Circulating microRNAs as biomarkers of Chagas cardiomyopathy

Abstract

Background: Chagas cardiomyopathy (CHCM) is the most important clinical manifestation of Chagas disease. The analysis of cardiac miRNAs may contribute to predicting the progression to CHCM in Chagas indeterminate phase and/or to the differential diagnosis for cardiomyopathy.

Methods: We carried out a case-control study to identify circulating miRNAs associated with CHCM. We assigned 104 participants to four groups: healthy controls (HC), Chagas non-cardiomyopathy controls, CHCM cases, and ischemic cardiomyopathy controls. We performed a clinical, echocardiographic, and laboratory evaluation and profiled circulating miRNA in the serum samples.

Results: Differences between groups were observed in clinical variables and in the analysis of miRNAs. Compared to HC, CHCM participants had 4 over-expressed and 6 under-expressed miRNAs; miR-95-3p and miR-130b-3p were upregulated in CHCM compared with controls, Chagas non-cardiomyopathy and ischemic cardiomyopathy participants, suggesting that might be a hallmark of CHCM. Analysis of gene targets associated with cardiac injury yielded results of genes involved in arrhythmia generation, cardiomegaly, and hypertrophy.

Conclusions: Our data suggest that the expression of circulating miRNAs identified by deep sequencing in CHCM could be associated with different cardiac phenotypes in CHCM subjects, compared with Chagas non-CHCM, ischemic cardiomyopathy controls, and healthy controls.

Keywords: Chagas cardiomyopathy; Chagas disease; biomarkers; circulating microRNAs; microRNAs.

Figure 1

Flowchart of the inclusion process and study methods.

Figure 2

Differential expression analysis of microRNAs (miRNAs). The figure provides a comprehensive overview of the results obtained from the differential expression analysis of microRNAs (miRNAs). The upper panel shows the distribution of differentially expressed (DE) miRNAs across the comparisons between groups: G1(HC); G2 (Chagas non-CHCM) (LogFC >|0.6|; p value < 0.05); G3 (CHCM); G4 (iCM). The lower panel features a stacked bar plot, offering a visual representation of the proportion of upregulated and downregulated miRNAs within each comparison. Each stacked bar is segmented to depict the relative contributions of upregulated and downregulated miRNAs.

Figure 3

Volcano plots illustrating differentially expressed circulating miRNAs. The volcano plots depict the distinct profiles of differentially expressed circulating miRNAs derived from the following comparisons: (A) G3 (CHCM) vs. G1 HC, (B) G3 (CHCM) vs. G2 (Chagas non-CHCM), and (C) G2 (Chagas non-CHCM) vs. G1 (HC). In each plot, the y-axis represents the negative base-10 logarithm of the p-values, while the x-axis reflects the fold change, presented as a log2-transformed ratio of expression between the groups being compared. Distinctive red and blue arrowheads denote significantly upregulated and downregulated miRNAs, respectively, within the initial group of each comparison (with LogFC >0.6 and p-value < 0.05). (D) Venn diagrams showing the shared upregulated miRNAs between G3 (CHCM) vs. G1 (HC) and G3 (CHCM) vs. G2 (Chagas non-CHCM) comparisons (upper diagram, D) and the shared downregulated miRNA between G3 (CHCM) vs. G1 (HC) and G2 (Chagas non-CHCM) vs. G1 (HC) comparisons (lower diagram, D).

Figure 4

Sankey plot network between selected miRNAs, gene targets, and cardiomyopathy signatures. The network illustrates the relationships between the up and downregulated miRNAs of G3 (CHCM), their gene targets, and the subsequent connections among these genes in the context of cardiomyopathies signatures sourced from MolSigDB.

Figure 5

Receiver operating characteristic (ROC) analysis of upregulated miRNAs in serum from chronic chagas disease patients. A Receiver Operating Characteristic (ROC) analysis was conducted to evaluate the performance of upregulated miRNAs as potential biomarkers for chronic Chagas disease. The Area Under the Curve (AUC) was calculated for each miRNA to assess its discriminatory power. Higher AUC values indicate stronger discriminatory ability, suggesting that miR-130b-3p (AUC >0.7) shows promising potential as a diagnostic biomarker for chronic Chagas disease. The ROC analysis underscores the utility of these miRNAs in distinguishing disease status, contributing to improved clinical management.

Figure 6

Correlation scatter plot depicting the relationship between age and upregulated miRNA expression in the G3 (CHCM). The scatter plot portrays the correlation analysis between the relative expression levels of upregulated miRNAs and age within the G3 (CHCM). The left-upper panel shows the specific case of miR-130-3p, which exhibits a discernible positive correlation with age (p < 0.05). While the relationship between the predictor variables and the outcome variable is statistically significant, the R-value suggests that only a small percentage of the variability in the outcome variable can be explained by the predictor variables included in the model.