Frontiers in ImmunologyImmunologyPeer-reviewed journal article
Pathophysiology of hypereosinophilia-associated heart disease
Usman Sunusi, Ben Ziegelmeyer, Immaculeta Osuji, Mario MedvedoviciD, Haley ToddiD, Joe Abou-Khalil, Nives ZimmermanniD
Abstract
BackgroundCardiac complications in patients with hypereosinophilia cause significant morbidity and mortality. However, mechanisms of how eosinophilic inflammation causes heart damage are poorly understood. MethodsWe developed a model of hypereosinophilia-associated heart disease by challenging hypereosinophilic mice with peptide from the cardiac myosin heavy chain. Disease outcomes were measured by histology, immunohistochemistry, flow cytometry, and measurement of cells and biomarkers in peripheral blood. Eosinophil dependence was determined by using eosinophil-deficient mice ({Delta}dblGATA). Single cells from heart were subjected to single cell RNA sequencing to assess cell composition, subtypes and expression profiles. ResultsMice challenged with myocarditic and control peptide had peripheral blood leukocytosis, but only those challenged with myocarditic peptide had heart inflammation. Heart tissue was infiltrated by eosinophil-rich inflammatory infiltrates associated with cardiomyocyte damage. Disease penetrance and severity were dependent on the presence of eosinophils. Single cell RNA sequencing showed enrichment of myeloid cells, T-cells and granulocytes (neutrophils and eosinophils) in the myocarditic mice. Macrophages were M2 skewed, and eosinophils had an activated phenotype. Gene enrichment analysis identified several pathways potentially involved in pathophysiology of disease. ConclusionEosinophils are required for heart damage in hypereosinophilia-associated heart disease. Additionally, myeloid cells, granulocytes and T-cell cooperatively or independently participate in the pathogenesis of hypereosinophilia-associated heart disease.
- organism: mouse
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: experimental assay
- Citations
- 2
- FWCI
- 1.14
- Field percentile
- top 21%
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