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Loss of effector Treg signature in APOB-reactive CD4+ T cells in patients with coronary artery disease

Nature Cardiovascular Research volume 4, pages 841–856 (2025)Cite this article

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Abstract

Atherosclerosis underlies most coronary artery disease (CAD). It involves a significant autoimmune component against apolipoprotein B (APOB). In this study, we used short activation-induced marker (AIM) assays to characterize APOB-reactive CD4+ T cells in patients with angiographically verified CAD. APOB-reactive CD4+ T cells expressing CD25 and 4-1BB markers were the most abundant. Their frequency correlated positively with CAD severity. Transcriptomic analysis revealed that these cells were clonally expanded and significantly enriched in genes expressed in tissue-homing effector regulatory T (eTreg) cells. They shared signatures with CD4+ T cells in mouse and human plaques, including expression of the plaque-homing chemokine receptor CXCR6. With increasing disease severity, the Treg signature was progressively and significantly lost. Conversely, APOB-specific Treg cells from patients with severe CAD gained glycolytic and interferon response signatures. We conclude that mild CAD is associated with a regulatory program in APOB-reactive CD4+ T cells, which is replaced by a pro-inflammatory program in patients with severe CAD.

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Fig. 1: Human APOB-reactive CD4+ T cell subsets expressing specific AIM combinations exhibit differential abundance in CAD.
Fig. 2: Identification of genes enriched in APOB-reactive AIM+ subsets as compared to AIM− control CD4+ T cells.
Fig. 3: Signature analysis reveals enrichment of conventional T cell genes in APOB-reactive AIM1 transcriptomes, whereas AIM2 and AIM3 are Treg cell subsets.
Fig. 4: Clonal expansion of APOB-specific CD4+ T cells in CAD.
Fig. 5: APOB-reactive Treg cells from patients with CAD express tissue-homing chemokine receptors.
Fig. 6: APOB-reactive Treg cells are enriched in eTreg signatures.
Fig. 7: APOB-reactive eTreg cells lose their regulatory program under conditions of severe atherosclerosis.

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Data availability

The raw sequencing data are publicly available at the National Center for Biotechnology Informationʼs Gene Expression Omnibus under accession code GSE279783. All other processed data are available in the main text or Supplementary Information. No new material resources were generated in this study. GSEA was done using gene sets in Human ImmuneSigDB (https://www.gsea-msigdb.org/gsea/msigdb/human/genesets.jsp?collection=C7), HALLMARK genes (https://www.gsea-msigdb.org/gsea/msigdb/human/genesets.jsp?collection=H) and from curated T-cell-specific published gene sets (GSE149068, GSE149069, GSE263393 and GSE149090). Human circulating Treg clusters were analyzed using the Broad Institute’s interactive SingleCellPortal (https://singlecell.broadinstitute.org/single_cell/study/SCP1963). Heatmaps were generated based on data in GSE77081 and GSE161426. For analyzing chemokine receptor expression in human coronary plaque T cells, the publicly available dataset (GSE196943) was used.

Code availability

No new algorithms were generated for this study.

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Acknowledgements

We thank members of the Clinical Core, Flow Cytometry Core and Sequencing Core at the La Jolla Institute for Immunology. We thank members of the cardiac catheterization laboratories at the University of Virginia. We thank the Augusta University Georgia Cancer Center Flow and Mass Cytometry Core Facility (RRID: SCR_025747). We acknowledge support from the National Institutes of Health (awards P01 HL136275 and R35 HL145241) to K.L.

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  1. Payel Roy

    Present address: Department of Biochemistry, Indian Institute of Science, Bengaluru, India

Authors and Affiliations

  1. Department of Physiology, Immunology Center of Georgia, Augusta University, Augusta, GA, USA

    Payel Roy, Anusha Bellapu, Mohammad Oliaeimotlagh, Qingkang Lyu, Smriti Parashar, Sunil Kumar, Megh Mehta & Klaus Ley

  2. Laboratory of Inflammation Biology, Center for Autoimmune Disease, La Jolla Institute for Immunology, La Jolla, CA, USA

    Payel Roy, Sujit Silas Armstrong Suthahar, Jeffrey Makings, Runpei Wu & Klaus Ley

  3. Department of Medicine, Immunology Center of Georgia, Augusta University, Augusta, GA, USA

    Austin W. T. Chiang

  4. Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA

    Alessandro Sette

  5. Department of Medicine, University of California, San Diego, La Jolla, CA, USA

    Alessandro Sette

  6. Carter Immunology Center, University of Virginia, Charlottesville, VA, USA

    Coleen A. McNamara

  7. Division of Cardiovascular Medicine/Department of Medicine, Cardiovascular Research Center, University of Virginia, Charlottesville, VA, USA

