bioRxiv Science⌕ Search

bioRxiv · 10.1101/2023.02.13.528426

Agricultural pesticides do not suppress infection of Biomphalaria (Gastropoda) by Schistosoma mansoni (Trematoda)

Abstract

BackgroundSchistosomiasis is a neglected tropical disease caused by trematodes of the genus Schistosoma. The pathogen is transmitted via freshwater snails. These snails indirectly benefit from agricultural pesticides which affect their enemy species. Pesticide exposure of surface waters may thus increase the risk of schistosomiasis transmission unless it also affects the pathogen. MethodologyWe tested the tolerance of the free-swimming infective life stages (miracidia and cercariae) of Schistosoma mansoni to the commonly applied insecticides diazinon and imidacloprid. Additionally, we investigated whether these pesticides decrease the ability of miracidia to infect and further develop as sporocysts within the host snail Biomphalaria pfeifferi. Principal findingsExposure to imidacloprid for 6 and 12 hours immobilized 50% of miracidia at 150 and 16 g/L, respectively (nominal EC50); 50% of cercariae were immobilized at 403 and 284 g/L. Diazinon immobilized 50% of miracidia at 51 and 21 g/L after 6 and 12 hours; 50% of cercariae were immobilized at 25 and 13 g/L. This insecticide tolerance is lower than those of the host snail B. pfeifferi but comparable to those of other commonly tested freshwater invertebrates. Exposure for up to 6 hours decreased the infectivity of miracidia at high sublethal concentrations (48.8 g imidacloprid/L and 10.5 g diazinon/L, i.e. 20 - 33 % of EC50) but not at lower concentrations commonly observed in the field (4.88 g imidacloprid/L and 1.05 g diazinon/L). The development of sporocysts within the snail host was not affected at any of these test concentrations. ConclusionsInsecticides did not affect the performance of S. mansoni at environmentally relevant concentrations. Accordingly, pesticide exposure is likely to increase the risk of schistosomiasis transmission by increasing host snail abundance without affecting the pathogen. Our results illustrate how the ecological side effects of pesticides are linked to human health, emphasizing the need for appropriate mitigation measures. Author summarySchistosomiasis is a major public health problem in 51 countries worldwide. Transmission requires human contact with freshwater snails that act as intermediate hosts, releasing free-swimming life stages of the trematodes. The host snails are highly tolerant to agricultural pesticides used in plant protection products. Pesticides enter freshwaters via drift and runoff, and indirectly foster the spread of host snails via adverse effects on more sensitive competitor and predator species in the water. Increasing the abundance of intermediate hosts raises potential contact with the human definitive host while transmission of the pathogen is not affected. Here we show that pesticides do not affect the ability of the trematode Schistosoma mansoni to infect and develop within its host snail Biomphalaria pfeifferi at environmentally relevant concentrations. Consequently, risk of schistosomiasis increases when pesticide pollution favours the proliferation of snail hosts whilst not negatively affecting the free-living parasites nor their development in their snail hosts. Measures to mitigate pesticide pollution of freshwaters should be a concern in public health programs to sustainably roll back schistosomiasis. Intersectional collaborations are required to bridge the gap between the agricultural and the public health sector in search of sustainable and safe methods of crop production.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ganatra, A., Becker, J. M., Shahid, N., Kaneno, S., Hollert, H., Liess, M., Agola, E. L., McOdimba, F., Fillinger, U.. 2023-02-15. Agricultural pesticides do not suppress infection of Biomphalaria (Gastropoda) by Schistosoma mansoni (Trematoda). https://doi.org/10.1101/2023.02.13.528426

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

SpaReg: sparsity-based 3D reconstruction of tissue microenvironments at native resolution across morphological and spatial molecular modalities

