Schistosomiasis Haematobia

Schistosomiasis, urogenital and intestinal, afflicts 251 million people worldwide with approximately two-thirds of the patients suffering from the urogenital form of the disease. Freshwater snails of the genus Bulinus (Gastropoda: Planorbidae) serve as obligate intermediate hosts for Schistosoma haematobium, the etiologic agent of human urogenital schistosomiasis. These snails also act as vectors for the transmission of schistosomiasis in livestock and wildlife. Despite their crucial role in human and veterinary medicine, our basic understanding at the molecular level of the entire Bulinus genus, which comprises 37 recognized species, is very limited. In this study, we employed Illumina-based RNA sequencing (RNAseq) to profile the genome-wide transcriptome of Bulinus globosus, one of the most important intermediate hosts for S. haematobium in Africa. A total of 179,221 transcripts (N50 = 1235) were assembled and the benchmarking universal single-copy orthologs (BUSCO) was estimated to be 97.7%. The analysis revealed a substantial number of transcripts encoding evolutionarily conserved immune-related proteins, particularly C-type lectin (CLECT) domain-containing proteins (n = 316), Toll/Interleukin 1-receptor (TIR)-containing proteins (n = 75), and fibrinogen related domain-containing molecules (FReD) (n = 165). Notably, none of the FReDs are fibrinogen-related proteins (FREPs) (immunoglobulin superfamily (IgSF) + fibrinogen (FBG)). This RNAseq-based transcriptional profile provides new insights into immune capabilities of Bulinus snails, helps provide a framework to explain the complex patterns of compatibility between snails and schistosomes, and improves our overall understanding of comparative immunology.

Traditionally, automated slide scanning involves capturing a rectangular grid of field-of-view (FoV) images which can be stitched together to create whole slide images, while the autofocusing algorithm captures a focal stack of images to determine the best in-focus image. However, these methods can be time-consuming due to the need for X-, Y- and Z-axis movements of the digital microscope while capturing multiple FoV images. In this paper, we propose a solution to minimise these redundancies by presenting an optimal procedure for automated slide scanning of circular membrane filters on a glass slide. We achieve this by following an optimal path in the sample plane, ensuring that only FoVs overlapping the filter membrane are captured. To capture the best in-focus FoV image, we utilise a hill-climbing approach that tracks the peak of the mean of Gaussian gradient of the captured FoVs images along the Z-axis. We implemented this procedure to optimise the efficiency of the Schistoscope, an automated digital microscope developed to diagnose urogenital schistosomiasis by imaging Schistosoma haematobium eggs on 13 or 25 mm membrane filters. Our improved method reduces the automated slide scanning time by 63.18% and 72.52% for the respective filter sizes. This advancement greatly supports the practicality of the Schistoscope in large-scale schistosomiasis monitoring and evaluation programs in endemic regions. This will save time, resources and also accelerate generation of data that is critical in achieving the targets for schistosomiasis elimination.

Schistosomiasis is a parasitic disease in Tanzania affecting over 50% of the population. Current control strategies involve mass drug administration (MDA) campaigns at the district level, which have led to problems of over- and under-treatment in different areas. WHO guidelines have called for more targeted MDA to circumvent these problems, however a scarcity of prevalence data inhibits decision makers from prioritizing sub-district areas for MDA. This study demonstrated how geostatistics can be used to inform planning for targeted MDA.

Geostatistical sub-district (ward-level) prevalence estimates were generated through combining a zero-inflated poisson model and kriging approach (regression kriging). To make predictions, the model used prevalence survey data collected in 2021 of 17,400 school children in six regions of Tanzania, along with several open source ecological and socio-demographic variables with known associations with schistosomiasis.

The model results show that regression kriging can be used to effectively predict the ward level parasite prevalence of the two species of Schistosoma endemic to the study area. Kriging was found to further improve the regression model fit, with an adjusted R-squared value of 0.51 and 0.32 for intestinal and urogenital schistosomiasis, respectively. Targeted treatment based on model predictions would represent a shift in treatment away from 193 wards estimated to be over-treated to 149 wards that would have been omitted from the district level MDA.

Geostatistical models can help to support NTD program efficiency and reduce disease transmission by facilitating WHO recommended targeted MDA treatment through provision of prevalence estimates where data is scarce.