Step-by-Step Bioinformatics Analysis of Schistosoma mansoni Long Non-coding RNA Sequences

Long non-coding RNAs as possible therapeutic targets in protozoa, and in Schistosoma and other helminths

Long non-coding RNAs (lncRNAs) emerged in the past 20 years due to massive amounts of scientific data regarding transcriptomic analyses. They have been implicated in a plethora of cellular processes in higher eukaryotes. However, little is known about lncRNA possible involvement in parasitic diseases, with most studies only detecting their presence in parasites of human medical importance. Here, we review the progress on lncRNA studies and their functions in protozoans and helminths. In addition, we show an example of knockdown of one lncRNA in Schistosoma mansoni, SmLINC156349, which led to in vitro parasite adhesion, motility, and pairing impairment, with a 20% decrease in parasite viability and 33% reduction in female oviposition. Other observed phenotypes were a decrease in the proliferation rate of both male and female worms and their gonads, and reduced female lipid and vitelline droplets that are markers for well-developed vitellaria. Impairment of female worms' vitellaria in SmLINC156349-silenced worms led to egg development deficiency. All those results demonstrate the great potential of the tools and methods to characterize lncRNAs as potential new therapeutic targets. Further, we discuss the challenges and limitations of current methods for studying lncRNAs in parasites and possible solutions to overcome them, and we highlight the future directions of this exciting field.

Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence

Schistosomiasis is a parasitic disease caused by trematode worms of the genus Schistosoma and affects over 200 million people worldwide. The control and treatment of this neglected tropical disease is based on a single drug, praziquantel, which raises concerns about the development of drug resistance. This, and the lack of efficacy of praziquantel against juvenile worms, highlights the urgency for new antischistosomal therapies. In this review we focus on innovative approaches to the identification of antischistosomal drug candidates, including the use of automated assays, fragment-based screening, computer-aided and artificial intelligence-based computational methods. We highlight the current developments that may contribute to optimizing research outputs and lead to more effective drugs for this highly prevalent disease, in a more cost-effective drug discovery endeavor.