logo
ResearchBunny Logo
Multiomic analyses uncover immunological signatures in acute and chronic coronary syndromes

Medicine and Health

Multiomic analyses uncover immunological signatures in acute and chronic coronary syndromes

K. Pekayvaz, C. Losert, et al.

This research delves into the hidden immune states driving acute and chronic coronary syndromes (ACS and CCS), utilizing multiomic factor analysis on longitudinal data from patients. The findings reveal critical multicellular immune signatures that are linked to disease states and treatment outcomes, marking a significant step in understanding cardiovascular disease. The investigation was performed by Kami Pekayvaz, Corinna Losert, Viktoria Knottenberg, and others.

00:00
00:00
Playback language: English
Introduction
Acute coronary syndrome (ACS) and chronic coronary syndrome (CCS) are major causes of mortality and morbidity, driven by atherosclerosis and subsequent myocardial ischemia. While inflammation plays a crucial role, the specific immunological signatures and their clinical implications remain unclear. Single-cell omics approaches offer high-resolution immune profiling, but their application to CS is limited. Multiomic factor analysis (MOFA) provides an unsupervised approach to integrate multiple data types, identifying major axes of variation and associating them with molecular processes. This study hypothesized that MOFA applied to multiomic blood data could reveal hidden immune signatures in CS linked to disease state and outcome.
Literature Review
Previous studies have investigated the role of inflammation in coronary syndromes using various approaches. Single-cell genomics has shown promise in characterizing immune cells in atherosclerotic plaques and at MI sites, but its clinical application in CS is limited. Existing research highlights the involvement of T cells and monocytes in atherogenesis and post-MI remodeling, but a comprehensive understanding of the multicellular immune programs is lacking. MOFA, as a data-driven dimensionality reduction technique, has been used successfully in other fields but not yet fully explored in CS for integrating multiomic datasets.
Methodology
This prospective study employed a longitudinal multiomics strategy with an independent validation cohort. The Munich cohort included patients with ACS (STEMI), CCS, and non-CCS controls, sampled longitudinally at four timepoints (ACS) or a single timepoint (CCS and controls). The Groningen cohort served as an independent validation set, with ACS patients sampled at three timepoints and controls at a single timepoint. Multiomic data included clinical blood tests, scRNA-seq, cytokine multiplex data, plasma proteomics, and neutrophil prime-seq (Munich cohort). ScRNA-seq and clinical data were analyzed in the Groningen cohort. MOFA integrated these various data types to identify major axes of variation, followed by pathway enrichment, cell-cell communication analysis (NicheNet), and predictive modeling. The study also included in vitro experiments using healthy PBMCs and ACS/CCS plasma to investigate the effects of IL-6 signaling and ACS plasma on monocyte phenotype and function.
Key Findings
MOFA identified two key factors: Integrative ACS Ischemia (IAI) and Integrative ACS Repair (IAR). IAI captured a large proportion of inter-patient variance and accurately discriminated ACS from CCS. It was characterized by distinct monocyte and T cell signatures, particularly involving IL-6 signaling. Inhibition of IL-6 signaling in vitro partially reversed the proinflammatory monocyte phenotype induced by ACS plasma. IAI was successfully replicated in the Groningen cohort. IAR was associated with short-term clinical outcome (left ventricular ejection fraction, LVEF), predicting good versus poor outcomes better than the GRACE score and individual clinical markers. It was characterized by reduced NK cell cytotoxicity and increased circulating anti-trypsin SERPINs. A predictive model based on IAR features showed high accuracy in both the Munich and Groningen cohorts. An additional factor, Integrative CCS (IC), identified distinct immune signatures in CCS patients compared to controls, characterized by a dysregulated activation pattern of monocytes and T cells.
Discussion
This study provides the first comprehensive multiomic characterization of systemic immune responses in ACS and CCS. The findings demonstrate the power of MOFA in uncovering complex, multicellular immune programs that are not readily apparent from analyzing individual features. The identification of IAI and IAR provides novel insights into the pathophysiology of ACS, highlighting the importance of IL-6 signaling and NK cell cytotoxicity in disease progression and outcome. The results suggest potential avenues for immune-modulatory therapies targeting IL-6 or NK cell activity. The predictive power of IAR opens possibilities for developing novel biomarkers for early risk stratification and personalized treatment strategies. Further research is needed to validate these findings in larger, more diverse cohorts and investigate the causal mechanisms linking immune signatures to clinical outcomes.
Conclusion
This study demonstrates that multiomic profiling coupled with MOFA can effectively identify systemic immune signatures associated with disease state and outcome in coronary syndromes. IAI and IAR provide novel insights into the immune mechanisms of ACS, offering potential therapeutic targets and biomarkers. Future research should focus on validating these findings in larger, independent cohorts, and exploring the mechanistic links between immune signatures and clinical outcomes. These findings could lead to the development of personalized treatment strategies and improved prognostic tools for patients with coronary syndromes.
Limitations
This study is an observational proof-of-concept study with a relatively small sample size, limiting the generalizability of the findings. The cost-intensive multiomic methods may limit widespread clinical adoption, but targeted assays based on identified biomarkers could be developed. The study focused primarily on STEMI patients, and the effect of guideline-recommended treatments on immune signatures should be further explored. Long-term clinical outcomes were not assessed in detail, which limits the assessment of sustained prognostic utility of the identified factors.
Listen, Learn & Level Up
Over 10,000 hours of research content in 25+ fields, available in 12+ languages.
No more digging through PDFs, just hit play and absorb the world's latest research in your language, on your time.
listen to research audio papers with researchbunny