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84 results for “Schistosoma mansoni”
Assembled chromosomes of the blood fluke Schistosoma mansoni provide insight into the evolution of its ZW sex-determination system
<p><em>Schistosoma mansoni </em>has a diploid genome of approximately 380 MB, organized in 7 pairs of autosomes and 2 sex chromosomes. The original <em>Schistosoma mansoni </em>Genome Project was completed by the Wellcome Sanger Institute in collaboration with The Institute for Genome Research using a Whole Genome Shotgun sequencing strategy. The draft assembly was subsequently improved first by incorporating Illumina reads from a clonal (single-miracidial) infection and more recently by incorporating long PacBio reads, HiC, and optical mapping data.</p> <p>Associated manuscript can be found at https://www.biorxiv.org/content/10.1101/2021.08.13.456314v1</p>
Schistosoma mansoni ATAC-seq results for IGV (female and male worms with and without LSD1 inhibitor)
<p>In this study, the anti-schistosomal activity of 39 <em>Homo sapiens</em> Lysine Specific Demethylase 1 (HsLSD1) inhibitors was investigated on parasitic life cycle stages associated with both definitive and intermediate host infection. Amongst this collection of small molecules, compound <strong>33</strong> was the most potent and reduced <em>ex vivo</em> viabilities of schistosomula, juveniles, miracidia and adults. At its sub-lethal concentration to adults (3.13 µM), compound <strong>33 </strong>also significantly impacted oviposition, ovarian as well as vitellarian architecture and gonadal/neoblast stem cell proliferation. ATAC-seq analysis of adults demonstrated that compound <strong>33</strong> significantly affected chromatin structure (intragenic regions > intergenic regions), especially in genes differentially expressed in cell populations (e.g., germinal stem cells, hes2<em><sup>+</sup></em>stem cell progeny, S1 cells and late female germinal cells) linked to these <em>ex vivo</em> phenotypes.</p> <p>The data presented here allow for visualisation in IGV https://igv.org/app/</p> <p>Produced in collaboration with IHPE. </p>
Collection of Schistosoma mansoni ChIP-Seq input fastq files
<p>These are fastq files of ChIP-Seq input files for different life cycle stages of <em>Schistosoma mansoni</em>.</p> <ul> <li>adult female worms</li> <li>pairs of adults</li> <li>female cercariae</li> <li>miracidia</li> <li>primary sporocysts (sp1)</li> </ul> <p>Produced at IHPE (http://ihpe.univ-perp.fr/)</p>
Datasets for "Targeted insertion and reporter transgene activity at a gene safe harbor of the human blood fluke, Schistosoma mansoni"
<p>To identify sites that could serve as potential genomic safe harbours (GSHs) for transgene integration, we conducted a genome-wide bioinformatic search based on established, widely accepted criteria, along with newly introduced criteria (below), that would satisfy benign and stable gene expression. </p> <p>At the outset, we identified <strong>euchromatic</strong> regions in all developmental stages of <em>S. mansoni </em>to avoid silencing genes to be integrated upon CRISPR/Cas manipulation. With these criteria, we enriched for regions that were, (i) close to peaks of H3K4me3, a histone modification that is associated with euchromatin and transcription start sites, (ii) regions that did not include H3K27me3, a histone modification that is associated with heterochromatin, (iii) regions of open euchromatin accessible to Tn5 integration, in an Assay of Transposase Accessible Chromatin sequencing (ATAC-seq) providing a positive display of integration events, and (iv) given that HIV-1 integrates preferentially into euchromatin in human cell lines, we used sites of HIV proviral integration known from <em>S. mansoni</em> to likewise support predictions of euchromatic regions.