A distinct T-cell receptor signature associates with cardiac outcome in myocardial infarction patients

Abstract Myocardial infarction (MI) is associated with an inflammatory process mainly attributed to innate immune components. Very recently, the role of T-cells in both inflammation and healing has been suggested through various human and mouse studies. Previous studies showed that CD4+ and CD8+ T cells affect post-MI repair but did not investigate how to leverage T cell biology to predict post-MI outcomes in patients. For instance, the antigenic trigger of T-cells is still unknown in human. Indeed, others and we identified T-cell specific for myosin infiltrating the myocardium in mouse models of MI, recent studies identified expanded clones in human myocardium, altogether suggesting a tissue-specific T-cell activation. However, it is still unclear how acute post-MI immune responses shape long-term cardiac functional outcomes in individual patients. In this study, we analyzed the role of T-cell in predicting post-MI repair by analyzing the T-cell receptor (TCR) repertoire. Indeed, the TCR repertoire is now considered as a marker of the clinical status of individuals. Previous studies in infectious but also autoimmune contexts showed the potential of the TCR repertoire to predict the disease. Therefore, assessing the dynamic changes in global TCR repertoires may provide valuable information about the antigen-specific immune responses underlying post-MI healing. In our study, we carefully selected patients that suffered from MI on a prospective cohort. The TCR repertoire has been analyzed by next generation sequencing at the index hospitalization with the aim to identify features predict of their healing outcome assessed at 12 months post-MI. While no major variations have been found in diversity of TCR gene usage, we identified unique TCR signatures predicting one-year cardiac functional outcomes. Our result enables early immune-based risk stratification of MI patients and calls for larger studies to develop novel predictive biomarkers and possibly new therapeutics.