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Tastsoglou, S.

Publications and source records attributed to Tastsoglou, S..

2 recordsLinked to original sources

A species-specific lncRNA modulates the reproductive ability of the Asian tiger mosquito

Long non-coding RNA (lncRNA) research has emerged as an independent scientific field in recent years. Despite their association with critical cellular and metabolic processes in plenty of organisms, lncRNAs are still a largely unexplored area in mosquito research. We propose that they could serve as exceptional tools for pest management due to unique features they possess. These include low inter-species sequence conservation and high tissue specificity. In the present study, we investigated the role of ovary-specific lncRNAs in the reproductive ability of the Asian tiger mosquito, Aedes albopictus. Through the analysis of transcriptomic data, we identified several lncRNAs that were differentially expressed upon blood feeding; we called these genes Norma (NOn-coding RNA in Mosquito ovAries). We observed that silencing some of these Normas resulted in significant impact on mosquito fecundity and fertility. We further focused on Norma3 whose silencing resulted in 43% oviposition reduction and 53% hatching reduction of the laid eggs, compared to anti-GFP controls. Moreover, a significant downregulation of a neighboring ([~]100 Kb) mucins cluster was observed in smaller anti-Norma3 ovaries, indicating a potential mechanism of in-cis regulation between Norma3 and the mucins. Our work constitutes the first experimental proof-of-evidence connecting lncRNAs with mosquito reproduction and opens a novel path for pest management.

molecular biology↗

Manatee: detection and quantification of small ncRNAs from next-generation sequencing data

Small non-coding RNAs (sncRNAs) play important roles in health and disease. Next Generation Sequencing technologies are considered as the most powerful and versatile methodologies to explore small RNA (sRNA) transcriptomes in diverse experimental and clinical studies. Small RNA-Seq data analysis proved to be challenging due to non-unique genomic origin, short length and abundant post-transcriptional modifications of sRNA species. Here we present Manatee, an algorithm for quantification of sRNA classes and detection of uncharacterized expressed non-coding loci. Manatee adopts a novel approach for abundance estimation of genomic reads that combines sRNA annotation with reliable alignment density information and extensive reads salvation. Comparison of Manatee against state-of-the-art implementations using real/simulated data sets demonstrates its superior accuracy in quantification of diverse sRNA classes providing at the same time insights about unannotated expressed loci. It is user-friendly, easily embeddable in pipelines and provides a simplified output suitable for direct usage in downstream analyses and functional studies.

bioinformatics↗