A bioinformatic approach for the prediction andfunctional classification of Toxoplasma gondii longnon-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 lncRNA sequences associated with this differentiation process. Notably, a substantial proportion of these predicted lncRNAs exhibited stage-specific expression, particularly at the bradyzoite stage. Furthermore, co-expression patterns between coding transcripts and lncRNAs suggest their involvement in shared processes, such as bradyzoite development. TglncRNA loci analysis revealed their potential influence on the expression of nearby coding genes, including subtelomeric genes unique to the T. gondii genome. Finally, with a k-mer analysis approach, we identified functional relationships between characterized lncRNAs from model organisms like Homo sapiens and predicted T. gondii lncRNAs. Our perspective led to the identification of a T. gondii lncRNA potentially mediating DNA damage repair pathways, shedding light on the adaptive mechanisms of T. gondii in response to stress conditions.