G9a Inhibition Promotes Neuroprotection through GMFB Regulation in Alzheimer’s Disease

Epigenetic alterations are a fundamental pathological hallmark of Alzheimer’s disease (AD). Herein, we show the upregulation of G9a and H3K9me2 in the brains of AD patients. Interestingly, treatment with a G9a inhibitor (G9ai) in SAMP8 mice reversed the high levels of H3K9me2 and rescued cognitive decline. A transcriptional profile analysis after G9ai treatment revealed increased gene expression of glia maturation factor β (GMFB) in SAMP8 mice. Besides, a H3K9me2 ChIP-seq analysis after G9a inhibition treatment showed the enrichment of gene promoters associated with neural functions. We observed the induction of neuronal plasticity and a reduction of neuroinflammation after G9ai treatment, and more strikingly, these neuroprotective effects were reverted by the pharmacological inhibition of GMFB in mice and cell cultures; this was also validated by the RNAi approach generating the knockdown of GMFB/Y507A.10 in Caenorhabditis elegans. Importantly, we present evidence that GMFB activity is controlled by G9a-mediated lysine methylation as well as we identified that G9a directly bound GMFB and catalyzed the methylation at lysine (K) 20 and K25 in vitro. Furthermore, we found that the neurodegenerative role of G9a as a GMFB suppressor would mainly rely on methylation of the K25 position of GMFB, and thus G9a pharmacological inhibition removes this methylation promoting neuroprotective effects. Then, our findings confirm an undescribed mechanism by which G9a inhibition acts at two levels, increasing GMFB and regulating its function to promote neuroprotective effects in age-related cognitive decline</p

Understanding the metabolic signatures of haematological cancers through an integrative multi-Omics approach

Haematological cancers are heterogenous diseases caused by a series of events which drive cells to uncontrolled proliferation and tumour progression. Nowadays, our understanding is that one hallmark of cancer cells is to reprogram their normal cellular metabolism to sustain their anabolic requirements for continuous cell growth and proliferation. Despite the remarkable progress in cancer metabolism, the exact mechanisms behind cancer metabolic reprogramming are not yet fully understood. The work presented in this thesis aims to provide novel biological insights into the metabolic reprogramming of haematological cancers and highlight potential metabolic vulnerabilities for therapeutic targeting approached to be investigated in future studies. A multi-Omics data integration approach was selected to achieve such ambitious aims. Herein, recent computational methodologies were applied to integrate and analyse transcriptomic with metabolomic profiles derived from cancer patients, as well as cell lines, mostly from mature B-cell neoplasms. Mature B-cell neoplasms, such as Chronic Lymphocytic Leukaemia (CLL) and Non-Hodgkin Lymphomas (NHL), rise from the clonal expansion of mature B-cells and they are responsible for most newly diagnosed cases of haematological cancers worldwide. The second chapter of this thesis presents an investigation into the transcriptome profile of CLL patients characterised by a distinct clinical response. Deregulated metabolic genes and pathways were identified between rare CLL cases that have undergone spontaneous regression versus CLL cases with poor clinical outcome. CLL cells from cases with poor outcome presented a differential reliance on oxidative phosphorylation and mitochondrial respiration compared to spontaneous regressed CLL cells. Going beyond traditional gene expression analysis, we performed an integration of transcriptomics profiles with Genome Scale Metabolic Models to identify metabolic genes as potential vulnerabilities in CLL. Our findings emphasise the important role of metabolic reprogramming in CLL and suggest the possibility of targeting metabolism for future studies and therapeutic approaches. The third chapter of this thesis describes a study exploring cancer metabolism in aggressive NHL associated with germinal centre development, focusing on endemic Burkitt Lymphoma (BL) and the germinal-centre–like subtype Diffuse Large B-cell Lymphomas (DLBCL). Analysis of the transcriptome of primary tumours revealed that BL cases possessed a distinct gene expression profile compared to DLBCL cases. This BL profile is suggestive of altered function of metabolism with elevated expression in serine metabolic genes, the c-Myc and mTORC1 pathways. On the opposite, DLBCL cases appeared to be dependent on extracellular signals from cytokines (INFγ response) or inflammation, possibly to trigger activation of intracellular signalling pathways that impact metabolism. Furthermore, integrative analysis at the pathway level between transcriptomic and metabolomic datasets from cell lines, indicated a dependency of BL cells on non-essential amino acid metabolism and particularly on the alanine, aspartate and glutamine metabolic pathways. These results not only highlighted key metabolic regulators in NHL, but most importantly, demonstrated the necessity of understanding and monitoring metabolic properties in these lymphomas. Finally, chapter four describes work undertaken to explore the transcriptomic and metabolic diversity of cancer cell lines. Machine learning approaches were applied to integrate and analyse Omics datasets retrieved from the Cancer Cell Line Encyclopaedia (CCLE) database. Unsupervised analysis highlighted the distinct transcriptomic and metabolomic profile of haematopoietic cell lines compared to other tumours. Taking a supervised approach enabled us to associate gene expression changes in cytoskeleton and cell adhesion molecules with aberrant metabolites levels, such as xanthine and creatinine. Together, these observations provide proof of concept for the highly dynamic variations between transcriptome and metabolome in different cancers. In summary, this work portrays the power of multi-Omics data integration to unveil key elements in metabolic reprogramming of haematological cancers and raises numerous questions and new hypotheses for future metabolic studies