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77 results for “parasite transmission”
Phenotypic data related to genetic architecture of transmission stage production and virulence in schistosome parasites
<p>These data were generated related to the study of the <strong>Genetic architecture of transmission stage production and virulence in schistosome parasites</strong>.</p> <p><strong>Abstract:</strong> Both theory and experimental data from multiple pathogens suggest that the production of transmission stages should be strongly associated with virulence, but the genetic bases of parasite transmission/virulence traits are poorly understood. In the blood fluke <em>Schistosoma mansoni</em>, parasite genotypes show extensive variation in numbers of cercariae larvae shed from infected snails. Furthermore, high shedding parasites cause high mortality to snails while low shedding parasites cause low mortality, consistent with expected trade-offs between parasite transmission and virulence. To understand the genetic basis of transmission stage production/virulence, we conducted reciprocal crosses between schistosomes from two laboratory populations that differ 8-fold in cercarial shedding and in their virulence to inbred snail hosts. Each parasite generation, we determined four-week cercarial shedding profiles in inbred <em>Biomphalaria glabrata</em> snails infected with single parasite larvae. We sequenced the whole genome of the F0 parents and the exome of the F1 progeny and 188 F2 progeny from each cross, and used linkage mapping to reveal quantitative trait loci (QTLs) underlying transmission stage production. Cercarial production is polygenic: we found three major QTLs on chromosome 1, 3 and 5 (Log-of-the-odds (LOD) = 5.61, 8.19, 6.25) and two minor QTLs on chromosome 2 and 4. These QTLs act additively and explained 28.56% of the phenotypic variation in cercarial shedding. Alleles inherited from the high and low shedding parents were co-dominant at all QTLs, except for chr. 1 and chr. 4 where the “high cercarial shedding” allele is recessive. These results demonstrate that the genetic architecture of key traits directly relevant to schistosome ecology can be dissected using classical linkage mapping approaches, and set the stage for fine mapping and functional validation of the genes involved using the growing armory of functional and cell biology tools available for this parasite.</p> <p> </p> <p>This dataset is made of 4 tables:</p> <ul> <li>F0_parental_populations.csv</li> <li>F1.csv</li> <li>F2.csv</li> <li>sex.tsv</li> </ul> <p> </p> <p><strong>F0_parental_populations.csv</strong></p> <p> </p> <p>This table contains the number of cercariae produced by each individual <em>Biomphalaria glabrata</em> Bg26 snails infected with single genotypes of <em>Schistosoma mansoni</em> parasite. We have compared the transmission stage production between two different populations of <em>S. mansoni</em> parasite. This dataset was originally published in Le Clec'h et al., 2019 (Striking differences in virulence, transmission and sporocyst growth dynamics between two schistosome populations. Parasites and Vectors. 2019 Oct 16;12(1):485. doi: 10.1186/s13071-019-3741-z).</p> <p> </p> <p>This table is made of 9 columns:</p> <ul> <li><strong>id</strong>: the unique identifier of each sample.</li> <li><strong>schistosoma_population</strong>: the population of schistosome used for the infection of the snail. Each snail was infected with a single parasite genotype. We have used SmLE (high shedder/highly virulent population) and SmBRE (low shedding/low virulent population).</li> <li><strong>Shed.1</strong>: the number of cercariae produced by each parasite genotype at the first shedding week (4 weeks after exposure to parasite).</li> <li><strong>Shed.2</strong>: the number of cercariae produced by each parasite genotype at the second shedding week (5 weeks after exposure to parasite).</li> <li><strong>Shed.3</strong>: the number of cercariae produced by each parasite genotype at the third shedding week (6 weeks after exposure to parasite).</li> <li><strong>Shed.4</strong>: the number of cercariae produced by each parasite genotype at the fourth shedding week (7 weeks after exposure to parasite).