    Coleen A. McNamara

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Contributions

K.L. and P.R. designed the study. P.R. conducted most experiments and analyzed and assembled data. A.B. and S.S.A.S. conducted most of the computational data processing. A.B., S.S.A.S., M.O. and M.M. analyzed data. Q.L., S.P. and S.K. conducted flow cytometry experiments. J.M. performed TCR extraction and analysis. R.W. prepared Smart-seq2 libraries. A.W.T.C. provided collaborative input for gene expression analyses. A.S. provided critical expertise and supportive data for stimulation assays. C.A.M. provided clinical expertise, patient samples and medical data. P.R., A.B. and S.S.A.S. prepared the figures. K.L. acquired funds. K.L. and P.R. supervised the study and wrote the paper. All authors discussed the data and provided critical input.

Corresponding authors

Correspondence to Payel Roy or Klaus Ley.

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Competing interests

K.L. is the founder and co-owner of Atherovax, Inc. He receives no compensation from Atherovax. No Atherovax funds were used in this study. K.L. and P.R. are named as co-inventors on a patent application (provisional application no. 63/789,764, filed by the La Jolla Institute for Immunology, approval status pending) that is related to the use of human APOB epitopes and related methods in modulating inflammatory responses and treating adverse cardiovascular events, disease and atherosclerosis. The other authors declare no competing interests.

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Nature Cardiovascular Research thanks Claudia Monaco and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Flow cytometry-based evaluation of activation marker expression in APOB-reactive human CD4+T cells.

a-b) Human PBMCs (n = 18 independent donors) were stimulated with APOB6 peptide pool and expression of different activation-induced marker (AIM) combinations in stimulated vs unstimulated PBMCs were compared. a) Mean ± SEM yield per million CD4+T cells (numbers in stimulated minus those in unstimulated sets) for nine AIM combinations are shown. b) Average (mean ± SEM) fold change (frequency in stimulated divided by that in unstimulated) for CD40L + CD69 + , CD25 + 4-1BB + , CD25 + OX-40+ and CD69 + OX-40 + AIM combinations are shown. c-e) Analysis of APOB-reactive CD4+T cells in CAD patients (n = 18) related to Fig. 1. c) Gating strategy in flow cytometry to identify AIM1,2,3 and AIM- subsets shown in Fig. 1a. d) Pie-chart showing relative abundance of each APOB-reactive CD4+T subset (AIM1, 2 and 3) within total AIM+ cells. Average frequencies were plotted to calculate percent abundance. e) Frequencies (mean ± SEM) of AIM1+ (left) and AIM3+ (right) cells in CADlo (Gensini<20, n = 9, mean Gensini score 10.6; SD ± 8.3) vs CADhi groups (Gensini>20, n = 9, mean Gensini 70.9; SD ± 28.7). Log10 transformed Y-axes; data points with 0 or negative values were collapsed onto the minimum value on the scale. Statistical tests done with two-tailed Mann-Whitney U test (e).

Source data

Extended Data Fig. 2 Gene expression in APOB-reactive and control transcriptomes from CAD patients.

a) Clinical table summarizing details related to demographics, medications, lab values and disease severity of patients in the CAD cohort used for transcriptomic analyses. Categorical variables are plotted as counts and percentages, while continuous variables are shown as mean ± SEM within the cohort (sample size = 40 patients). b) Table showing donor IDs (as used in the CAVA cohort), CAD severity scores (Gensini), and input cell numbers for cDNA preparation for each APOB-reactive (AIM1,2,3) and control (AIM-) libraries that were sequenced. Samples labelled as “Dropout” were excluded due to poor quality and yield of cDNA or library. All samples from donor #472 were excluded due to low ( < 50%) viability of PBMCs from this donor. Median viability of PBMCs from other donors was 93.1% (Min-max 80.34 – 98.1, interquartile range 4.7). c) Venn diagram depicting overlap across genes downregulated in AIM1, 2 and 3 subsets as compared to the AIM- group.

Extended Data Fig. 3 Analysis of gene expression across three APOB-reactive subsets.

a) Elbow plot analysis to identify the number of PCs that contribute to >90% of the variance in the transcriptomes. b) Volcano plots showing number of DE genes between AIM2 and AIM1 (left), AIM3 and AIM1 (middle), AIM3 and AIM2 (right). Y axis capped at p = 10-14. Horizontal line at −log10 (p-value) = 1.3 (representing adjusted p-value 0.05). Vertical lines at log2fold ± 1. Statistical analyses were performed using a two-tailed Wald test with Benjamini–Hochberg p-value adjustment. Criteria for significant DE: log2fold ± 1, adjusted p-value < 0.05. Red: upregulated; Blue: downregulated; Grey dots: not significantly different. c) Heatmap of APOB-enriched genes ranked based on their enrichment in AIM1 (top 50) or AIM2&3 (bottom 50) transcriptomes.

