Preclinical neuroblastoma models for a pharmacological study of a new MYCN oncogene inhibitor

Background. Neuroblastoma is the most deadly solid tumor of childhood. In the 25% of cases it is associated with MYCN amplification (MA), resulting in the disregulation of several genes involved in cancer progression, chemotherapy resistance and poor prognosis causing the disregulation of several genes involved in cancer progression and chemotherapy resistance and resulting in a poor prognosis. Moreover, in this contest, therapy-related p53 mutations are frequently found in relapsed cases conferring an even stronger aggressiveness. For this reason, the actual therapy requires new antitumor molecules. Therefore, rapid, accurate, and reproducible preclinical models are needed to evaluate the evolution of the different subtypes and the efficacy of new pharmacological strategies. Procedures. We report the real-time tumorigenesis of MA Neuroblastoma mouse models: transgenic TH-MYCN mice and orthotopic xenograft models with either p53wt or p53mut, by non-invasive micro PET and bioluminescent imaging, respectively. Characterization of MYCN amplification and expression was performed on every collected sample. We tested the efficacy of a new MYCN inhibitor in vitro and in vivo. Results. MicroPET in TH-MYCN mice permitted the identification of Neuroblastoma at an early stage and offered a sensitive method to follow metabolic progression of tumors. The MA orthotopic model harboring multitherapy-related p53 mutations showed a shorter latency and progression and a stronger aggressiveness respect to the p53wt model. The presence of MA and overexpression was confirmed in each model and we saw a better survival in the TH-MYCN homozigous mice treated with the inhibitor. Conclusions. The mouse models obtained show characteristics of non-invasiveness, rapidity and sensitivity that make them suitable for the in vivo preclinical study of MA-NB. In particular, our firstly reported p53mut BLI xenograft orthotopic mouse model offers the possibility to evaluate the role of multitherapy-related p53 mutations and to validate new p53 independent therapies for this highly aggressive Neuroblastoma subtype. Moreover, we have shown potential clinical suitability of an antigene strategy through its cellular and molecular activity, ability to specifically inhibit transcription and in vivo efficacy with no evidence of toxicity.

Stochastic models and dynamic measures for the characterization of bistable circuits

During the last few years, a great deal of interest has risen concerning the applications of stochastic methods to several biochemical and biological phenomena. Phenomena like gene expression, cellular memory, bet-hedging strategy in bacterial growth and many others, cannot be described by continuous stochastic models due to their intrinsic discreteness and randomness. In this thesis I have used the Chemical Master Equation (CME) technique to modelize some feedback cycles and analyzing their properties, including experimental data. In the first part of this work, the effect of stochastic stability is discussed on a toy model of the genetic switch that triggers the cellular division, which malfunctioning is known to be one of the hallmarks of cancer. The second system I have worked on is the so-called futile cycle, a closed cycle of two enzymatic reactions that adds and removes a chemical compound, called phosphate group, to a specific substrate. I have thus investigated how adding noise to the enzyme (that is usually in the order of few hundred molecules) modifies the probability of observing a specific number of phosphorylated substrate molecules, and confirmed theoretical predictions with numerical simulations. In the third part the results of the study of a chain of multiple phosphorylation-dephosphorylation cycles will be presented. We will discuss an approximation method for the exact solution in the bidimensional case and the relationship that this method has with the thermodynamic properties of the system, which is an open system far from equilibrium.In the last section the agreement between the theoretical prediction of the total protein quantity in a mouse cells population and the observed quantity will be shown, measured via fluorescence microscopy.