</p> <p>Examination of the draft genome of <em>S. mansoni</em> in Worm Base Parasite, version 7 (WormBase Parasite) identified 6,884 regions with enrichment of H3K4me3 in the absence of H3K27me3 in available developmental stages (H3K4me3 not K3K27me3). In mature, adult schistosomes, we found consistently 10,533 ATAC positive regions. There were 4,027 ATAC regions that overlapped with H3K4me3 but not K3K27me3, and 2,915 genes overlapped with (ATAC and H3K4me3 not H3K27me3). Forty-two unambiguous HIV integration sites were identified, and eight genes were ≤ 11 kb upstream or downstream from these integration sites. Repeats were masked with RepeatMasker V4.1.0 using a specific repeat library produced with RepeatModeler2 V2.0.1 and stored as a GFF file.</p> <p>To identify intergenic GSH, we located 10,149 intergenic regions. There were 9,985 regions beyond 2 kb upstream and 8,837 regions outside long non-coding-RNA (lncRNA), which were intersected to 95,587 unique intergenic regions outside 2 kb and lncRNA of ≥100 bp. Two hundred regions were identified intersecting with merged ATAC H3K4me3 signal. Four of these were situated ≤ 11 kb distance from HIV integration sites. </p> <p>Made at George Washington University, Justus Liebig University Giessen, Khon Kaen University, Naresuan University, Aberystwyth University, Schistosomiasis Resource Center, IHPE. </p>
Genetic variants (chr. 6) from Old World Schistosoma mansoni exomes
<p>Variant calling file (VCF) produced from exome libraries of <em>Schistosoma mansoni</em> (bloodfluke) samples from the Old Wold (West Africa (Senegal, Niger), East Africa (Tanzania), and Middle East (Oman)). One sample form the New World (Caribbean (HR9)) was added for comparison. The variants were called on the 3 Mb of chromosome 6 centered on the <em>SmSULT-OR</em> gene. This gene is involved in resistance to the drug oxamniquine (OXA). The aim of the related article was to investigate the origin of OXA resistant mutations in the New Wolrd by identifying sequence variation in <em>SmSULT-OR</em> in <em>S. mansoni</em> from the Old World, where OXA has seen minimal usage.</p>
Data release: Whole-genome sequencing of Schistosoma mansoni reveals extensive diversity with limited selection despite mass drug administration
<p>Source data used in the publication: Berger et al. (2021) - Provisional title: 'Whole-genome sequencing of <em>Schistosoma mansoni</em> reveals extensive diversity with limited selection despite mass drug administration'. These data were used to generate all figures used in the publication and all files are organised and labelled specifically to run with the custom code that uses these data can be found at: http://doi.org/10.5281/zenodo.4975908. </p> <p><br> <strong>File descriptions:</strong></p> <p><strong>SOURCE DATA.zip - All source data for all figures. </strong></p> <p><strong>Figure 1b:</strong></p> <ul> <li>supplementary_data_9.txt - Metadata</li> </ul> <p><strong>Figure 2a&b:</strong></p> <ul> <li>207_PCA.eigenvec - PCA eigenvectors</li> <li>207_PCA.eigenval - PCA eigenvalues</li> </ul> <p><strong>Figure 2c:</strong></p> <ul> <li>autosomes.mdist - PLINK distance matrix used to build the neighbour joining phylogeny</li> </ul> <p><strong>Figure 2d:</strong></p> <ul> <li>all.pi.pixy.schools.txt - Nucleotide diversity results for each school subpopulation.</li> </ul> <p><strong>Figure 2e:</strong></p> <ul> <li>autosomes.dxy.5kb.schools.txt - Autosomal D<sub>XY</sub> results between school subpopulations. </li> <li>autosomes.fst.5kb.schools.txt - Autosomal F<sub>ST</sub> results between school subpopulations.