📋 Tender Overview
A Multi-Omics Approach For Novel Drug Targets, Biomarkers And Risk Algorithms For Myocardial Infarction
Multiomics Advances Heart Attack Risk Predictioncardiovascular Diseases (Cvd) Encompass Various Conditions Affecting The Heart And Blood Vessels And Constitute A Major Health Concern. In Myocardial Infarction (Mi) A Sudden Disruption In Blood Flow To The Heart Leads To Cardiac Tissue Damage Or Death. Despite Research On The Genetic, Lifestyle, And Environmental Factors Associated With Mi, Practical Applications To Prevent It Remain Limited. Researchers From The University Of Malta And The Leiden University Medical Centre Will Collaborate On The Targetmi Project, Funded By The European Innovation Council. The Consortium Will Undertake A High Throughput Multiomics Analysis On Samples And Data From The Maltese Acute Myocardial Infarction (Mami) Study Including Genomic, Metabolomic, Proteomic, And Rna Data, To Discover New Drug Targets And Biomarkers For Mi And Develop Novel Risk Assessment Strategies.
Funded Under
The European Innovation Council (Eic)
Total Cost
€ 3 999 840,00
Eu Contribution
€ 3 999 840,00
Coordinated By
Universita Ta Malta
Malta
Objective
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 facilitat
