There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Lucas Farina Lima |
| Institution: | University of São Paulo |
| Department: | Institute of Mathematics and Statistic |
| Country: | |
| Proposed Analysis: | Alzheimer's disease (AD) is a neurodegenerative disorder that is characterized by the accumulation of protein aggregates in the brain, leading to cognitive impairment and memory loss. Despite extensive research efforts, the etiology and molecular mechanisms underlying AD remain poorly understood. Recent studies have suggested that mobile genetic elements, such as transposable elements (TEs), may play a role in the development and progression of AD. Therefore, the use of genomic datasets may provide a valuable resource for investigating the involvement of TEs in AD pathogenesis.TEs move from one genomic location to another, and can contribute to genomic diversity and evolution. However, dysregulation of TEs has been implicated in a variety of diseases, including cancer and neurological disorders. Recent studies have shown that TEs could contribute to AD pathology, and can contribute to the accumulation of beta-amyloid plaques and neurofibrillary tangles. TEs have been shown to activate inflammatory pathways and to induce cellular stress responses, further contributing to neurodegeneration. By comparing the genomic profiles of AD patients and healthy controls, it is possible to identify dysregulated TEs and to investigate their potential role in AD pathology. We propose a systematic analysis using co-expression gene networks of TEs in the different stages of disease progression and its impact on gene expression and regulation in AD. Furthermore, we will use machine learning algorithms to classify the patients in more precise groups and can help to identify key TE-associated pathways and targets for therapeutic intervention. Others data will be used to compare difference profiles across others population and diseases, mainly HIV and parkinson disease. |
| Additional Investigators |
