integration of heterogeneous leukemic data sets applicable to any high-throughput analysis including basic research. All layers are stored in a semantic graph which facilitates modifications by just adding edges (relationships/attributes) and nodes (values/results) as well as calculating biological consensus and clinical correlation. The front-end is accessible through a GUI (graphical user interface) on a Java-based Semantic Web server. I used this framework to describe the genomic landscape of T-PLL (T-cell prolymphocytic leukemia), which is a rare (~0.6/million) mature T-cell malignancy with aggressive clinical course, notorious treatment resistance, and generally low overall survival. We have conducted gene expression and copy-number profiling as well as NGS (next-generation sequencing) analyses on a cohort comprising 94 T-PLL cases. TCL1A (T-cell leukemia/lymphoma 1A) overexpression and ATM (Ataxia Telangiectasia Mutated) impairment represent central hallmarks of T-PLL, predictive for patient survival, T-cell function and proper DNA damage responses. We identified new chromosomal lesions, including a gain of AGO2 (Argonaute 2, RISC Catalytic Component; 57.14% of cases), which is decisive for the chromosome 8q lesion. While we found significant enrichments of truncating mutations in ATM mut/no del (p=0.01365), as well as FAT (FAT Atypical Cadherin) domain mutations in ATM mut/del (p=0.01156), JAK3 (Janus Kinase 3) mut/ATM del cases may represent another tumor lineage. Using whole-transcriptome sequencing, we identified novel structural variants affecting chromosome 14 that lead to the expression of a TCL1A-TCR (T-cell receptor) fusion transcript and a likely degradated TCL1A protein. Two clustering approaches of normal T-cell subsets vs. leukemia gene expression profiles, as well as immunophenotyping-based agglomerative clustering and TCR repertoire reconstruction further revealed a restricted, memory-like T-cell phenotype. This is to date the most comprehensive, multi-level, integrative study on T-PLL and it led to an evolutionary disease model and a histone deacetylase-inhibiting / double strand break-inducing treatment that performs better than the current standard of chemoimmunotherapy in preclinical testing

In vitro and in vivo study of the role of the mitochondria-shaping protein Opa1 in cancer

Mitochondria are double membrane–enclosed organelles that play a crucial role in ATP production, metabolism, regulation of cellular signaling and amplification of programmed cell death (Wasilewski and Scorrano, 2009). In the process of apoptosis mitochondria release cytochrome c and other cofactors that are required to amplify cell death (Li et al., 1997). The complete release of cytochrome c depends on the changes in the shape and in the ultrastructure of the organelle, since during these processes mitochondrial network undergoes fragmentation, that is accompanied by cristae remodeling and widening of cristae junctions (Frank et al., 2001; Scorrano et al., 2002). Of note, deregulation of apoptosis represents a typical hallmark of cancer, since cancer cells exploit the inhibition of the mitochondrial arm of apoptosis to acquire the malignant phenotype (Thompson, 1995). Mitochondria are dynamic organelles, and all processes that impinge on the changes in the shape and in the ultrastructure of the organelle are controlled by a regulated action of mitochondria shaping proteins, which represent large GTPases that share structural homology with the dynamin protein family (Dimmer and Scorrano, 2006). Mitochondrial shape in the steady state is a result of the balanced action of fission and fusion events (Griparic and van der Bliek, 2001). The process of mitochondrial fission is controlled by a synchronized action of a cytosolic protein Drp1 (Dynamin – related protein 1) (Cereghetti et al., 2008), that is recruited to the outer mitochondrial membrane where it binds its adaptors Fis1 (Fission – 1), MFF (Mitochondrial fission factor), Mid49 and Mid51 (Mitochondrial division), and participates in the division of mitochondria (Palmer et al., 2011). Mitochondrial fusion, on the other hand, is a process controlled by mitofusins (Mfn1 and Mfn2), proteins located in the outer mitochondrial membrane, together with the only inner membrane GTPase - Optic Atrophy 1 (Opa1) (Santel and Fuller, 2001; Chen et al., 2003; Cipolat et al., 2004). In humans, alternative splicing of Opa1 gives rise to 8 mRNA