Descrizione del progetto
L’origine molecolare dell’invecchiamento
Le malattie complesse dell’uomo e l’invecchiamento iniziano con lenti cambiamenti che coinvolgono un gran numero di geni. Una medicina preventiva è pertanto difficile da attuare. Per studiare le origini delle malattie tardive, è necessaria un’analisi multidimensionale dei cambiamenti molecolari e dei parametri fisiologici durante l’invecchiamento. Il progetto SYSAGING propone di utilizzare C. elegans come modello di piccolo animale, ma in rapida crescita e che invecchia, per analizzare le origini molecolari di malattie complesse. La microscopia automatizzata e una piattaforma di elaborazione delle immagini saranno integrate con profilazione trascrittomica e biosensori in vivo per raccogliere dati a livello molecolare, cellulare, individuale e di popolazione per mappare i fattori che aumentano i rischi di malattia. Questo progetto aiuterà a identificare i potenziali obiettivi per la medicina preventiva a livello molecolare e fisiologico.
Obiettivo
A central goal of molecular medicine is to understand how genetics, diet, and environment interact to determine health. However, most complex diseases arise from slow, stochastic changes involving large numbers of genes, making it difficult to systematically develop preventative therapies. To study the early and mid-life origins of late-life diseases, we need new methods capable of measuring the high-dimensional dynamics of physiologic change during aging.
C. elegans is a small, fast-aging animal and a powerful model for asking fundamental questions about the conserved molecular origins of complex diseases. However, it is not yet feasible to systematically collect molecular and phenotypic time-series at the precision and scale needed to build quantitative dynamic models of aging. Recently, I developed an automated microscopy and image processing technology that allows life-long observation of large populations. In this proposal, we develop this prototype into an integrative platform combining transcriptomic profiling, in vivo biosensors, and new imaging technology. Collecting data at multiple spatial scales—molecules, cells, individuals, and populations—we can map the causal steps through which slow, stochastic molecular changes drive increases in disease risk. We will then apply this method at scale to characterize all known lifespan-altering interventions in C. elegans, including many being explored for clinical application.
Combining molecular genetics with theoretic approaches, we will build quantitative models of how complex diseases emerge from slow molecular-level changes, and make methodological progress toward rapid characterization of the determinants of age-associated diseases. This work will help isolate the physiologic changes whose disruption delays aging and reduces disease risk, including new targets for preventative therapies.
Campo scientifico
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensorsbiosensors
- natural sciencesbiological sciencesgenetics
- natural sciencesbiological sciencesmolecular biologymolecular genetics
- natural sciencesphysical sciencesopticsmicroscopy
- medical and health scienceshealth sciencesnutrition
Parole chiave
- Ageing
- health
- lifespan
- age-related diseases
- interventions in ageing
- Fundamental Mechanisms of Ageing
- dynamical systems
- stochastic processes
- causality
- statistical inference
- complexity theory
- microscopy
- fluorescent imaging
- longitudinal imaging
- genetics
- molecular genetics
- transcriptomics
- single-cell sequencing
- Caenorhabditis elegans
- nematodes
Programma(i)
Argomento(i)
Meccanismo di finanziamento
ERC-STG - Starting GrantIstituzione ospitante
08003 Barcelona
Spagna