Tissue microenvironments comprise cellular and acellular components whose three-dimensional (3D) architecture guides disease fate. Direct imaging of intact specimens by light-sheet and multiphoton microscopy, and computational reconstruction from serial sections, have established that 3D spatial context reveals cell and tissue organization inaccessible at single planes. Computational reconstruction in particular can leverage archived human tissue, benefiting from the cost-effectiveness, robustness, scalable storage, workflow compatibility, and century-long pathobiology knowledge of histology, and can integrate multiple spatial modalities. However, sectioning can introduce tears and folds, and computational alignment can further distort tissue integrity. Here we introduce SpaReg, a sparsity-based 3D reconstruction method spanning histology, spatial proteomics and spatial transcriptomics. Across multiple organs, SpaReg robustly reconstructs large tissue volumes with preserved subcellular morphology despite sectioning artifacts. On a standardized histology benchmark, SpaReg achieves the best balance between 3D reconstruction accuracy and tissue integrity, and on spatial transcriptomics benchmarks it ranks among the leading methods while scaling to hundreds of sections and millions of cells in a dataset that several existing methods fail to process. Preservation of subcellular morphology by SpaReg also enables training of a Hematoxylin and Eosin (H&E)-based epithelial, T and B cell classifier, generating single-cell-resolved 3D maps directly from H&E. Applied to pancreatic tissue containing pancreatic ductal adenocarcinoma arising from an intraductal papillary mucinous neoplasm, these maps reveal that 2D sections overestimate immune exclusion, and resolve lymphoid aggregates in 3D. SpaReg, therefore, provides a scalable foundation for morphologically faithful, multimodal 3D atlases and spatially informed disease modeling

systems biology↗

TxCyto: A machine learning framework for estimating cytokine activity from whole transcriptome

Cytokines are critical mediators of intercellular communication, and a comprehensive characterization of their activity is essential for understanding health and disease. Existing tools to infer cytokine activity rely on experimental measurements. However, such measurements are available only for a small minority (43) of cytokines, and moreover, cytokine activity and response are highly context-specific, making a comprehensive experimental profiling across tissues, disease states, and biological contexts impractical. To address this gap, we developed TxCyto - a deep learning-based framework that infers the activity of cytokines, and more broadly of the tumor secretome, directly from the whole transcriptome profile of a sample. Trained on pan-cancer TCGA tumor transcriptomes, TxCyto was extensively validated in multiple independent datasets, including cytokine perturbation experiments. Across multiple cancer immunotherapy cohorts, TxCyto identified cytokines whose predicted activity was associated with therapeutic response. Furthermore, in spatial transcriptomic data for Liver cancer, TxCyto discovered spatial niches associated with response to immunotherapy. Overall, we develop a machine learning tool -TxCyto, for predicting the activity of 645 cytokines and tumor secretome from readily available whole transcriptomes. The TxCyto framework is generally applicable to other classes of regulatory molecules and TxCyto code base, and the tools are provided at https://github.com/Rahulncbs/TxCyto.

systems biology↗

Interpretable machine learning coupled to gene regulatory networks uncovers subcircuits underlying cell fate decisions

Gene regulatory networks (GRNs) model causal linkages that control cell fate decisions and differentiation transitions. Prioritizing regulatory subnetworks underlying cell state differences is of critical importance, but current methods including those reliant on topological metrics introduce circularity as the metrics prioritizing TFs are computed from the same networks whose assumptions they inherit. Separately, interpretable machine learning methods can identify latent factors (LFs) that discriminate cellular states with formal statistical guarantees but do not model regulatory linkages. Here, we present FOCAL (Factor-Outcome Coupling for Assessment of Linkages), a paradigm to prioritize regulatory subnetworks by coupling state-specific and dynamic GRNs with outcome-supervised LFs learned using interpretable machine learning without reference to network topology. This shifts GRN focus from macroscopic TF nodes to state-specific and dynamic TF-gene linkages. In B and T cells, FOCAL identified GIFs (GRNs coupled to Interpretable latent Factors), prioritized regulatory subnetworks underlying established states as well as transient regulatory episodes preceding them. By coupling LFs learnt from perturbation experiments of lineage-defining TFs, FOCAL identified transcriptional predisposition to alternative fates within progenitor cell populations before overt differentiation. This uncovered a novel NFATC2-IRF8 interplay in activated B cells, that was validated by in-vitro and in-vivo genetic perturbations. The two transcription factors act cooperatively to restrain extrafollicular plasmablast differentiation and promote germinal center B cell fate.

systems biology↗