A Multi-Omics Approach for Novel Drug Targets, Biomarkers and Risk Algorithms for Myocardial Infarction

Project Information

TargetMI

Grant agreement ID: 101114924

DOI

10.3030/101114924

EC signature date 21 June 2023

Start date 1 October 2023

End date 30 September 2028

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

Periodic Reporting for period 2 - TargetMI (A Multi-Omics Approach for Novel Drug Targets, Biomarkers and Risk Algorithms for Myocardial Infarction)

Reporting period: 2024-10-01 to 2026-01-31

Summary of the context and overall objectives of the project

With almost 18 million deaths per year, and numbers expected to increase, cardiovascular disease is the leading cause of death globally. The most common type of cardiovascular disease results in blood clots in arteries, which obstructs blood flow causing myocardial infarction (MI, commonly referred to as heart attack) when this occurs in the coronary arteries, or stroke when it occurs in the brain. Despite research on the genetic, lifestyle, and environmental factors associated with MI, practical applications to prevent it remain limited. Recent multi-omic advancements have not yet been successfully employed to resolve or greatly improve this global problem with important medical costs and morbidity. In TargetMI, we are developing a high throughput approach to systematically analyse an unprecedented volume of multi-omic data (whole genome, transcriptomic, metabolomic and proteomic data) to identify novel drug targets, risk algorithms and biomarkers for atherosclerosis (the process leading to MI and stroke), MI and their risk factors, besides gaining new knowledge and understanding of these conditions. We are using the latest technologies to measure levels of thousands of molecules in blood from over 1000 individuals collected in a previous study (the MAMI or Maltese Acute Myocardial Infarction Study). These levels are being extensively analysed computationally using bioinformatics to compare the differences between those who had a heart attack and those who did not. These findings together with genetic factors contributing to these differences are being assessed for their utility in identifying those at increased risk for MI in a relatively short timeframe. An approach of using this extensive data to identify robust drug targets is also being developed and validated. This will greatly accelerate the process for identifying and developing new medicines that could help to prevent heart attacks, as well as other diseases. Clinical applications of these findings will help early detection of those at risk allowing time for preventive strategies.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

We have added proteomics data and conducted analysis on it, gene-environmental interactions, additional GWAS analysis, work on case stratification and exploration of the methods used for that, improved batch effect correction of RNASeq data. We digitised information from angiograms and also family trees with medical conditions. We collated data from multiple sources (medical history, questionnaire data, tests done in hospital, tests done by us, 10 year follow up data and use of medications) to get more accurate data. We built an MI timeline for all the research participants in the MAMI Study so we better map events such as onset of 1st MI, recurrent MI, death from cardiovascular events (CVE), occurrences of cardiovascular events. We conducted analysis of the 10 year follow up data identifying people who developed 1st MI or CVE, diabetes, hypertension and hypercholesterolaemia after we collected their blood, together with time from blood collection, enabling us to study results in a prospective manner avoiding issues of reverse causation. We started work on risk algorithm development including validating the SCORE2 in the Maltese population, and developing and assessing polygenic risk scores from the whole genome sequencing data (Objective 4). We identified high risk groups. We have updated annotations of our RNA-Seq and WGS data. We further developed computational pipelines for transcriptomic and proteomic analysis and conducted QC and data analysis for untargeted proteomics. We are conducting differential expression analysis. Together with the portfolio partners and EU programme manager we have developed a strategic plan for the portfolio work and have started this work. We have automated the TargetID strategy to analyse the multilayered data and identify better candidate drug targets in a high throughput manner and added improvements to it. We are now well poised to finalise these analyses and move to the next stages of the project.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

The TargetID Strategy was developed further. Data from a 10 year follow up study was analysed. We are identifying biomarkers at RNA and protein level (still in progress). A multiplexed assay will be developed for the protein biomarkers and the results will be validated in a Dutch collection. Innovation which has progressed considerably includes computational pipelines, the team's expertise, the enhanced data, the TargetID strategy and biomarkers. We have several advisors mentoring us on the exploitation plan and regulatory aspects.