Inflammation and mitochondria in the pathogenesis of chronic Chagas disease cardiomyopathy

Chagas disease (CD), caused by the protozoan parasite Trypanosoma cruzi, is a neglected disease affecting around 6 million people. About 30% of CD patients develop chronic Chagas disease cardiomyopathy (CCC), an inflammatory cardiomyopathy that occurs decades after the initial infection, while most infected patients (60%) remain asymptomatic in the so-called indeterminate form (IF). Death results from heart failure or arrhythmia in a subset of CCC patients. Myocardial fibrosis, inflammation, and mitochondrial dysfunction are involved in the arrhythmia substrate and triggering events. Survival in CCC is worse than in other cardiomyopathies, which may be linked to a Th1-T cell rich myocarditis with abundant interferon (IFN)-γ and tumor necrosis factor (TNF)-α, selectively lower levels of mitochondrial energy metabolism enzymes in the heart, and reduced levels of high-energy phosphate, indicating poor adenosine triphosphate (ATP) production. IFN-γ and TNF-α signaling, which are constitutively upregulated in CD patients, negatively affect mitochondrial function in cardiomyocytes, recapitulating findings in CCC heart tissue. Genetic studies such as whole-exome sequencing (WES) in nuclear families with multiple CCC/IF cases has disclosed rare heterozygous pathogenic variants in mitochondrial and inflammatory genes segregating in CCC cases. In this minireview, we summarized studies showing how IFN-γ and TNF-α affect cell energy generation, mitochondrial health, and redox homeostasis in cardiomyocytes, in addition to human CD and mitochondria. We hypothesize that cytokine-induced mitochondrial dysfunction in genetically predisposed patients may be the underlying cause of CCC severity and we believe this mechanism may have a bearing on other inflammatory cardiomyopathies.

GCPBayes pipeline: a tool for exploring pleiotropy at the gene level

Cross-phenotype association using gene-set analysis can help to detect pleiotropic genes and inform about common mechanisms between diseases. Although there are an increasing number of statistical methods for exploring pleiotropy, there is a lack of proper pipelines to apply gene-set analysis in this context and using genome-scale data in a reasonable running time. We designed a user-friendly pipeline to perform cross-phenotype gene-set analysis between two traits using GCPBayes, a method developed by our team. All analyses could be performed automatically by calling for different scripts in a simple way (using a Shiny app, Bash or R script). A Shiny application was also developed to create different plots to visualize outputs from GCPBayes. Finally, a comprehensive and step-by-step tutorial on how to use the pipeline is provided in our group's GitHub page. We illustrated the application on publicly available GWAS (genome-wide association studies) summary statistics data to identify breast cancer and ovarian cancer susceptibility genes. We have shown that the GCPBayes pipeline could extract pleiotropic genes previously mentioned in the literature, while it also provided new pleiotropic genes and regions that are worthwhile for further investigation. We have also provided some recommendations about parameter selection for decreasing computational time of GCPBayes on genome-scale data.