Restricted T-Cell Repertoire in the Epicardial Adipose Tissue of Non-ST Segment Elevation Myocardial Infarction Patients

Abstract

Aims: Human epicardial adipose tissue, a dynamic source of multiple bioactive factors, holds a close functional and anatomic relationship with the epicardial coronary arteries and communicates with the coronary artery wall through paracrine and vasocrine secretions. We explored the hypothesis that T-cell recruitment into epicardial adipose tissue (EAT) in patients with non-ST segment elevation myocardial infarction (NSTEMI) could be part of a specific antigen-driven response implicated in acute coronary syndrome onset and progression.

Methods and results: We enrolled 32 NSTEMI patients and 34 chronic coronary syndrome (CCS) patients undergoing coronary artery bypass grafting (CABG) and 12 mitral valve disease (MVD) patients undergoing surgery. We performed EAT proteome profiling on pooled specimens from three NSTEMI and three CCS patients. We performed T-cell receptor (TCR) spectratyping and CDR3 sequencing in EAT and peripheral blood mononuclear cells of 29 NSTEMI, 31 CCS, and 12 MVD patients. We then used computational modeling studies to predict interactions of the TCR beta chain variable region (TRBV) and explore sequence alignments. The EAT proteome profiling displayed a higher content of pro-inflammatory molecules (CD31, CHI3L1, CRP, EMPRINN, ENG, IL-17, IL-33, MMP-9, MPO, NGAL, RBP-4, RETN, VDB) in NSTEMI as compared to CCS (P < 0.0001). CDR3-beta spectratyping showed a TRBV21 enrichment in EAT of NSTEMI (12/29 patients; 41%) as compared with CCS (1/31 patients; 3%) and MVD (none) (ANOVA for trend P < 0.001). Of note, 11/12 (92%) NSTEMI patients with TRBV21 perturbation were at their first manifestation of ACS. Four patients with the first event shared a distinctive TRBV21-CDR3 sequence of 178 bp length and 2/4 were carriers of the human leukocyte antigen (HLA)-A*03:01 allele. A 3D analysis predicted the most likely epitope able to bind HLA-A3*01 and interact with the TRBV21-CDR3 sequence of 178 bp length, while the alignment results were consistent with microbial DNA sequences.

Conclusions: Our study revealed a unique immune signature of the epicardial adipose tissue, which led to a 3D modeling of the TCRBV/peptide/HLA-A3 complex, in acute coronary syndrome patients at their first event, paving the way for epitope-driven therapeutic strategies.

Keywords: NSTE ACS; T-cell receptor (TCR); antigen-driven immunity; computational modeling; epicardial adipose tissue (EAT); first acute myocardial infarction; immune response; precision medicine.

Figure 1

Flowchart showing enrolled patients and their experimental and study result allocations. We obtained EAT biopsies and peripheral blood from 32 NSTEMI patients, 34 CCS patients, and 12 MVD patients undergoing surgery. Three EAT specimens from NSTEMI and CCS patients were used for proteome profiling. We therefore performed TCR spectratyping and CDR3 sequencing in EAT and PBMCs of 29 NSTEMI, 31 CCS, and 12 MVD patients. We performed HLA-A molecular typing from DNA extracted from the PBMCs of the same patients. Twelve NSTEMI patients showed a TRBV21 perturbation, four of which shared a distinctive TRBV21-CDR3 sequence of 178 bp length and 2/4 were carriers of the human leukocyte antigen (HLA)-A*03:01 allele. We then used computational 3D modeling studies to predict interactions between HLA-A3*01 and the TRBV21-CDR3 sequence of 178 bp length, employing BLAST to compare the predicted peptide sequence to specific microbial databases. BLAST, Basic Local Alignment Search Tool; EAT, epicardial adipose tissue; HLA, human leukocyte antigen; MVD, mitral valve disease; NSTEMI, non-ST elevation myocardial infarction; PBMC, peripheral blood mononuclear cells; CCS, chronic coronary syndrome; TCR, T-cell receptor.