splice variants which further get processed by proteolytic proteases giving rise to 2 long and 3 short forms of Opa1 (Olichon et al., 2007; Duvezin-Caubet et al., 2007). Opa1 is a multifunctional protein: apart from its function in promoting mitochondrial fusion (Cipolat et al., 2004), it also plays a role in the control of apoptosis by keeping in check the cristae remodeling pathway, by forming multimeric complexes at the cristae junctions, keeping in shape the size of these junctions (Frezza et al., 2006; Cipolat et al., 2006). Another important role of Opa1 is in the control of mitochondrial metabolism, because Opa1 favors the superassembly of respiratory chain complexes into supercomplexes, increasing the efficiency of oxidative phosphorilation (Cogliati et al., 2013). All these functions concur to determine the beneficial outcome of its mild overexpression in vivo, which protects from heart and brain ischaemia, denervation-induced muscular atrophy and fulminant hepatitis (Varanita et al., 2015). Furthermore, it corrects mouse models of primary mitochondrial dysfunction caused by defects in components of the respiratory chain (Civiletto et al., 2015). However, all these beneficial effects come with a counterpart, since a handful of studies reported that Opa1 is overexpressed in several human cancers where high levels of Opa1 correlated with a worst prognosis and an impaired response to therapy (Fang et al., 2012), while blocking its expression was associated with an induction of the mitochondria - associated apoptotic pathway in the cancer cell and a better clinical outcome (Zhao et al., 2013). In this Thesis we set out to understand what role does Opa1 play in the acquisition and maintenance of the cancer phenotype, both in cellular and animal models, while reasoning that a possible explanation why we don’t have constitutively high Opa1 levels is the fact that the trade off of Opa1 overexpression could be an increased susceptibility to cancer development/progression. Well established cell lines, initially deriving from patients diagnosed with diffuse large B cell lymphoma (DLBCL) served as our in vitro model system. DLBCLs are one of the most common adult non-Hodgkin lymphoid malignancies today (Lohr et al., 2012). They are a genetically heterogeneous group of tumors that can be further divided in several subsets, identified by their distinct molecular signatures (Alizadeh et al., 2000). Genome wide arrays and multiple clustering algorithms defined a B cell receptor (BCR)/proliferation cluster (BCR–DLBCL), which displays upregulation of genes encoding BCR signaling components, and an OxPhos cluster (OxPhos–DLBCL) which is enriched in genes involved in mitochondrial oxidative phosphorylation. The OxPhos subset lacks an intact BCR signaling network, suggesting dependence on alternative survival mechanisms, which are not yet defined (Monti et al., 2005; Caro et al., 2012). Since a proteomic approach, aimed at carefully dissecting components of the mitochondrial proteome in the BCR versus OxPhos cell group, identified increased levels of Opa1 in the OxPhos (Danial N, manuscript in preparation), we wished to elucidate what role does Opa1 play in these cancer cell subsets. In order to test whether Opa1 overexpression contributes to the development and progression of cancer in vivo, we reached out to an already established mouse lymphoma model, the Eµ-myc transgenic mouse (Adams et al., 1985), that we further crossed with a mouse model of controlled Opa1 overexpression that was recently generated in our lab (Cogliati et al., 2013), and the net result of this cross gave rise to the mouse model we used in our study. In this Thesis we present evidence that Opa1 is increasingly processed in the BCR subset of diffuse large B cell lymphoma, and that mitochondrial morphology, metabolism, and ultrastructure are different between the BCR and the OxPhos DLBCL subsets that display different levels of Opa1. Furthermore, we also show evidence of a marked synergy between Opa1 and c-Myc in doubly transgenic mouse models, where Opa1 overexpression is contributing to the development of, and aggravating cancer in Eμ-Myc transgenic animals. The work performed in this thesis highlights a role for Opa1 in DLBCL features, and tumor progression in vivo. Thus, our data indicate that Opa1 displays oncogenic features and it can be taken into consideration as a novel therapeutic target for cancer treatment