The dual role of BCAT1 in glioblastoma: a metabolic enzyme with a twist

Glioblastoma is the most common central nervous system malignancy in adults with a very poor outcome due to its invasiveness, intratumoral heterogeneity, poorly differentiated features, and immunosuppressive microenvironment. Previous work suggested that Branched-chain amino acid transaminase 1 (BCAT1) is often highly expressed in glioblastoma and multiple modes of action for its oncogenic potential have been proposed. In this thesis, I focus on investigating a novel role of BCAT1 in maintaining mitotic fidelity, and how it impacts the cellular plasticity of glioblastoma cells and the tumor immune microenvironment. We have found that BCAT1 localizes to the key mitotic structures during cell division as well as in the nucleus during interphase. Using co-immunoprecipitation mass spectrometry, I showed that BCAT1 associates with many components of the mitotic spindle and the kinetochore during mitosis. Through proteomic and phosphoproteomic analysis I showed that the central kinases of the spindle assembly checkpoint, TTK and AURKB, showed significantly decreased activity during mitosis upon BCAT1-KO. By analyzing the expression patterns of human and mouse glioblastoma cells and tumor samples of the TCGA-GBM cohort, I found that BCAT1 expression is strongly correlated with the cellular state of glioblastoma, with high expression being indicative of a mesenchymal phenotype and low or no expression with a neuronal cellular state. I further confirmed these observations through a series of differentiation experiments of murine glioblastoma stem cells where the Bcat1-KO showed a much higher tendency towards differentiation and lacked the plasticity of the control cells. Consistently, in vivo findings corroborated these results with a complete lack of tumor outgrowth of the Bcat1-KO cells in immunocompetent mice, and a significant growth delay in immunodeficient mice. Lastly, I explored the impact of tumor BCAT1 expression on the immune microenvironment. I found that low BCAT1 expression was associated with a higher immune infiltration of both myeloid and T-cells in human tumor samples. These findings were additionally confirmed in in vivo experiments in immunocompetent mice. Furthermore, Bcat1-KO tumors did develop in the immunodeficient NSG and Rag2KO mouse models, highlighting the importance of the immune compartment in completely abrogating their growth. In conclusion, the data presented here confirm the novel role of BCAT1 in maintaining mitotic fidelity of glioblastoma cells. Furthermore, it shows that BCAT1 expression is necessary for maintaining the plasticity of glioblastoma cells and an immunosuppressive tumor microenvironment

Cancer proteogenomics : connecting genotype to molecular phenotype

The central dogma of molecular biology describes the one-way road from DNA to RNA and finally to protein. Yet, how this flow of information encoded in DNA as genes (genotype) is regulated in order to produce the observable traits of an individual (phenotype) remains unanswered. Recent advances in high-throughput data, i.e., ‘omics’, have allowed the quantification of DNA, RNA and protein levels leading to integrative analyses that essentially probe the central dogma along all of its constituent molecules. Evidence from these analyses suggest that mRNA abundances are at best a moderate proxy for proteins which are the main functional units of cells and thus closer to the phenotype. Cancer proteogenomic studies consider the ensemble of proteins, the so-called proteome, as the readout of the functional molecular phenotype to investigate its influence by upstream events, for example DNA copy number alterations. In typical proteogenomic studies, however, the identified proteome is a simplification of its actual composition, as they methodologically disregard events such as splicing, proteolytic cleavage and post-translational modifications that generate unique protein species – proteoforms. The scope of this thesis is to study the proteome diversity in terms of: a) the complex genetic background of three tumor types, i.e. breast cancer, childhood acute lymphoblastic leukemia and lung cancer, and b) the proteoform composition, describing a computational method for detecting protein species based on their distinct quantitative profiles. In Paper I, we present a proteogenomic landscape of 45 breast cancer samples representative of the five PAM50 intrinsic subtypes. We studied the effect of copy number alterations (CNA) on mRNA and protein levels, overlaying a public dataset of drug- perturbed protein degradation. In Paper II, we describe a proteogenomic analysis of 27 B-cell precursor acute lymphoblastic leukemia clinical samples that compares high hyperdiploid versus ETV6/RUNX1-positive cases. We examined the impact of the amplified chromosomes on mRNA and protein abundance, specifically the linear trend between the amplification level and the dosage effect. Moreover, we investigated mRNA-protein quantitative discrepancies with regard to post-transcriptional and post-translational effects such as mRNA/protein stability and miRNA targeting. In Paper III, we describe a proteogenomic cohort of 141 non-small cell lung cancer clinical samples. We used clustering methods to identify six distinct proteome-based subtypes. We integrated the protein abundances in pathways using protein-protein correlation networks, bioinformatically deconvoluted the immune composition and characterized the neoantigen burden. In Paper IV, we developed a pipeline for proteoform detection from bottom-up mass- spectrometry-based proteomics. Using an in-depth proteomics dataset of 18 cancer cell lines, we identified proteoforms related to splice variant peptides supported by RNA-seq data. This thesis adds on the previous literature of proteogenomic studies by analyzing the tumor proteome and its regulation along the flow of the central dogma of molecular biology. It is anticipated that some of these findings would lead to novel insights about tumor biology and set the stage for clinical applications to improve the current cancer patient care

