Systemic cytokines, chemokines and growth factors reveal specific and shared immunological characteristics in infectious cardiomyopathies.

Heart disease is a major cause of death worldwide. Chronic Chagas cardiomyopathy (CCC) caused by infection with Trypanosoma cruzi leading to high mortality in adults, and rheumatic heart disease (RHD), resulting from infection by Streptococcus pyogenes affecting mainly children and young adults, are amongst the deadliest heart diseases in low-middle income countries. Despite distinct etiology, the pathology associated with both diseases is a consequence of inflammation. Here we compare systemic immune profile in patients with these cardiopathies, to identify particular and common characteristics in these infectious heart diseases. We evaluated the expression of 27 soluble factors, employing single and multivariate analysis combined with machine-learning approaches. We observed that, while RHD and CCC display higher levels of circulating mediators than healthy individuals, CCC is associated with stronger immune activation as compared to RHD. Despite distinct etiologies, univariate analysis showed that expression of TNF, IL-17, IFN-gamma, IL-4, CCL4, CCL3, CXCL8, CCL11, CCL2, PDGF-BB were similar between CCC and RHD, consistent with their inflammatory nature. Network analysis revealed common inflammatory pathways between CCC and RHD, while highlighting the broader reach of the inflammatory response in CCC. The final multivariate model showed a 100% discrimination power for the combination of the cytokines IL-12p70, IL-1Ra, IL-4, and IL-7 between CCC and RHD groups. Thus, while clear immunological distinctions were identified between CCC and RHD, similarities indicate shared inflammatory pathways in these infectious heart diseases. These results contribute to understanding the pathogenesis of CCC and RHD and may impact the design of immune-based therapies for these and other inflammatory cardiopathies that may also share immunological characteristics.

Cytokine gene functional polymorphisms and phenotypic expression as predictors of evolution from latent to clinical rheumatic heart disease.

INTRODUCTION: Inflammation associated with rheumatic heart disease (RHD) is influenced by gene polymorphisms and inflammatory cytokines. There are currently no immunologic and genetic markers to discriminate latent versus clinical patients, critical to predict disease evolution. Employing machine-learning, we searched for predictors that could discriminate latent versus clinical RHD, and eventually identify latent patients that may progress to clinical disease. METHODS: A total of 212 individuals were included, 77 with latent, 100 with clinical RHD, and 35 healthy controls. Circulating levels of 27 soluble factors were evaluated using Bio-Plex ProTM® Human Cytokine Standard 27-plex assay. Gene polymorphism analyses were performed using RT-PCR for the following genes: IL2, IL4, IL6, IL10, IL17A, TNF and IL23. RESULTS: Serum levels of all cytokines were higher in clinical as compared to latent RHD patients, and in those groups than in controls. IL-4, IL-8, IL-1RA, IL-9, CCL5 and PDGF emerged in the final multivariate model as predictive factors for clinical, compared with latent RHD. IL-4, IL-8 and IL1RA had the greater power to predict clinical RHD. In univariate analysis, polymorphisms in IL2 and IL4 were associated with clinical RHD and in the logistic analysis, IL6 (GG + CG), IL10 (CT + TT), IL2 (CA + AA) and IL4 (CC) genotypes were associated with RHD. CONCLUSION: Despite higher levels of all cytokines in clinical RHD patients, IL-4, IL-8 and IL-1RA were the best predictors of clinical disease. An association of polymorphisms in IL2, IL4, IL6 and IL10 genes and clinical RHD was observed. Gene polymorphism and phenotypic expression of IL-4 accurately discriminate latent versus clinical RHD, potentially instructing clinical management.