A bioinformatic approach for the prediction and functional classification of Toxoplasma gondii long non-coding RNAs

Abstract

Long non-coding RNAs (lncRNAs) have emerged as significant players in diverse cellular processes, including cell differentiation. Advancements in computational methodologies have facilitated the prediction of lncRNA functions, enabling insights even in non-model organisms like pathogenic parasites, in roles such as parasite development, antigenic variation, and epigenetics. In this work, we focus on the apicomplexan Toxoplasma gondii differentiation process, where the infective stage, tachyzoite, can develop into the cysted stage, bradyzoite, under stress conditions. Using a publicly available transcriptome dataset, we predicted putative lncRNA sequences associated with this differentiation process. Notably, a substantial proportion of these putative lncRNAs exhibited stage-specific expression, particularly at the bradyzoite stage. Furthermore, co-expression patterns between coding transcripts and putative TglncRNAs suggest their involvement in shared processes, such as bradyzoite development. Putative TglncRNA loci analysis revealed their potential influence on the expression of nearby coding genes, including subtelomeric genes unique to the T. gondii genome. Finally we propose a k-mer analysis approach to predict putative functional relationships between characterized lncRNAs from model organisms like Homo sapiens and the putative T. gondii lncRNAs. Our perspective led to predict putative T. gondii lncRNA that potentially could act mediating DNA damage repair pathways, opening a new study field to validate this kind of adaptive mechanisms of T. gondii in response to stress conditions.