Description du projet
L’origine moléculaire du vieillissement
Les maladies humaines complexes et le vieillissement commencent par des changements lents qui impliquent un grand nombre de gènes. La médecine préventive est donc difficile à mettre en œuvre. Pour étudier les origines des maladies dégénératives, une analyse multidimensionnelle des changements moléculaires et des paramètres physiologiques au cours du vieillissement est nécessaire. SYSAGING propose d’utiliser C. elegans comme modèle d’un animal petit mais à croissance et vieillissement rapides pour analyser les origines moléculaires de maladies complexes. La microscopie automatisée et une plateforme de traitement d’images seront intégrées au profilage transcriptomique et aux biocapteurs in vivo pour recueillir des données aux niveaux moléculaire, cellulaire, individuel et de la population afin de cartographier les facteurs qui augmentent les risques de maladie. Ce projet permettra d’identifier les cibles potentielles de la médecine préventive au niveau moléculaire et physiologique.
Objectif
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.
Champ scientifique
- 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
Programme(s)
Thème(s)
Régime de financement
ERC-STG - Starting GrantInstitution d’accueil
08003 Barcelona
Espagne