Development of 3D Cancer Cell Models to Test Metabolic Interventions as an Anticancer Strategy

In recent years, it has become evident that the role of mitochondria in the metabolic rewiring is essential for cancer development and progression. The metabolic profile during tumorigenesis has been performed mainly in traditional 2D cell models, including cell lines of various lineages and phenotypes. Although useful in many ways, their relevance can be often debatable, as they lack the interactions between different cells of the tumour microenvironment and/or interaction with the extracellular matrix 1,2. Improved models are now being developed using 3D cell culture technology, contributing with increased physiological relevance 3,4. In this work, we improved a method for the generation of 3D models from healthy and tumour colon tissue, based on organoid technology, and performed their molecular and biochemical characterization and validation. Further, in-plate cryopreservation was applied to these models, and optimal results were obtained in terms of cell viability and functionality of the cryopreserved models. We also cryopreserved colon fibroblasts with the aim to introduce them in a co-culture cryopreserved model with organoids. This technology allows the conversion of cell models into “plug and play” formats. Therefore, cryopreservation in-plate facilitates the accessibility of specialized cell models to cell-based research and application, in cases where otherwise such specialized models would be out of reach. Finally, we briefly explored the field of bioprinting, by testing a new matrix to support the growth of colon tumour organoids, which revealed promising preliminary results. To facilitate the reader, we organized this thesis into chapters, divided by the main points of work which include development, characterization and validation of the model, commercial output, and associated applications. Each chapter has a brief introduction, followed by results and discussion and a final conclusion. The thesis has also a general discussion and conclusion section in the end, which covers the main results obtained during this work.

Altered transcriptional and epigenetic regulation affects brain cells proliferation and differentiation in the ultra-rare genetic demyelinating disease AGC1 deficiency: a study on in vitro models

AGC1 deficiency is a rare demyelinating disease caused by mutations in the SLC25A12 gene, which encodes for the mitochondrial glutamate-aspartate carrier 1 (AGC1/Alarar), highly expressed in the central nervous system. In neurons, impairment in AGC1 activity leads to reduction in N-acetyl-aspartate, the main lipid precursor for myelin synthesis (Profilo et al., 2017); in oligodendrocytes progenitors cells, AGC1 down regulation has been related to early arrest proliferation and premature differentiation (Petralla et al., 2019). Additionally, in vivo AGC1 deficiency models i.e., heterozygous mice for AGC1 knock-out and neurospheres from their subventricular zone, respectively, showed a global decrease in cells proliferation and a switch in neural stem cells (NSCs) commitment, with specific reduction in OPCs number and increase in neural and astrocytic pools (Petralla et al., 2019). Therefore, the present study aims to investigate the transcriptional and epigenetic regulation underlying the alterations observed in OPCs and NSCs biological mechanisms, in either AGC1 deficiency models of Oli-neu cells (murine immortalized oligodendrocytes precursors cells), partially silenced by a shRNA for SLC25A12 gene, and SVZ-derived neurospheres from AGC1+/- mice. Western blot and immunofluorescence analysis revealed significant variations in the expression of transcription factors involved in brain cells’ proliferation and differentiation, in association with altered histone post-translational modifications, as well as histone acetylases (HATs) and deacetylases (HDACs) activity/expression, suggesting an improper transcriptional and epigenetic regulation affecting both AGC1 deficiency in vitro models. Furthermore, given the large role of acetylation in controlling in specific time-windows OPC maturation (Hernandez and Casaccia; 2015), pharmacological HATs/HDACs inhibitions were performed, confirming the involvement of chromatin remodelling enzymes in the altered proliferation and early differentiation observed in the AGC1 deficiency models of siAGC1 Oli-neu cells and AGC1+/- mice-derived neurospheres.

Diffusive models and chaos indicators for non-linear betatron motion in circular hadron accelerators

Understanding the complex dynamics of beam-halo formation and evolution in circular particle accelerators is crucial for the design of current and future rings, particularly those utilizing superconducting magnets such as the CERN Large Hadron Collider (LHC), its luminosity upgrade HL-LHC, and the proposed Future Circular Hadron Collider (FCC-hh). A recent diffusive framework, which describes the evolution of the beam distribution by means of a Fokker-Planck equation, with diffusion coefficient derived from the Nekhoroshev theorem, has been proposed to describe the long-term behaviour of beam dynamics and particle losses. In this thesis, we discuss the theoretical foundations of this framework, and propose the implementation of an original measurement protocol based on collimator scans in view of measuring the Nekhoroshev-like diffusive coefficient by means of beam loss data. The available LHC collimator scan data, unfortunately collected without the proposed measurement protocol, have been successfully analysed using the proposed framework. This approach is also applied to datasets from detailed measurements of the impact on the beam losses of so-called long-range beam-beam compensators also at the LHC. Furthermore, dynamic indicators have been studied as a tool for exploring the phase-space properties of realistic accelerator lattices in single-particle tracking simulations. By first examining the classification performance of known and new indicators in detecting the chaotic character of initial conditions for a modulated Hénon map and then applying this knowledge to study the properties of realistic accelerator lattices, we tried to identify a connection between the presence of chaotic regions in the phase space and Nekhoroshev-like diffusive behaviour, providing new tools to the accelerator physics community.