Extended Data Fig. 4 Flow cytometry analysis of Treg-related markers in three APOB-reactive subsets.

a) Gating strategy in flow cytometry to identify AIM1,2,3 and AIM- subsets. b-d) Representative FACS plots showing FOXP3 (b), HELIOS (c) and CTLA4 (d) protein expression in AIM1, AIM2, AIM3 and AIM- subsets.

Extended Data Fig. 5 Clonality analysis of APOB-reactive TCRβ CDR3 repertoire.

Table showing the number of expanded CDR3 clones from APOB-reactive subsets (AIM1-3 and total AIM + ) within each donor. The final column shows the number of AIM+ clones that were also detected in the control AIM- CDR3 repertoire from the same donor.

Extended Data Fig. 6 Chemokine receptor expression in APOB-reactive Tregs and all Tregs.

a) Gating strategy in flow cytometry to identify AIM2+ Tregs, total Tregs and Non-APOB (AIM-) subsets. b) Representative FACS plots (left) and quantification (mean ± SEM, right) showing expression of CCR5 protein marker on APOB-reactive Treg subset (green) and on all CD25+CD127lo Treg cells (black) in human PBMCs (n = 6). Non-APOB reactive (AIM-) cells and Fluorescence minus one (FMO) were used as negative controls. Statistical comparisons (b) were done using two-tailed Mann–Whitney U test.

Source data

Extended Data Fig. 7 APOB-reactive Tregs are enriched in effector Treg markers.

a) Median expression levels (TPMs) of general and effector Treg-related genes in APOB-reactive Tconv (AIM1, n = 37), Treg (AIM2, n = 35), and non-APOB reactive control (AIM-, n = 38) samples. b-d) Gene expression analysis in circulating human Treg subsets accessed at Broad Institute’s SingleCellPortal (SCP1963). UMAP visualization showing CCR4, CCR8, CXCR6 (b) and TIGIT (c) in Treg subsets. Color scale: yellow (lowest) to blue (highest) expression. Red outline marks FOXP3 expressing Treg cells. d) Violin plots of HLA-DRA, HLA-DRB1, and HLA-DRB5 genes (n = 13 subjects). e) FACS plots showing surface expression of TIGIT (left) and HLA-DR (right) proteins on APOB-reactive Treg subset and on all CD25+CD127lo Treg cells in human PBMCs. f) Significantly enriched pathways related to effector Treg genes described in Fig. 6g. Dotted line at -log10 adjusted p = 1.3. g) FACS plots showing FMO control for PD-1 expression on human AIM2 (left) and total Treg (right) cells. Gating strategy to identify AIM2+ Tregs and total Tregs (e,g) shown in ED Fig. 6a. Statistical comparisons were done using the Kruskal-Wallis test with Dunn’s adjustment (a) and two-tailed Fisher’s exact test and Benjamini-Hochberg adjustment (f).

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Extended Data Fig. 8 Dynamics of Treg gene expression in APOB-reactive Treg transcriptomes from patients with varying CAD severity.

a) GSEA plot showing negative enrichment of activated Treg signature in APOB-reactive Treg transcriptomes from patients with highest CAD severity (Gensini >30) compared to those with less severe CAD (Gensini <30). Enrichment score was calculated using a weighted Kolmogorov–Smirnov-like statistic and phenotype-based permutation test in-built in GSEA. b) Heatmaps showing expression (median TPMs) of Treg genes from DICE in AIM2 transcriptomes from donors grouped by CAD severity. c) Table showing NES values for Th1/Th17 signatures in APOB-reactive Treg transcriptomes from CAD patients with Gensini 10-20, 20.5-30 and >30.

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Supplementary information

Reporting Summary

Supplementary Tables 1–7

Supplementary Table 1: Class II HLA alleles of CAVA donors used for transcriptomic analyses. Supplementary Table 2: Gene sets used for signature analysis. Supplementary Table 3: Distinct and overlapping sets of genes differentially expressed between AIM− and each AIM+ subset. Supplementary Table 4: Genes differentially expressed across the three APOB-reactive AIM+ groups. Supplementary Table 5: Productive (in-frame for protein translation and without stop codons) TCR β chain CDR3 clones. Supplementary Table 6: Normalized expression values of antigen-specific and eTreg-related genes. Supplementary Table 7: Normalized gene expression values of memory Treg genes.

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Roy, P., Bellapu, A., Suthahar, S.S.A. et al. Loss of effector Treg signature in APOB-reactive CD4+ T cells in patients with coronary artery disease. Nat Cardiovasc Res 4, 841–856 (2025). https://doi.org/10.1038/s44161-025-00671-9

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