Integrated whole-genome gene expression analysis reveals an atlas of dynamic immune landscapes after myocardial infarction.

INTRODUCTION

Myocardial infarction (MI) is a deadly medical condition leading to irreversible damage to the inflicted cardiac tissue. Elevated inflammatory response marks the severity of MI and is associated with the development of heart failure (HF), a long-term adverse outcome of MI. However, the efficacy of anti-inflammatory therapies for MI remains controversial. Deciphering the dynamic transcriptional signatures in peripheral blood mononuclear cells (PBMCs) is a viable and translatable route to better understand post-MI inflammation, which may help guide post-MI anti-inflammatory treatments.

METHODS

In this work, integrated whole-genome gene expression analysis was performed to explore dynamic immune landscapes associated with MI.

RESULTS

GSEA and GSVA showed that pathways involved in the inflammatory response and metabolic reprogramming were significantly enriched in PBMCs from MI patients. Based on leukocyte profiles generated by xCell algorithm, the relative abundance of monocytes and neutrophils was significantly increased in PBMCs from MI patients and had positive correlations with typical inflammation-associated transcripts. Mfuzz clustering revealed temporal gene expression profiles of PBMCs during the 6-month post-MI follow-up. Analysis of DEGs and gene sets indicated that PBMCs from HF group were characterized by elevated and lasting expression of genes implicated in inflammation and coagulation. Consensus clustering generated 4 metabolic subtypes of PBMCs with molecular heterogeneity in HF patients.

DISCUSSION

In summary, integrated whole-genome gene expression analysis here outlines a transcriptomic framework that may improve the understanding of dynamic signatures present in PBMCs, as well as the heterogeneity of PBMCs in MI patients with or without long-term clinical outcome of HF. Moreover, the work here uncovers the diversity and heterogeneity of PBMCs from HF patients, providing novel bioinformatic evidence supporting the mechanistic implications of metabolic reprogramming and mitochondrial dysfunction in the post-MI inflammation and HF. Therefore, our work here supports the notion that individualized anti-inflammatory therapies are needed to improve the clinical management of post-MI patients.