</li> <li><strong>sum</strong>: the sum of the cercariae produced by each parasite genotype over the 4 weeks of shedding (Shed.1 + Shed.2 + Shed.3 + Shed.4).</li> <li><strong>average</strong>: the average number of cercariae produced by each parasite genotype over the 4 weeks of shedding.</li> <li><strong>sex</strong>: the sex of each parasite genotype determined by PCR <sup>1</sup>.</li> </ul> <p> </p> <p><strong>F1.csv</strong></p> <p> </p> <p>This table contains the number of cercariae produced by each individual <em>Biomphalaria glabrata</em> Bg26 snails infected with single genotypes of F1 progeny from the cross SmLE x SmBRE (see the manuscript for details).</p> <p> </p> <p>This table is made of 11 columns:</p> <ul> <li><strong>id</strong>: the unique identifier of each sample.</li> <li><strong>cross</strong>: F1A or F1B cross. Each snail was infected with a single parasite genotype from either F1A or F1B progeny.</li> <li><strong>Shed.1</strong>: the number of cercariae produced by each parasite genotype at the first shedding week (4 weeks after exposure to parasite).</li> <li><strong>Shed.2</strong>: the number of cercariae produced by each parasite genotype at the second shedding week (5 weeks after exposure to parasite).</li> <li><strong>Shed.3</strong>: the number of cercariae produced by each parasite genotype at the third shedding week (6 weeks after exposure to parasite).</li> <li><strong>Shed.4</strong>: the number of cercariae produced by each parasite genotype at the fourth shedding week (7 weeks after exposure to parasite).</li> <li><strong>sum</strong>: the sum of the cercariae produced by each parasite genotype over the 4 weeks of shedding (Shed.1 + Shed.2 + Shed.3 + Shed.4).</li> <li><strong>average</strong>: the average number of cercariae produced by each parasite genotype over the 4 weeks of shedding.</li> <li><strong>PO</strong>: the total phenoloxidase activity in infected snail hemolymph, measured at 7.5 weeks post-exposure <sup>2</sup>.</li> <li><strong>Hb</strong>: the hemoglobin rate in infected snail hemolymph, measured at 7.5 weeks post-exposure <sup>3</sup>.</li> <li><strong>sex</strong>: the sex of each parasite genotype determined by PCR <sup>1</sup>.</li> </ul> <p> </p> <p><strong>F2.csv</strong></p> <p>This table contains the number of cercariae produced by each individual <em>Biomphalaria glabrata</em> Bg26 snails infected with single genotypes of F2 progeny from the cross SmLE x SmBRE (see the manuscript for details).</p> <p> </p> <p>This table is made of 10 columns:</p> <ul> <li><strong>id</strong>: the unique identifier of each sample.</li> <li><strong>cross</strong>: F2A or F2B cross. Each snail was infected with a single parasite genotype from either F2A or F2B progeny.</li> <li><strong>Shed.1</strong>: the number of cercariae produced by each parasite genotype at the first shedding week (4 weeks after exposure to parasite).</li> <li><strong>Shed.2</strong>: the number of cercariae produced by each parasite genotype at the second shedding week (5 weeks after exposure to parasite).</li> <li><strong>Shed.3</strong>: the number of cercariae produced by each parasite genotype at the third shedding week (6 weeks after exposure to parasite).</li> <li><strong>Shed.4</strong>: the number of cercariae produced by each parasite genotype at the fourth shedding week (7 weeks after exposure to parasite).</li> <li><strong>sum</strong>: the sum of the cercariae produced by each parasite genotype over the 4 weeks of shedding (Shed.1 + Shed.2 + Shed.3 + Shed.4)</li> <li><strong>average</strong>: the average number of cercariae produced by each parasite genotype over the 4 weeks of shedding.</li> <li><strong>PO</strong>: the total phenoloxidase activity in infected snail hemolymph, measured at 7.5 weeks post-exposure <sup>2</sup>.</li> <li><strong>Hb</strong>: the hemoglobin rate in infected snail hemolymph, measured at 7.5 weeks post-exposure <sup>3</sup>.</li> </ul> <p> </p> <p><strong>sex.csv</strong></p> <p> </p> <p>This table contains the <em>in silico</em> sexing of F0 parents, F1 parents and F2 progeny of <em>S. mansoni</em> parasites.