</li> </ul> <p><strong>Figure 2f:</strong></p> <ul> <li>admixture_all.txt - ADMIXTURE results for each sample and population sizes, column 1 represents number of populations (K), columns 3-8 represent admixture values for each population. </li> </ul> <p><strong>Figure 3a, Supplementary figure 10a:</strong></p> <ul> <li>sfs.csv - Site frequency spectra (allelic proportions at each frequency bin) for each school. </li> </ul> <p><strong>Figure 3b:</strong></p> <ul> <li>TD.all.txt - Tajima's D values calculated in 5 kb windows for each school subpopulation. </li> </ul> <p><strong>Figure 4a, Supplementary figures 13-18: </strong></p> <ul> <li>ALL.MAYUGE.IHS.ihs.out.100bins.norm.txt.zip - Normalised iHS scores for the Mayuge district parasite populations (Selscan output).</li> </ul> <p><strong>Figure 4b, Supplementary figures 13-18: </strong></p> <ul> <li>ALL.TORORO.IHS.ihs.out.100bins.norm.txt.zip -<strong> - </strong>Normalised iHS scores for the Tororo district parasite populations (Selscan output).</li> </ul> <p><strong>Figure 4c, Supplementary figures 13-18: </strong></p> <ul> <li>ALL.MAYUGEvsTORORO.xpehh.xpehh.out.norm.txt.zip - - Normalised XP-EHH scores between Mayuge and Tororo parasite populations.</li> </ul> <p><strong>Figure 4d, Supplementary figures 13-18:</strong></p> <ul> <li>MAYUGE_TORORO_2000.windowed.weir.txt.zip - F<sub>ST</sub> values calculated between Mayuge and Tororo populations in 2kb windows. </li> </ul> <p><strong>Figure 4e, Supplementary figures 12a&c:</strong></p> <ul> <li>MAYUGE_PI.windowed.pi.zip - Nucleotide diversity values calculated in 2 kb windows for Mayuge populations. </li> <li>TORORO_PI.windowed.pi.zip - Nucleotide diversity values calculated in 2 kb windows for Kocoge populations (Tororo district).</li> </ul> <p><strong>Figure 5a:</strong></p> <ul> <li>all.pi.treat.fix.txt.zip - Nucleotide diversity results for each treatment subpopulation</li> </ul> <p><strong>Figure 5b</strong></p> <ul> <li>autosomes.dxy.5kb.treatment.txt - <strong> </strong>- Autosomal D<sub>XY</sub> results between clearance phenotype subpopulations. </li> <li>autosomes.fst.5kb.treatment.txt<strong> </strong>- Autosomal F<sub>ST</sub> results between clearance phenotype subpopulations. </li> </ul> <p><strong>Figure 5c:</strong></p> <ul> <li>fst.windows.2kb.treatment.txt.zip - F<sub>ST</sub> values for comparisons between different treatment groups (Pre-treatment, post-treatment (good clearers), post-treatment (poor clearers))</li> </ul> <p><strong>Figure 5d: </strong></p> <ul> <li>assoc_err_binary.txt.zip - Results of binary trait association between miracidia sampled from hosts with good clearance phenotypes (where treatment appeared to be highly effective) and miracidia isolated post-treatment from hosts with poor clearance phenotypes (where miracidia are potentially derived from parasites that survived treatment.</li> </ul> <p><strong>Figure 5e:</strong></p> <ul> <li>assoc_err_linear.txt.zip - - Results of linear regression genome-wide association study with the ERR estimates for all 198 samples, using the mean of the posterior ERR estimates from Crellen et al. (2016) as a quantitative trait.</li> </ul> <p><strong>Supplementary figure 1:</strong></p> <ul> <li>median.coverage.txt - Normalised depth of read coverage (column 4) calculated in 25 kb windows (columns 2&3) across all samples for all chromosomes (column 1).</li> </ul> <p><strong>Supplementary figure 2a-f: </strong></p> <ul> <li>cohort.genotyped.txt.zip - <strong> </strong>- Variant quality site values (used to inform variant site retention or removal). </li> </ul> <p><strong>Supplementary figure 2g:</strong></p> <ul> <li>hard_filtered.imiss.txt - Per sample variant missingness (used to inform quality control).</li> </ul> <p><strong>Supplementary figure 2h:</strong></p> <ul> <li>hard_filtered_filtindv.lmiss.txt.zip - Per site missingness (used to inform quality control).