Abstract

BackgroundNovel methods to identify anthelmintic drug and vaccine targets are urgently needed, especially for those parasite species currently being controlled by singular, often limited strategies. A clearer understanding of the transcriptional components underpinning helminth development will enable identification of exploitable molecules essential for successful parasite/host interactions. Towards this end, we present a combinatorial, bioinformatics-led approach, employing both statistical and network analyses of transcriptomic data, for identifying new immunoprophylactic and therapeutic lead targets to combat schistosomiasis.Methodology/Principal FindingsUtilisation of a Schistosoma mansoni oligonucleotide DNA microarray consisting of 37,632 elements enabled gene expression profiling from 15 distinct parasite lifecycle stages, spanning three unique ecological niches. Statistical approaches of data analysis revealed differential expression of 973 gene products that minimally describe the three major characteristics of schistosome development: asexual processes within intermediate snail hosts, sexual maturation within definitive vertebrate hosts and sexual dimorphism amongst adult male and female worms. Furthermore, we identified a group of 338 constitutively expressed schistosome gene products (including 41 transcripts sharing no sequence similarity outside the Platyhelminthes), which are likely to be essential for schistosome lifecycle progression. While highly informative, statistics-led bioinformatics mining of the transcriptional dataset has limitations, including the inability to identify higher order relationships between differentially expressed transcripts and lifecycle stages. Network analysis, coupled to Gene Ontology enrichment investigations, facilitated a re-examination of the dataset and identified 387 clusters (containing 12,132 gene products) displaying novel examples of developmentally regulated classes (including 294 schistosomula and/or adult transcripts with no known sequence similarity outside the Platyhelminthes), which were undetectable by the statistical comparisons.Conclusions/SignificanceCollectively, statistical and network-based exploratory analyses of transcriptomic datasets have led to a thorough characterisation of schistosome development. Information obtained from these experiments highlighted key transcriptional programs associated with lifecycle progression and identified numerous anti-schistosomal candidate molecules including G-protein coupled receptors, tetraspanins, Dyp-type peroxidases, fucosyltransferases, leishmanolysins and the netrin/netrin receptor complex.

Highlights

  • Parasitic helminths constitute the most important group of metazoan pathogens affecting the global health and wellbeing of human and animal populations

  • Despite the implementation of focused and well-funded chemotherapeutic control initiatives over the last decade, schistosomiasis remains a significant cause of morbidity and mortality within countries of the developing world

  • We demonstrate the added value in simultaneously comparing the developmental expression profiles of selected gene family members described as the generation vaccine candidates, drug targets (G-protein coupled receptors [19]) or enzymes involved in producing immunomodulatory epitopes

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Summary

Introduction

Parasitic helminths constitute the most important group of metazoan pathogens affecting the global health and wellbeing of human and animal populations. There are no commercial vaccines available to combat infection prophylactically and the general community consensus is that no current experimental vaccine will be available for widespread use in the foreseeable future [2] Despite these disturbing trends and problematic issues, recent genomic and transcriptomic characterisation of several nematode and platyhelminth species have provided the necessary resources for post-genomic technologies to make positive strides in the identification of novel anthelmintic targets [3,4,5,6]. A clearer understanding of the transcriptional components underpinning helminth development will enable identification of exploitable molecules essential for successful parasite/host interactions Towards this end, we present a combinatorial, bioinformatics-led approach, employing both statistical and network analyses of transcriptomic data, for identifying new immunoprophylactic and therapeutic lead targets to combat schistosomiasis

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