Mining SNPs in extracellular vesicular transcriptome of Trypanosoma cruzi: a step closer to early diagnosis of neglected Chagas disease

Abstract

One of the newest and strongest members of intercellular communicators, the Extracellular vesicles (EVs) and their enclosed RNAs; Extracellular RNAs (exRNAs) have been acknowledged as putative biomarkers and therapeutic targets for various diseases. Although a very deep insight has not been possible into the physiology of these vesicles, they are believed to be involved in cell-to-cell communication and host-pathogen interactions. EVs might be significantly helpful in discovering biomarkers for possible target identification as well as prognostics, diagnostics and developing vaccines. In recent studies, highly bioactive EVs have drawn attention of parasitologists for being able to communicate between different cells and having likeliness of reflecting both source and target environments. Next-generation sequencing (NGS) has eased the way to have a deeper insight into these vesicles and their roles in various diseases. This article arises from bioinformatics-based analysis and predictive data mining of transcriptomic (RNA-Seq) data of EVs, derived from different life stages of Trypanosoma cruzi; a causing agent of neglected Chagas disease. Variants (Single Nucleotide Polymorphisms (SNPs)) were mined from Extracellular vesicular transcriptomic data and functionally analyzed using different bioinformatics based approaches. Functional analysis showed the association of these variants with various important factors like Trans-Sialidase (TS), Alpha Tubulin, P-Type H+-ATPase, etc. which, in turn, are associated with disease in different ways. Some of the ‘candidate SNPs’ were found to be stage-specific, which strengthens the probability of finding stage-specific biomarkers. These results may lead to a better understanding of Chagas disease, and improved knowledge may provide further development of the biomarkers for prognosis, diagnosis and drug development for treating Chagas disease.

Keywords

• Extracellular vesicles
• Chagas
• RNA-Seq
• NGS data analysis
• R/Bioconductor
• Biomarkers