</li> </ul> <p><strong>Supplementary figure 3a, 4a, 4b:</strong></p> <ul> <li>prunedData.eigenvec - PCA eigenvectors</li> <li>prunedData.eigenval - PCA eigenvalues</li> </ul> <p><strong>Supplementary figure 3b:</strong></p> <ul> <li>pruned_data.mdist.csv - Distance matrix used as the basis for the neighbour joining phylogeny.</li> </ul> <p><strong>Supplementary figure 5:</strong></p> <ul> <li>cv_scores.txt - ADMIXTURE coefficient of variation scores (column 2) for each population size (1).</li> </ul> <p><strong>Supplementary figure 6:</strong></p> <ul> <li>*_SMC_SE.csv - SMC++ results (from 25 subsampled replicates) for each school subpopulation and outgroup samples. </li> </ul> <p><strong>Supplementary Figure 7:</strong></p> <ul> <li>smcpp.csv - SMC++ results for each school subpopulation and outgroup samples. </li> </ul> <p><strong>Supplementary Figure 8a-d</strong></p> <ul> <li>pi.per_host.txt.zip - Nucleotide diversity values for each host infrapopulation. </li> </ul> <p><strong>Supplementary Figure 9:</strong></p> <ul> <li>sexing.csv - inferred sex (based on differential read coverage over pseudoautosomal and Z-specific regions of the Z chromosome). </li> </ul> <p><strong>Supplementary Figure 10b:</strong></p> <ul> <li>sfs_res.csv - residuals for the SFS analysis in 3a/10a.</li> </ul> <p><strong>Supplementary Figure 11:</strong></p> <ul> <li>MAYUGE_TAJIMA_D.Tajima.D.2kb.txt.zip - Tajima's D values calculated for the Mayuge population in 2kb windows. </li> <li>Tororo_TAJIMA_D.Tajima.D.2kb.txt.zip - Tajima's D values calculated for the Tororo population in 2kb windows. </li> </ul> <p><strong>Supplementary Figures 13-18:</strong></p> <ul> <li>genes.bed - Coordinates of gene models (<em>S. mansoni </em>v7 annotation).</li> <li>KOCOGE_SITE_PI.sites.pi.txt.zip - Per site nucleotide diversity values</li> <li>MAYUGE_TORORO_sites.weir.fst.txt.zip - Per site F<sub>ST</sub> values between Mayuge and Tororo populations. </li> <li>coverage_5kb.windows.txt.zip - Per sample depth of read coverage in 5 kb windows. Columns 4,5,6 represent the median, mean and sstev of coverage for each 5kb window (columns 2&3) along each chromosome (column 1). </li> <li>median.sample.coverage.txt - Median chromosomal depth of read coverage for each sample. </li> </ul> <p><strong>Supplementary Figure 19:</strong></p> <ul> <li>kocoge_median.ld.txt.zip - <strong> </strong>- The decay of linkage disequilibrium with genomic distance between all sites within 50 kb for the Kocoge parasite samples. Chromosomes are shown in column 1, distance in column 2, median values in column 3. </li> <li>mayuge_median.ld.txt.zip - The decay of linkage disequilibrium with genomic distance between all sites within 50 kb for the Mayuge parasite samples. Chromosomes are shown in column 1, distance in column 2, median values in column 3. </li> </ul> <p><strong>Misc files:</strong></p> <p>schools.list - List of samples and schools where they were sampled. </p> <p> </p>
Figure 2 in Shell geometric morphometrics in Biomphalaria glabrata (Mollusca: Planorbidae) uninfected and infected with Schistosoma mansoni
Figure 2. Principal component analysis diagram of the first two principal components (within percentage explained variance contribution) from 60 Biomphalaria glabrata specimens, uninfected (black dots) and infected with Schistosoma mansoni (gray dots). Ellipse encloses 90% of data for each group.
Figure 1 in Shell geometric morphometrics in Biomphalaria glabrata (Mollusca: Planorbidae) uninfected and infected with Schistosoma mansoni
Figure 1. Shell of Biomphalaria glabrata showing the landmarks (LM1–LM12) disposition. White landmarks correspond to type I and II landmarks, while the gray to semilandmarks.
