Leader: Marco Bianchi (UNISA); Other collaborator(s):
We will investigate age-related mechanisms by high throughput genetic and multiomics profiling, including whole-genome sequencing, PBMC epigenomics (ATAC-seq) and transcriptomics (RNA-seq), and serum metabolomics (MS) on longitudinally-collected samples from a dedicated biobank of healthy and unhealthy agers (spoke 3). Omics data will be integrated with multilayer phenotyping to feed AI-based identification of aging trajectories and models to identify biomarkers and pathways for validation in primary samples and cell/animal models. Studies on cell/animal models will provide mechanistic insight and markers amenable to clinical validation, through a virtuous bidirectional translational approach.
Brief description of the activities and of the intermediate results: We aim to investigate age-related mechanisms by high-throughput transcriptomic (RNA-seq) profiling on longitudinally collected samples from a dedicated biobank of healthy and unhealthy agers (Spoke 3). We selected the study cohort and optimized the sequencing technology and analytic pipeline. Omics data will be integrated with multilayer phenotyping to feed AI-based identification of aging trajectories and models to identify biomarkers and pathways for validation in primary samples and cell/animal models.
Main policy/industry/practice implications:
At the moment we do not envisage policy/Industry/practice implications
Coming soon
Coming soon