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

Introduction

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.

Complexity of Myocardial Infarction

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.

Data Collection

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.

Identification of Phenotypes

We will identify intermediate phenotypes associated with the risk of MI and its associated risk factors. Using a combination of approaches including extreme phenotype and family-based approaches, we will identify variants that robustly influence these intermediate phenotypes.

Potential Drug Targets

The genes thus identified are potential drug targets that influence the risk of MI via an intermediate phenotype and are applicable across all populations. They will be validated through various approaches including:

1. Computational analysis (using Mendelian randomization and 10-year follow-up data)
2. Functional work that includes using zebrafish as an animal model

Machine Learning and Biomarker Identification

Machine learning algorithms will be used to analyze the multi-layered data to identify novel biomarkers and risk algorithms, including polygenic risk scores, for early risk prediction in the clinic.

Validation and Clinical Use

Quantitative targeted proteomic assays will be developed for further validation in other cohorts, facilitating clinical use.

Conclusion

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.