Fibronectin degradation as biomarker for Trypanosoma cruzi infection and treatment monitoring in mice

Biomarkers for the diagnosis, treatment follow-up, and prediction of cardiac complications in Chagas disease in chronic phase: Recent advances

Chagas disease is caused by the Trypanosoma cruzi parasite and is transmitted by infected triatomine bugs. This infection affects approximately 8 million people in the Americas, and due to globalisation and displacement, it is becoming increasingly common to find infected patients worldwide. Diagnosis of the disease in its acute form is relatively simple, as the parasite can be detected in peripheral blood smears, and symptoms are visible. However, in its chronic condition, the parasite is almost undetectable, and indirect tests are necessary to determine the presence of antibodies in infected patients. It is important to note that a single test is not enough to confirm the disease in this phase, as a second serological test should confirm the diagnosis. If the results are contradictory, a third test should be performed to confirm or discard the disease. Unfortunately, laboratories may not have access to all necessary tests in many rural areas where the disease is more frequent. Rapid tests to diagnose this disease present problems, such as significant variations in sensitivity and specificity in different countries. Therefore, searching for new biomarkers that allow for optimal correlation is essential. In this work, we have searched scientific literature from the last 10 years for mentions of novel biomarkers for diagnosis, treatment follow-up, and prediction of cardiac complications in Chagas disease in its chronic phase.

Intra-host Trypanosoma cruzi strain dynamics shape disease progression: the missing link in Chagas disease pathogenesis

Chronic Chagasic cardiomyopathy develops years after infection in 20-40% of patients, but disease progression is poorly understood. Here, we assessed Trypanosoma cruzi parasite dynamics and pathogenesis over a 2.5-year period in naturally infected rhesus macaques. Individuals with better control of parasitemia were infected with a greater diversity of parasite strains compared to those with increasing parasitemia over time. Also, the in vivo parasite multiplication rate decreased with increasing parasite diversity, suggesting competition among strains or a stronger immune response in multiple infections. Significant differences in electrocardiographic (ECG) profiles were observed in Chagasic macaques compared to uninfected controls, suggesting early conduction defects, and changes in ECG patterns over time were observed only in macaques with increasing parasitemia and lower parasite diversity. Disease progression was also associated with plasma fibronectin degradation, which may serve as a biomarker. These data provide a novel framework for the understanding of Chagas disease pathogenesis, with parasite diversity shaping disease progression.IMPORTANCEChagas disease progression remains poorly understood, and patients at increased risk of developing severe cardiac disease cannot be distinguished from those who may remain asymptomatic. Monitoring of Trypanosoma cruzi strain dynamics and pathogenesis over 2-3 years in naturally infected macaques shows that increasing parasite diversity in hosts is detrimental to parasite multiplication and Chagasic cardiomyopathy disease progression. This provides a novel framework for the understanding of Chagas disease pathogenesis.

Machine Learning-Based Feature Selection and Classification for the Experimental Diagnosis of Trypanosoma cruzi

Chagas disease, caused by the Trypanosoma cruzi (T. cruzi) parasite, is the third most common parasitosis worldwide. Most of the infected subjects can remain asymptomatic without an opportune and early detection or an objective diagnostic is not conducted. Frequently, the disease manifests itself after a long time, accompanied by severe heart disease or by sudden death. Thus, the diagnosis is a complex and challenging process where several factors must be considered. In this paper, a novel pipeline is presented integrating temporal data from four modalities (electrocardiography signals, echocardiography images, Doppler spectrum, and ELISA antibody titers), multiple features selection analyses by a univariate analysis and a machine learning-based selection. The method includes an automatic dichotomous classification of animal status (control vs. infected) based on Random Forest, Extremely Randomized Trees, Decision Trees, and Support Vector Machine. The most relevant multimodal attributes found were ELISA (IgGT, IgG1, IgG2a), electrocardiography (SR mean, QT and ST intervals), ascending aorta Doppler signals, and echocardiography (left ventricle diameter during diastole). Concerning automatic classification from selected features, the best accuracy of control vs. acute infection groups was 93.3 ± 13.3% for cross-validation and 100% in the final test; for control vs. chronic infection groups, it was 100% and 100%, respectively. We conclude that the proposed machine learning-based approach can be of help to obtain a robust and objective diagnosis in early T. cruzi infection stages.