</p> <p>This table is made of 4 columns:</p> <ul> <li><strong>id</strong>: the unique identifier of each sample</li> <li><strong>read_depth</strong>: the read depth ratio between the Z-linked and pseudo-autosomal regions.</li> <li><strong>ratio</strong>: computed ratio between the Z-linked and pseudo-autosomal regions.</li> <li><strong>sex</strong>: the sex of each parasite genotype determined <em>in silico</em>: a ratio around 1 corresponds to a male carrying two Z chromosomes while a ratio around 0.5 corresponds to a female carrying only one Z chromosome.</li> </ul> <p><strong>Notes:</strong></p> <p><sup>1</sup>. Le Clec’h W, Chevalier F et al. Real-time PCR for sexing Schistosoma mansoni cercariae. Mol Biochem Parasitol. Jan-Feb 2016; 205(1-2):35-8.doi: 10.1016/j.molbiopara.2016.03.010. Epub 2016 Mar 26.</p> <p><sup>2</sup>. Le Clec’h W et al. Characterization of hemolymph phenoloxidase activity in two Biomphalaria snail species and impact of Schistosoma mansoni infection. Parasit Vectors. 2016 Jan 22; 9:32.doi: 10.1186/s13071-016-1319-6.</p> <p><sup>3</sup>. Le Clec'h et al. Striking differences in virulence, transmission and sporocyst growth dynamics between two schistosome populations. Parasit Vectors. 2019 Oct 16; 12(1):485. doi: 10.1186/s13071-019-3741-z.</p>
Data for: Sequential infection of Daphnia magna by a gut microsporidium followed by a haemolymph yeast decreases transmission of both parasites
<p>This dataset supports the findings presented in:<br> <br> Manzi, F., Halle, S., Seemann, L., Ben-Ami, F., & Wolinska, J. (2021). Sequential infection of <em>Daphnia magna</em> by a gut microsporidium followed by a haemolymph yeast decreases transmission of both parasites. <em>Parasitology,</em> 1-42. doi:10.1017/S0031182021001384</p>
How does parasite environmental transmission stage concentration change before, during, and after disease outbreaks?
<p>Outbreaks of environmentally transmitted parasites require that susceptible hosts encounter transmission stages in the environment and become infected, but we also know that transmission stages can be in the environment without triggering disease outbreaks. One challenge for understanding the relationship between environmental transmission stages and disease outbreaks is that the distribution and abundance of transmission stages outside of their hosts have been difficult to quantify. Thus, we have limited data about how changes in transmission stage abundance influence disease dynamics; moreover, we do not know whether the relationship between transmission stages and outbreaks differs among parasite species. We used digital PCR to quantify environmental transmission stages of five parasites in six lakes in southeastern Michigan every two weeks from June to November 2021. At the same time, we quantified infection prevalence in hosts and host density. Our study focused on eight zooplankton host species (<em>Daphnia</em> spp. and <em>Ceriodaphnia</em> <em>dubia</em>) and five of their parasites from diverse taxonomic groups (bacteria, yeast, microsporidia, and oomycete) with different infection mechanisms. We found that parasite transmission stage concentration increased prior to disease outbreaks for all parasites. However, parasites differed significantly in the relative timing of peaks in transmission stage concentration and infection outbreaks. The 'continuous shedder' parasites had transmission stage peaks at the same time as or slightly after the outbreak peaks. In contrast, parasites relying on host death for transmission ('obligate killers') had transmission stage peaks before outbreak peaks. For most parasites, lakes with outbreaks had higher spore concentrations than those without outbreaks, especially once an outbreak began; the exception was for a parasite, <em>Pasteuria</em> <em>ramosa</em>, with very strong genotypic specificity of infection. Overall, our results show that disease outbreaks are tightly linked to transmission stage concentration; outbreaks were preceded by increases in transmission stage concentration in the environment and then were fueled by the production of more transmission stages during the outbreak itself, with concentrations decreasing to pre-outbreak levels as outbreaks waned. Thus, tracking transmission stages in the environment improves our understanding of the drivers of disease outbreaks and reveals how parasite traits may affect these dynamics.</p>
