Interleukin-1β Drives Disease Progression in Arrhythmogenic Cardiomyopathy

Abstract

Arrhythmogenic cardiomyopathy (ACM) is a genetic form of heart failure that affects 1 in 5000 people globally and is caused by mutations in cardiac desmosomal proteins including PKP2, DSP, and DSG2. Individuals with ACM suffer from ventricular arrhythmias, sudden cardiac death, and heart failure. There are few effective treatments and heart transplantation remains the best option for many affected individuals. Here we performed single nucleus RNA sequencing (snRNAseq) and spatial transcriptomics on myocardial samples from patients with ACM and control donors. We identified disease-associated spatial niches characterized by co-existence of fibrotic and inflammatory cell types and failing cardiac myocytes. The inflammatory-fibrotic niche co-localized to areas of cardiac myocyte loss and was comprised of FAP (fibroblast activation protein) and POSTN (periostin) expressing fibroblasts and macrophages expressing NLRP3 (NLR family pyrin domain containing 3) and NFκB activated genes. Using homozygous Desmoglein-2 mutant (Dsg2 ^mut/mut ) mice, we identified analogous populations of Postn expressing fibroblasts and inflammatory macrophage populations that co-localized within diseased areas. Detailed single cell RNA sequencing analysis of inflammatory macrophage subsets that were increased in ACM samples revealed high levels of interleukin-1β (Il1b) expression. To delineate the possible benefit of targeting IL-1β in ACM, we treated Dsg2 ^mut/mut mice with an anti-IL-1β neutralizing antibody and observed attenuated fibrosis, reduced levels of inflammatory cytokines and chemokines, preserved cardiac function, and diminished conduction slowing and automaticity, key mechanisms of arrhythmogenesis. These results suggest that currently approved therapeutics that target IL-1β or IL-1 signaling may improve outcomes for patients with ACM.

Fig 1. ACM alters the cardiac cellular and transcriptomic environment.

(A) Study design schematic outlining human tissue sequencing methods. (B) Global UMAP with annotations of major cell populations. (C) Violin plot outlining major canonical markers identifying cell populations. (D) Composition plot displaying relative proportions of major cell types between donor control group and ACM group. (E) Pseudobulk analysis displaying degree of gene expression changes across major cell types in Donor vs ACM. (F) Number of total differentially upregulated genes for each cell type. (G) Spatial transcriptomic plots displaying major cell populations overlaid over H&E tissue images.

Fig 2. ACM has a unique spatial niche, which includes strong overlap between myeloid cells and fibroblast.

(A) Global UMAPs derived from spatial gene expression data outlining major niches. (B) Heatmap displaying genes expressed by spatial niches. (C) Spatial niches overlaid onto tissue architecture to determine where niches are positioned in tissue space. (D) Top differentially expressed genes between ACM and Donor controls overlaid onto the spatial UMAP. (E) Pathway analysis displaying major upregulated pathways in ACM relative to Donor controls. (F) Pearson correlation plot displaying likelihood that two cell types will be found in the same spatial location in tissue. Darker blue indicates higher probability. (G) H&E images from two ACM samples highlighting areas of tissue damage, myeloid, and fibroblast concentration. (H) Circle graph displaying overall cell type proportions in tissue along with a region-specific cell type proportion graph in areas of tissue damage and fibrosis. (I) Heatmap displaying expression of major signaling pathways across spatial niches.

Fig 3. POSTN+ fibroblasts and inflammatory macrophages are increased in ACM and colocalize in areas of tissue damage and fibrosis.

(A) UMAP of fibroblast populations. (B) Composition plots of fibroblast populations between Donor controls and ACM. (C) Major fibroblast gene markers overlaid on the fibroblast UMAP. (D) Heatmap displaying expression of fibroblast markers across spatial niches. (E) Fibroblast differential gene expression signature associated with either Donor controls or ACM overlaid onto the fibroblast UMAP space. (F) ACM fibroblast gene expression signature overlaid onto ACM tissue space. (G) UMAP of myeloid populations. (H) Composition plots of myeloid populations between Donor controls and ACM. (I) Major gene markers for myeloid populations overlaid on the myeloid UMAP. (J) Pathway analysis displaying pathways upregulated in ACM relative to Donor controls based on differentially expressed myeloid genes. (K) Colocalization of the inflammatory macrophage population (Mac4) and the POSTN+ fibroblast population (Fib3) in areas of tissue damage and fibrosis and heatmap displaying expression of major inflammatory genes across spatial niches. (L) Immunofluorescence staining displaying colocalization of CCR2+ CD68+ macrophages and FAP+ fibroblasts in ACM tissue samples. Two independent samples were used. Broader images are captured at 20x.