Figure 3 in Shell geometric morphometrics in Biomphalaria glabrata (Mollusca: Planorbidae) uninfected and infected with Schistosoma mansoni
Figure 3. Grid deformation showing differences in the Principal Component 1 (PC1) between the mean configuration of Biomphalaria glabrata uninfected and infected with Schistosome mansoni.
FIGURE 1 in RANDOM SPATIAL DISTRIBUTION OF SCHISTOSOMA MANSONI AND HOOKWORM INFECTIONS AMONG SCHOOL CHILDREN WITHIN A SINGLE VILLAGE
FIGURE 1. Spatial distribution of different infection intensity levels of Schistosoma mansoni among school children living in the village of Fagnampleu, western Côte d'Ivoire.
FIGURE 2 in RANDOM SPATIAL DISTRIBUTION OF SCHISTOSOMA MANSONI AND HOOKWORM INFECTIONS AMONG SCHOOL CHILDREN WITHIN A SINGLE VILLAGE
FIGURE 2. Spatial distribution of different infection intensity levels of hookworm among school children living in the village of Fagnampleu, western Côte d'Ivoire.
Resource fluctuations inhibit the reproduction and virulence of the human parasite Schistosoma mansoni in its snail intermediate host
Resource availability can powerfully influence host-parasite interactions. However, we currently lack a mechanistic framework to predict how resource fluctuations alter individual infection dynamics. We address this gap with experiments manipulating resource supply and starvation for a human parasite, Schistosoma mansoni, and its snail intermediate host to test a hypothesis derived from mechanistic energy budget theory: resource fluctuations should reduce schistosome reproduction and virulence by inhibiting parasite ingestion of host biomass. Low resource supply caused hosts to remain small, reproduce less, and produce fewer human-infectious cercariae. Periodic starvation also inhibited cercarial production and prevented infection-induced castration. The periodic starvation experiment also revealed substantial differences in fit between two bioenergetic model variants, which differ in their representation of host starvation. Simulations using the best fit parameters of the winning model suggest that schistosome performance substantially declines with resource fluctuations with periods >7 days. These experiments strengthen mechanistic theory that can be readily scaled up to the population level to understand key feedbacks between resources, host population dynamics, parasitism, and control interventions. Integrating resources with other environmental drivers of disease in an explicit bioenergetic framework could ultimately yield mechanistic predictions for many disease systems.
Supporting information for "Automated ChIPmentation procedure on limited biological material of the human blood fluke Schistosoma mansoni"
<ul> <li>Files “CG_Ro_1_High Sensitivity DNA Assay_DE13805677_2019-06-20_09-10-48.pdf” and “CG_Ro_2_High Sensitivity DNA Assay_DE13805677_2019-06-20_10-12-12.pdf” uncropped files used in figure 2</li> <li>File “gel qpcr test input sds_01.tif” : uncropped file used in figure 8.</li> <li>File "20200612-1_Report.pdf" : qPCR report for inputs 1µL and available chromatine ("Row7"), figure 6 </li> <li>File "20200919-1_Report.pdf" : qPCR report for for testing SDS after Tn5, figure 7B and figure 8</li> <li>File "20200921-1_Report.pdf" : qPCR report for for comparing inputs with enzyme of Diagenode kit and our protocol with other enzyme Tn5 </li> <li>File "wilcoxon_curves.tgz" contains compressed versions of R-script "wilcoxon_curves.Rmd" that was used to compared metagene profiles and generate "wilcoxon_curves.html" and underlying ressources that are also in this compressed archive</li> </ul> <p>Produced at IHPE (http://ihpe.univ-perp.fr)</p>
Time series analysis of tegument ultrastructure of in vitro transformed miracidium to mother sporocyst of the human parasite Schistosoma mansoni
<p>Here is a compilation of all the Scanning Electron Microscopy pictures at our disposal regarding the in vitro transformation of miracidia to mother sporocysts of <em>Schistosoma mansoni</em>. These datas were partially published in:</p> <p><a href="https://doi.org/10.1016/j.actatropica.2023.106840">https://doi.org/10.1016/j.actatropica.2023.106840</a></p> <p> </p>
Resource fluctuations inhibit the reproduction and virulence of the human parasite Schistosoma mansoni in its snail intermediate host
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Temporal variability and flooding influence the ecological niche of <em>Biomphalaria</em> intermediate hosts for <em>Schistosoma mansoni</em> in rural Uganda
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Data from: Inbreeding within human Schistosoma mansoni: do host- specific factors shape the genetic composition of parasite populations?
