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Disentangling sRNA-Seq data to study RNA communication between species

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Original languageEnglish
JournalNucleic Acids Research
DOIs
Publication statusPublished - 27 Dec 2019

Abstract

Many organisms exchange small RNAs (sRNAs) during their interactions, that can target or bolster defense strategies in host–pathogen systems. Current sRNA-Seq technology can determine the sRNAs present in any symbiotic system, but there are very few bioinformatic tools available to interpret the results. We show that one of the biggest challenges comes from sequences that map equally well to the genomes of both interacting organisms. This arises due to the small size of the sRNAs compared to large genomes, and because a large portion of sequenced sRNAs come from genomic regions that encode highly conserved miRNAs, rRNAs or tRNAs. Here, we present strategies to disentangle sRNA-Seq data from samples of communicating organisms, developed using diverse plant and animal species that are known to receive or exchange RNA with their symbionts. We show that sequence assembly, both de novo and genome-guided, can be used for these sRNA-Seq data, greatly reducing the ambiguity of mapping reads. Even confidently mapped sequences can be misleading, so we further demonstrate the use of differential expression strategies to determine true parasite-derived sRNAs within host cells. We validate our methods on new experiments designed to probe the nature of the extracellular vesicle sRNAs from the parasitic nematode Heligmosomoides bakeri that get into mouse intestinal epithelial cells.

    Research areas

  • Massively Parallel (Deep) Sequencing, genomics, Transcriptome Mapping - Monitoring Gene Expression

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