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. 2020 Mar 23;14(3):e0008129.
doi: 10.1371/journal.pntd.0008129. eCollection 2020 Mar.

Schistosoma species detection by environmental DNA assays in African freshwaters

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Schistosoma species detection by environmental DNA assays in African freshwaters

Hind Alzaylaee et al. PLoS Negl Trop Dis. .

Erratum in

Abstract

Background: Schistosomiasis is a neglected tropical parasitic disease associated with severe pathology, mortality and economic loss worldwide. Programs for disease control may benefit from specific and sensitive diagnostic methods to detect Schistosoma trematodes in aquatic environments. Here we report the development of novel environmental DNA (eDNA) qPCR assays for the presence of the human-infecting species Schistosoma mansoni, S. haematobium and S. japonicum.

Methodology/principal findings: We first tested the specificity of the assays across the three species using genomic DNA preparations which showed successful amplification of target sequences with no cross amplification between the three focal species. In addition, we evaluated the specificity of the assays using synthetic DNA of multiple Schistosoma species, and demonstrated a high overall specificity; however, S. japonicum and S. haematobium assays showed cross-species amplification with very closely-related species. We next tested the effectiveness of the S. mansoni assay using eDNA samples from aquaria containing infected host gastropods, with the target species revealed as present in all infected aquaria. Finally, we evaluated the effectiveness of the S. mansoni and S. haematobium assays using eDNA samples from eight discrete natural freshwater sites in Tanzania, and demonstrated strong correspondence between infection status established using eDNA and conventional assays of parasite prevalence in host snails.

Conclusions/significance: Collectively, our results suggest that eDNA monitoring is able to detect schistosomes in freshwater bodies, but refinement of the field sampling, storage and assay methods are likely to optimise its performance. We anticipate that environmental DNA-based approaches will help to inform epidemiological studies and contribute to efforts to control and eliminate schistosomiasis in endemic areas.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Maximum likelihood phylogenetic reconstruction of the mtDNA 16S sequences used for the establishing the specificity of the assays designed for S. haematobium, S. mansoni and S. japonicum.
Numbers above branches represent the branch support (proportion of 100 bootstrap replicates.
Fig 2
Fig 2. Locations of eDNA sampling sites in the Mbeya Region of Tanzania in 2018.
In total eight sites were surveyed, labelled A-H. The key results from eDNA survey (helix symbol), host snail survey (snail symbol) and test for the presence of schistosomes in snail tissue (cercariae symbol) are given for S. haematobium (H) and S. mansoni (M) assays, with + indicating the site was positive, while a dot indicates no detection was made. Map drawn using the following open source software and data: DIVA-GIS7.5 (https://www.diva-gis.org); Africa river network (15 sec resolution), African drainage basin (15sec resolution) and African Digital Elevation Model data (30 sec resolution) from HydroSHEDS (https://www.hydrosheds.org); African Water Bodies shapefile from RCMRD GeoPortal (http://geoportal.rcmrd.org).
Fig 3
Fig 3. Probability of qPCR amplification of DNA standards of concentrations ranging from 1 copy/μl to 1,000,000 copies/μl.
a) S. mansoni assay–probability of amplification in any one qPCR across all standard templates tested. b) S. mansoni assay–probability of amplification in any one standard template that is subject to triplicate qPCR, c) S. haematobium assay–probability of amplification in any one qPCR across all standard templates tested, d) S. haematobium assay–probability of amplification in any one standard template that is subject to triplicate qPCR. Lines represent logistic models of the form y = a / (1 + b*e(-cx)). a) a = 1.002, b = 27.303, c = 3.826. b) a = 1.000, b = 7.501, c = 4.786. c) a = 1.004, b = 1.009, c = 1.415. b) a = 1.004, b = 0.342, c = 1.399. All models r > 0.995. Grey shading indicates 95% confidence intervals.

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