Data surveillance for infectious diseases: models, complex networks and machine learning

In this thesis, we investigate the role of applied physics in epidemiological surveillance through the application of mathematical models, network science and machine learning. The spread of a communicable disease depends on many biological, social, and health factors. The large masses of data available make it possible, on the one hand, to monitor the evolution and spread of pathogenic organisms; on the other hand, to study the behavior of people, their opinions and habits. Presented here are three lines of research in which an attempt was made to solve real epidemiological problems through data analysis and the use of statistical and mathematical models. In Chapter 1, we applied language-inspired Deep Learning models to transform influenza protein sequences into vectors encoding their information content. We then attempted to reconstruct the antigenic properties of different viral strains using regression models and to identify the mutations responsible for vaccine escape. In Chapter 2, we constructed a compartmental model to describe the spread of a bacterium within a hospital ward. The model was informed and validated on time series of clinical measurements, and a sensitivity analysis was used to assess the impact of different control measures. Finally (Chapter 3) we reconstructed the network of retweets among COVID-19 themed Twitter users in the early months of the SARS-CoV-2 pandemic. By means of community detection algorithms and centrality measures, we characterized users’ attention shifts in the network, showing that scientific communities, initially the most retweeted, lost influence over time to national political communities. In the Conclusion, we highlighted the importance of the work done in light of the main contemporary challenges for epidemiological surveillance. In particular, we present reflections on the importance of nowcasting and forecasting, the relationship between data and scientific research, and the need to unite the different scales of epidemiological surveillance.

Quantitative susceptibility mapping as biomarker of neurodegeneration: methodological models and clinical applications

Quantitative Susceptibility Mapping (QSM) is an advanced magnetic resonance technique that can quantify in vivo biomarkers of pathology, such as alteration in iron and myelin concentration. It allows for the comparison of magnetic susceptibility properties within and between different subject groups. In this thesis, QSM acquisition and processing pipeline are discussed, together with clinical and methodological applications of QSM to neurodegeneration. In designing the studies, significant emphasis was placed on results reproducibility and interpretability. The first project focuses on the investigation of cortical regions in amyotrophic lateral sclerosis. By examining various histogram susceptibility properties, a pattern of increased iron content was revealed in patients with amyotrophic lateral sclerosis compared to controls and other neurodegenerative disorders. Moreover, there was a correlation between susceptibility and upper motor neuron impairment, particularly in patients experiencing rapid disease progression. Similarly, in the second application, QSM was used to examine cortical and sub-cortical areas in individuals with myotonic dystrophy type 1. The thalamus and brainstem were identified as structures of interest, with relevant correlations with clinical and laboratory data such as neurological evaluation and sleep records. In the third project, a robust pipeline for assessing radiomic susceptibility-based features reliability was implemented within a cohort of patients with multiple sclerosis and healthy controls. Lastly, a deep learning super-resolution model was applied to QSM images of healthy controls. The employed model demonstrated excellent generalization abilities and outperformed traditional up-sampling methods, without requiring a customized re-training. Across the three disorders investigated, it was evident that QSM is capable of distinguishing between patient groups and healthy controls while establishing correlations between imaging measurements and clinical data. These studies lay the foundation for future research, with the ultimate goal of achieving earlier and less invasive diagnoses of neurodegenerative disorders within the context of personalized medicine.