The size, structure and distribution of host populations are key determinants of the genetic composition of parasite populations. Despite the evolutionary and epidemiological merits, there has been little consideration of how host heterogeneities affect the evolutionary trajectories of parasite populations. We assessed the genetic composition of natural populations of the parasite Schistosoma mansoni in northern Senegal. A total of 1346 parasites were collected from 14 snail and 57 human hosts within three villages and individually genotyped using nine microsatellite markers. Human host demographic parameters (age, gender and village of residence) and co-infection with Schistosoma haematobium were documented, and S. mansoni infection intensities were quantified. F-statistics and clustering analyses revealed a random distribution (panmixia) of parasite genetic variation among villages and hosts, confirming the concept of human hosts as 'genetic mixing bowls' for schistosomes. Host gender and village of residence did not show any association with parasite genetics. Host age, however, was significantly correlated with parasite inbreeding and heterozygosity, with children being more infected by related parasites than adults. The patterns may be explained by (1) genotype-dependent 'concomitant immunity' that leads to selective recruitment of genetically unrelated worms with host age, and/or (2) the 'genetic mixing bowl' hypothesis, where older hosts have been exposed to a wider variety of parasite strains than children. The present study suggests that host-specific factors may shape the genetic composition of schistosome populations, revealing important insights into host–parasite interactions within a natural system.
Schistosoma mansoni raw genotype calls from exome data
<p><em>Schistosoma mansoni, </em>a snail-vectored, blood fluke that infects humans, was introduced into the Americas from Africa during the Trans-Atlantic slave trade. As this parasite shows strong specificity to the snail intermediate host, we expected that adaptation to S. American <em>Biomphalaria</em> spp. snails would result in population bottlenecks and strong signatures of selection. We scored 475,081 single nucleotide variants (SNVs) in 143 <em>S. mansoni</em> from the Americas (Brazil, Guadeloupe, and Puerto Rico) and Africa (Cameroon, Niger, Senegal, Tanzania, and Uganda), and used these data to ask: (i) Was there a population bottleneck during colonization? (ii) Can we identify signatures of selection associated with colonization? And (iii) what were the source populations for colonizing parasites? We found a 2.4-2.9-fold reduction in diversity and much slower decay in linkage disequilibrium (LD) in parasites from East to West Africa. However, we observed similar nuclear diversity and LD in West Africa and Brazil, suggesting no strong bottlenecks and limited barriers to colonization. We identified five genome regions showing selection in the Americas, compared with three in West Africa and none in East Africa, which we speculate may reflect adaptation during colonization. Finally, we infer that unsampled African populations from central African regions between Benin and Angola, with contributions from Niger, are likely the major source(s) for Brazilian <em>S. mansoni</em>. The absence of a bottleneck suggests that this is a rare case of a serendipitous invasion, where <em>S. mansoni </em>parasites were preadapted to the Americas and were able to establish with relative ease.</p>
The Effect of Praziquantel Treatment on Schistosoma Mansoni Morbidity and re-Infection Along Lake Victoria, Uganda
ClinicalTrials.gov study NCT00215267. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Clinical Evaluation of Ujiplus® Against Schistosoma Mansoni
ClinicalTrials.gov study NCT04679831. IPD Sharing: YES. Countries: 1. Publications: 5.
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