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

Reporting period: 2023-10-01 to 2024-09-30

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

New -omic data including proteomic data will be added to the genomic and transcriptomic datasets already available. The specifications for this testing in the 1,000 samples of the MAMI Study have been defined and a trial run on some samples was successful. We have also extracted further -omic data from data already available such as structural and copy number variants from the whole genome sequencing data and these have been merged into one vcf file. Genomic data is being analysed using both a conventional GWAS approach and other approaches that are being tested. Levels of RNA from 20,000 molecules in blood have been compared in cases and controls to identify differences. This has also been done using four different approaches, three of which are described in the scientific literature, with another approach being developed and tested in-house. The differentially expressed genes are being analysed for their usefulness at predicting future risk of MI using a machine learning approach, as well as data from a 10-year follow up health study of the research participants of the MAMI Study. The effect of genetic variants on gene expression has been analysed using standard cis eQTL analysis. Trans eQTL analysis has also been conducted. Bimodally expressed genes were also identified as these may influence results from some analyses. All these analyses will be useful for drug target identification and biomarker development. A high throughput strategy has been set up for rapid identification of genetic variants that can highlight candidate drug targets. The computational pipeline of this is being automated. Work on risk algorithm analysis has started for now using conventional risk factors. Some preliminary work on subgroup analysis of cases has also started but is in very early stages.

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)

Differential expression of RNA levels is being tested for its application to identify people at risk for getting a heart attack in the future. This is being evaluated on a test set as well as on MAMI Study participants who developed an MI in a 10 year follow-up study. If this is shown to be useful, it can be used as a biomarker to identify those most at risk of getting a heart attack in a relatively short timeframe. Both conventional approaches and novel approaches have been used to identify differential expression of RNA as well as associations between genotypes and phenotype. The resulting algorithms are being tested. It is expected that further work will be done to improve prediction. These approaches can also be adopted for effects of risk factors of MI such as smoking, diabetes, hypertension and hypercholesterolaemia as well as obesity. This will increase our understanding of these conditions as well as highlight deranged molecules and pathways which can be useful information even for drug targets. The differentially expressed genes from these analyses in turn serve as input for our novel high throughput strategy which facilitates rapid combing of the large multi-omic data to identify potential drug targets with strong biological evidence of utility. The strategy is in place and is being validated and improved upon by the addition of results from eQTL and GWAS analysis (on both SNP and structural variants). The computational pipeline requires some further work to be fully automated. Once this is fully optimised and validated, it can be used to help identify candidate drug targets for myocardial infarction (MI)) and its risk factors. The best drug targets will then be selected for further research. The data, results and computational pipelines from structural and copy number variant analysis, eQTL and GWAS analysis are useful outcomes which can be exploited further.