Fig 4. Dsg2^mut/mut mice have analogous POSTN+ fibroblast and inflammatory macrophage populations as found in human ACM.

(A) Study design outlining how myeloid and fibroblast libraries were sequenced. (B) Global UMAP of mouse fibroblast populations. (C) Composition plots comparing fibroblast populations between WT mice and Dsg2^mut/mut mice. (D) Gene expression score generated from mouse orthologs of differentially upregulated fibroblast genes from human ACM sequencing data overlaid onto the mouse fibroblast UMAP. (E) The previously generated human gene expression score represented on a dot plot across mouse fibroblast populations. (F) Global UMAP of mouse myeloid populations. (G) Composition plots comparing myeloid populations between WT mice and Dsg2^mut/mut mice. (H) Gene expression score generated from mouse orthologs of differentially upregulated myeloid genes from human ACM sequencing data overlaid onto the mouse myeloid UMAP. (I) The previously generated human gene expression score represented on a dot plot across mouse myeloid populations. (J) Expression score for Il1b measured across mouse myeloid populations.

Fig 5. IL1β blockade significantly attenuates disease in Dsg2^mut/mut mice.

(A) Study design outlining treatment schedule of neutralizing IL1β antibody. (B) Measurement of ejection fraction (n=6, 10, and 9 for each group respectively). (C) Representative ecgs from each treatment group and quantification of the proportion of ectopic beats and QRS duration (n=6, 10, and 9 for each group respectively). (D) Representative Masson’s trichrome images from each treatment group and quantification of fibrosis percentage (n=6, 10, and 9 for each group respectively). Brown-Forsythe and Welch ANOVA testing was used for graphs from (B), (C), and (D). (E) Representative immunostained hearts probed for NLRP3, H&E, and CASP1. n≥5 hearts/cohort/stain; yellow arrows, NLRP3 positive staining localized in areas of myocardial lesions; white arrowhead, NLRP3 positive staining localized around vessel lumen.

Fig 6. Early IL1β blockade alters the transcriptomic environment in Dsg2^mut/mut mice.

(A) Global UMAP of cell populations captured in iCell8cx sequencing. (B) Total number of differentially expressed genes between Dsg2^mut/mut isotype control and Dsg2^mut/mut anti IL1β treated across different cell types. (C) Pathway analysis displaying differentially expressed pathways in cardiac myocytes between Dsg2^mut/mut isotype control and Dsg2^mut/mut anti IL1β treated mice. (D) Heatmap displaying top differentially expressed cardiac myocyte genes between WT isotype controls and Dsg2^mut/mut isotype controls and compared to those same genes in Dsg2^mut/mut anti IL1β treated mice. (E) Pathway analysis displaying differentially expressed pathways in fibroblasts between Dsg2^mut/mut isotype control and Dsg2^mut/mut anti IL1β treated mice. (F) Heatmap displaying top differentially expressed fibroblast genes between WT isotype controls and Dsg2^mut/mut isotype controls and compared to those same genes in Dsg2^mut/mut anti IL1β treated mice. (G) Pathway analysis displaying differentially expressed pathways in endothelial cells between Dsg2^mut/mut isotype control and Dsg2^mut/mut anti IL1β treated mice. (H) Heatmap displaying top differentially expressed endothelial genes between WT isotype controls and Dsg2^mut/mut isotype controls and compared to those same genes in Dsg2^mut/mut anti IL1β treated mice.

Fig 7. IL1β inhibition prevents NFκB nuclear localization in cardiac myocytes and infiltrating myocardial CCR2/CD68+ macrophages in Dsg2^mut/mut mice.

(A) Representative immunostained hearts probed for RelA, CCR2/CD68, JUP, and Cx43. n≥5 hearts/cohort/stain; black arrows, cardiomyocyte RelA positive nuclei; red arrowheads, CCR2/CD68+ macrophages; red arrows: CCR2/CD68+ cardiac myocytes. (B) Number of cells per mm² positive for nuclear RelA localization. (C) Number of cells per mm² positive for CCR2. (D) Pearson’s correlation analysis for cells that showed dual labeling for RelA and CCR2 (P- and r-values inset). For B and C, *P<0.05 for any cohort vs WT+Isotype; and ^†P<0.05 for any cohort vs Dsg2^mut/mut+Isotype