Figure 2

Flow cytometry characterization of EAT-resident T cells and proteome profiling of EAT specimens. (A) A phenotypic characterization of EAT-resident T cells was performed by flow cytometry in NSTEMI (n = 10) and CCS (n = 10) patients. A representative flow cytometry analysis is displayed and the EAT specimen of one patient is shown. About 50% of EAT-infiltrating T cells were CD4⁺, while 30% were CD8⁺ without differences among groups. (B) An inflammatory proteome profiling was performed in NSTEMI (n = 3) and CCS (n = 3) patients; X-ray films (B) and heatmap analyses (C) are shown [squares of the same color indicate the same molecule in (B, C)]. NSTEMI and CCS patients significantly differed for multiple immune-related molecules.

Figure 3

Quantitative analysis of TCR repertoire perturbations. The TRBV-TRBC spectratyping technique was applied to analyze the TCR repertoires of the enrolled patients. EAT samples were compared with the respective PBMCs. Three matrixes are displayed, one for each group of patients [(A) for NSTEMI, (B) for CCS, and (C) for MVD]. Single patients are displayed in columns, while the rows represent the same TRBV-TRBC TCR recombination for all patients in each group. Each square of the matrixes represents a TRBV-TRBC rearrangement for every single patient. TRBV-TRBC spectratypes from an ideal naive TCR repertoire follow an approximate Gaussian distribution containing eight or more peaks. Skewed TRBV-TRBC profiles can be detected as perturbation of this distribution. White squares represent non-perturbed Gaussian. Green squares display minimally perturbed Gaussian. Red squares indicate a high enrichment of the same peak(s) in the EAT sample compared with the homologous PBMC sample. Despite the high variability of the TCR repertoire used by each individual, it was possible to detect specific TCR signatures characterizing NSTEMI, CCS, or MVD patients.

Figure 4

Quantitative analysis of TRBV21 perturbation. (A) Comparison of the TRBV21 perturbations in the three groups of patients. The TRBV21 average perturbation between EAT and PBMC samples is different in the three groups of patients (ANOVA for trends P < 0.001). It is significantly higher in NSTEMI patients as compared with CCS and MVD patients (P < 0.01 for both comparisons), but similar between CCS and MVD patients. This analysis revealed a perturbation threshold of 10% for TRBV21. (B) Pie chart showing the percentage of NSTEMI with TRVB21 D >10% (41%) and the proportion of patients at their first event (pie of pie chart) (92%). (C) Quantitative analysis of TRBV21 perturbation in NSTEMI patients. NSTEMI patients with high perturbation of TRBV21 (D > 10%) are displayed in the matrix with dark blue rectangles, highlighting that the most frequently perturbed peak is the 178-bp length. (D) Frequency (%) of HLA-A*02+ and HLA-A*03+ in the three groups of patients tested. HLA-A*03⁺ frequency is higher in the NSTEMI group (38%) than in the CCS group (10%). In MVD patients, A3 was not present in the analyzed cohort. The graph shows the expected frequency of HLA-A in a reference Caucasian population deduced from http://allelefrequencies.net (>110,000 individuals). As a referral, we also reported the frequency of the commonly used HLA in the Caucasian population.

Figure 5

In-silico 3D modeling. Molecular modeling of the TCRBV/HLA-class I/epitope complex. Overall 3D structure of the quaternary complex (A). The backbone structures of TCRBV21 (yellow), HLA-A3*01 α-chain (blue), and β2-microglobulin (green) are displayed in ribbon and solvent-accessible surface representations. The epitope residues are in stick representation color-coded by atom types. A zoom view of the contact interface showing the residues important for the stabilization of the complex (B). Sketch of the predicted interactions at the interface (C); epitope residues are shown as blue circles, and HLA and TCR residues as green and violet rectangles, respectively.

Figure 6

Take-home figure. The figure shows the close relationship between EAT and coronary arteries and the intricate cellular and molecular network possibly implicated in the pathogenesis of ACS. The identification of a specific T-cell clonal expansion in the EAT of NSTEMI patients at their first clinical manifestation and the prediction of the putative sequence of the MHC–TCR–peptide complex (through in-silico modeling) indicate the existence in the EAT of an antigen-driven immune response likely involving microbiome-derived antigens as triggers for instability. These observations represent a significant step toward the perspective of engineered T-cell or epitope-based vaccine therapies. Created with BioRender.com.