Descrizione del progetto
La multiomica fa progredire la previsione del rischio di infarto cardiaco
Le malattie cardiovascolari (MCV) comprendono diverse patologie che interessano il cuore e i vasi sanguigni e costituiscono un grave problema per la salute. Nell’infarto del miocardio (IM) un’improvvisa interruzione del flusso sanguigno al cuore provoca danni al tessuto cardiaco o la morte. Nonostante la ricerca sui fattori genetici, di stile di vita e ambientali associati all’infarto del miocardio, le applicazioni pratiche per prevenirlo rimangono limitate. I ricercatori dell’Università di Malta e del Centro Medico Universitario di Leida collaboreranno al progetto TargetMI, finanziato dal Consiglio europeo per l’innovazione. Il consorzio intraprenderà un’analisi multiomica ad alto rendimento su campioni e dati provenienti dallo studio MAMI (Maltese Acute Myocardial Infarction), compresi dati genomici, metabolomici, proteomici e relativi all’RNA, per scoprire nuovi bersagli farmacologici e biomarcatori per l’infarto del miocardio e sviluppare nuove strategie di valutazione del rischio.
Obiettivo
In TargetMI we propose a high throughput multi-omic approach for rapid discovery of novel drug targets, biomarkers and risk algorithms, applied here to atherosclerosis, myocardial infarction (MI) and their risk factors. Cardiovascular disease is a major cause of death and morbidity worldwide. The causes of MI are highly complex involving genetic, lifestyle and environmental factors. Whilst much research effort has been invested in attempting to decipher these factors, clinical applications of findings are disappointingly few. We will harness four -omic datasets (whole genome, transcriptomic, metabolomic and proteomic data) on 1000 highly phenotyped samples of the Maltese Acute Myocardial Infarction (MAMI) Study. These were collected from cases, controls and relatives of cases (including 80 families) with meticulous attention to preanalytical variables. We will identify intermediate phenotypes associated with risk of MI and its associated risk factors. Using a combination of approaches including extreme phenotype and family-based approaches we will identify variants which robustly influence these intermediate phenotypes. The genes thus identified are potential drug targets that influence risk of MI via an intermediate phenotype and are applicable across all populations. They will be validated through various approaches including computational analysis, (using Mendelian randomisation and 10 year follow-up data), and functional work that includes using zebrafish as an animal model. Machine learning algorithms will be used to analyse the multi-layered data to identify novel biomarkers and risk algorithms, including polygenic risk scores, for early risk prediction in the clinic. Quantitative targeted proteomic assays will be developed for further validation in other cohorts facilitating clinical use. Besides the increase in knowledge on the molecular etiology of MI, this powerful integrated strategy will bring rapid clinical translation of unprecedented multi-omic data.
Campo scientifico (EuroSciVoc)
CORDIS classifica i progetti con EuroSciVoc, una tassonomia multilingue dei campi scientifici, attraverso un processo semi-automatico basato su tecniche NLP.
CORDIS classifica i progetti con EuroSciVoc, una tassonomia multilingue dei campi scientifici, attraverso un processo semi-automatico basato su tecniche NLP.
- scienze mediche e della salutemedicina clinicacardiologiamalattie cardiovascolariarteriosclerosi
- scienze naturaliscienze biologichegeneticagenomi
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Parole chiave
Programma(i)
- HORIZON.3.1 - The European Innovation Council (EIC) Main Programme
Invito a presentare proposte
HORIZON-EIC-2022-PATHFINDERCHALLENGES-01
Vedi altri progetti per questo bandoMeccanismo di finanziamento
HORIZON-EIC - HORIZON EIC GrantsCoordinatore
2080 L-Imsida
Malta