Systems Modeling to Predict Mechano-Chemo Interactions In Cardiac Fibrosis

Cardiac fibrosis poses a central challenge in preventing heart failure for patients who have suffered a cardiac injury such as myocardial infarction or aortic valve stenosis. This chronic condition is characterized by a reduction in contractile function through combined hypertrophy and excessive scar formation, and although currently prescribed therapeutics targeting hypertrophy have shown improvements in patient outcomes, pathological fibrosis remains a leading cause of reduced cardiac function for patients long-term. Cardiac fibroblasts play a key role in regulating scar formation during heart failure progression, and interacting biochemical and biomechanical cues within the myocardium guide the activation of fibroblasts and expression of extracellular matrix proteins. While targeted experimental studies of fibroblast activation have elucidated the roles of individual pathways in fibroblast activation, intracellular crosstalk between mechanotransduction and chemotransduction pathways from multiple biochemical cues has largely confounded efforts to control overall cell behavior within the myocardial environment. Computational networks of intracellular signaling can account for complex interactions between signaling pathways and provide a promising approach for identifying influential mechanisms mediating cell behavior. The overarching goal of this dissertation is to improve our understanding of complex signaling in fibroblasts by investigating the role of mechano-chemo interactions in cardiac fibroblast-mediated fibrosis using a combination of experimental studies and systems-level computational models. Firstly, using an in vitro screen of cardiac fibroblast-secreted proteins in response to combinations of biochemical stimuli and mechanical tension, we found that tension modulated cell sensitivity towards biochemical stimuli, thereby altering cell behavior based on the mechanical context. Secondly, using a curated model of fibroblast intracellular signaling, we expanded model topology to include robust mechanotransduction pathways, improved accuracy of model predictions compared to independent experimental studies, and identified mechanically dependent mechanisms-of- ction and mechano-adaptive drug candidates in a post-infarction scenario. Lastly, using an inferred network of fibroblast transcriptional regulation and model fitting to patient-specific data, we showed the utility of model-based approaches in identifying influential pathways underlying fibrotic protein expression during aortic valve stenosis and predicting patient-specific responses to pharmacological intervention. Our work suggests that computational-based approaches can generate insight into influential mechanisms among complex systems, and such tools may be promising for further therapeutic development and precision medicine

Optimization of logical networks for the modelling of cancer signalling pathways

Cancer is one of the main causes of death throughout the world. The survival of patients diagnosed with various cancer types remains low despite the numerous progresses of the last decades. Some of the reasons for this unmet clinical need are the high heterogeneity between patients, the differentiation of cancer cells within a single tumor, the persistence of cancer stem cells, and the high number of possible clinical phenotypes arising from the combination of the genetic and epigenetic insults that confer to cells the functional characteristics enabling them to proliferate, evade the immune system and programmed cell death, and give rise to neoplasms. To identify new therapeutic options, a better understanding of the mechanisms that generate and maintain these functional characteristics is needed. As many of the alterations that characterize cancerous lesions relate to the signaling pathways that ensure the adequacy of cellular behavior in a specific micro-environment and in response to molecular cues, it is likely that increased knowledge about these signaling pathways will result in the identification of new pharmacological targets towards which new drugs can be designed. As such, the modeling of the cellular regulatory networks can play a prominent role in this understanding, as computational modeling allows the integration of large quantities of data and the simulation of large systems. Logical modeling is well adapted to the large-scale modeling of regulatory networks. Different types of logical network modeling have been used successfully to study cancer signaling pathways and investigate specific hypotheses. In this work we propose a Dynamic Bayesian Network framework to contextualize network models of signaling pathways. We implemented FALCON, a Matlab toolbox to formulate the parametrization of a prior-knowledge interaction network given a set of biological measurements under different experimental conditions. The FALCON toolbox allows a systems-level analysis of the model with the aim of identifying the most sensitive nodes and interactions of the inferred regulatory network and point to possible ways to modify its functional properties. The resulting hypotheses can be tested in the form of virtual knock-out experiments. We also propose a series of regularization schemes, materializing biological assumptions, to incorporate relevant research questions in the optimization procedure. These questions include the detection of the active signaling pathways in a specific context, the identification of the most important differences within a group of cell lines, or the time-frame of network rewiring. We used the toolbox and its extensions on a series of toy models and biological examples. We showed that our pipeline is able to identify cell type-specific parameters that are predictive of drug sensitivity, using a regularization scheme based on local parameter densities in the parameter space. We applied FALCON to the analysis of the resistance mechanism in A375 melanoma cells adapted to low doses of a TNFR agonist, and we accurately predict the re-sensitization and successful induction of apoptosis in the adapted cells via the silencing of XIAP and the down-regulation of NFkB. We further point to specific drug combinations that could be applied in the clinics. Overall, we demonstrate that our approach is able to identify the most relevant changes between sensitive and resistant cancer clones

The role of the small G-protein R-Ras in vascular function and blood pressure control

Elevated blood pressure, termed hypertension, is a major risk factor for cardiovascular mortality. Despite decades of research the pathogenesis of hypertension remains unclear. Genome-wide association studies have identified genes contributing to the polygenic nature of blood pressure. A meta-analysis of Exome chip genotypes from >300,000 individuals identified a rare missense single nucleotide variant, rs61760904, associated with a large effect on systolic blood pressure, located in an exon in the small GTPase R-Ras. R-Ras is highly expressed in the vasculature and is implicated in cardiovascular processes. This thesis investigated the potential role of R-Ras in blood pressure control. The CRISPR-Cas9 RrasDEL-415 knockout mouse model was specifically generated for this project. The blood pressure phenotype of these mice was characterised using radiotelemetry. Young R-Ras knockout mice exhibited blood pressure comparable to wild-type littermates, at baseline and with angiotensin II-induced hypertension. However, systolic blood pressure was significantly elevated in the aged R-Ras knockout mouse. This age-related phenotype in the R-Ras knockout mouse was explored in humans using an age-stratified association analysis of the RRAS variant in ~500,000 individuals from the UK Biobank. Individuals homozygous for the rare variant aged ≥50 years had higher mean systolic blood pressure (+14.6 mmHg) compared to individuals <50 years (+9.7 mmHg). This motivated the hypothesis that compensatory mechanisms were masking the impact of R-Ras on blood pressure control in vivo in the young mouse. Phosphoproteomic analysis of the young mouse aorta revealed significant downregulation of pathways related to blood pressure control with R-Ras knockout – notably, ‘cAMP signaling’ and ‘Vascular smooth muscle contraction’. The work presented in this PhD thesis supports a role of R-Ras in vascular mechanisms of blood pressure control, and in the age-related pathogenesis of hypertension in humans. Future work, notably repeating phosphoproteomics of the aged R-Ras knockout mouse aorta, is essential to delineate the exact mechanisms responsible

Network-based methods for biological data integration in precision medicine

[eng] The vast and continuously increasing volume of available biomedical data produced during the last decades opens new opportunities for large-scale modeling of disease biology, facilitating a more comprehensive and integrative understanding of its processes. Nevertheless, this type of modelling requires highly efficient computational systems capable of dealing with such levels of data volumes. Computational approximations commonly used in machine learning and data analysis, namely dimensionality reduction and network-based approaches, have been developed with the goal of effectively integrating biomedical data. Among these methods, network-based machine learning stands out due to its major advantage in terms of biomedical interpretability. These methodologies provide a highly intuitive framework for the integration and modelling of biological processes. This PhD thesis aims to explore the potential of integration of complementary available biomedical knowledge with patient-specific data to provide novel computational approaches to solve biomedical scenarios characterized by data scarcity. The primary focus is on studying how high-order graph analysis (i.e., community detection in multiplex and multilayer networks) may help elucidate the interplay of different types of data in contexts where statistical power is heavily impacted by small sample sizes, such as rare diseases and precision oncology. The central focus of this thesis is to illustrate how network biology, among the several data integration approaches with the potential to achieve this task, can play a pivotal role in addressing this challenge provided its advantages in molecular interpretability. Through its insights and methodologies, it introduces how network biology, and in particular, models based on multilayer networks, facilitates bringing the vision of precision medicine to these complex scenarios, providing a natural approach for the discovery of new biomedical relationships that overcomes the difficulties for the study of cohorts presenting limited sample sizes (data-scarce scenarios). Delving into the potential of current artificial intelligence (AI) and network biology applications to address data granularity issues in the precision medicine field, this PhD thesis presents pivotal research works, based on multilayer networks, for the analysis of two rare disease scenarios with specific data granularities, effectively overcoming the classical constraints hindering rare disease and precision oncology research. The first research article presents a personalized medicine study of the molecular determinants of severity in congenital myasthenic syndromes (CMS), a group of rare disorders of the neuromuscular junction (NMJ). The analysis of severity in rare diseases, despite its importance, is typically neglected due to data availability. In this study, modelling of biomedical knowledge via multilayer networks allowed understanding the functional implications of individual mutations in the cohort under study, as well as their relationships with the causal mutations of the disease and the different levels of severity observed. Moreover, the study presents experimental evidence of the role of a previously unsuspected gene in NMJ activity, validating the hypothetical role predicted using the newly introduced methodologies. The second research article focuses on the applicability of multilayer networks for gene priorization. Enhancing concepts for the analysis of different data granularities firstly introduced in the previous article, the presented research provides a methodology based on the persistency of network community structures in a range of modularity resolution, effectively providing a new framework for gene priorization for patient stratification. In summary, this PhD thesis presents major advances on the use of multilayer network-based approaches for the application of precision medicine to data-scarce scenarios, exploring the potential of integrating extensive available biomedical knowledge with patient-specific data

SYSTEM INVESTIGATION OF LY303511-INDUCED SENSITIZATION OF HELA CELLS TO TRAIL-INDUCED APOPTOSIS: ROLE OF HYDROGEN PEROXIDE, PEROXYNITRITE, NITRIC OXIDE AND CALCIUM

Ph.DDOCTOR OF PHILOSOPH