The impact of within-host coinfection interactions on between-host parasite transmission dynamics varies with spatial scale
<p>Within-host interactions among coinfecting parasites can have major consequences for individual infection risk and disease severity. However, the impact of these within-host interactions on between-host parasite transmission, and the spatial scales over which they occur, remain unknown. We developed and applied a novel spatially explicit analysis to parasite infection data from a wild wood mouse (<em>Apodemus sylvaticus</em>) population. We previously demonstrated a strong within-host negative interaction between two wood mouse gastrointestinal parasites, the nematode <em>Heligmosomoides polygyrus,</em> and the coccidian <em>Eimeria hungaryensis</em>, using drug-treatment experiments. Here, we show this negative within-host interaction can significantly alter the between-host transmission dynamics of <em>E. hungaryensis</em>, but only within spatially-restricted neighbourhoods around each host. However, for the closely related species <em>E. apionodes</em>, which experiments show does not interact strongly with <em>H. polygyrus</em>, we did not find any effect on transmission over any spatial scale. Our results demonstrate that the effects of within-host coinfection interactions can ripple out beyond each host to alter the transmission dynamics of the parasites, but only over local scales that likely reflect the spatial dimension of transmission. Hence there may be knock-on consequences of drug treatments impacting the transmission of non-target parasites, altering infection risks even for non-treated individuals in the wider neighbourhood.</p>
Fig. 6 Seasonal parasite transmission along the lower Mbam river showing a in ONCHOCERCA VOLVULUS transmission in the Mbam valley of Cameroon following 16 years of annual community-directed treatment with ivermectin, and the description of a new cytotype of SIMULIUM SQUAMOSUM
Fig. 6 Seasonal parasite transmission along the lower Mbam river showing a combined parity and infection rates for flies dissected at Bayomen and Nyamongo I riverside sites (L1–L2 = percentage of flies infected with developing parasite stages only, L3H = percentage of flies containing L3 stages in the head), and b monthly transmission potentials at Bayomen, Nyamongo I, and Egona II estimated based on dissection data only. Ondouano not shown since no larvae were found in dissected flies
Fig. 2 in Networks and the ecology of parasite transmission: A framework for wildlife parasitology
Fig. 2. How do we use networks to understand the ecology of parasite transmission? Networks allow us to describe how the behaviour of individuals collectively affects the transmission of parasites within wildlife populations. They provide a flexible framework that enables analysis at three different levels; individual (panel A), dyadic (pair-wise associations) (panel B) and the network (population) level (panel C). Within each level of analysis, there are different metrics and analytical approaches that can be used to explore the ecology of parasite transmission.
Fig. 1 in Networks and the ecology of parasite transmission: A framework for wildlife parasitology
Fig. 1. What is a network? A network in its most elementary form is an adjacency matrix, where row and column labels represent the individuals in the network, and the remaining cells represent the pair-wise associations among individuals in the network (panel A). These associations can be weighted, as below (panel A), where stronger relationships are assigned a higher value (for example, the duration or frequency of contact). They can also be directed, to reflect the direction of the association; in this instance, the direction of possible parasite transmission. In this case, rows represent donor nodes, and columns represent recipient nodes (e.g., in panel A: from node C (donor) to node D (recipient), there is a score of 1). The matrix can be visualised as a network diagram (panel B), consisting of nodes, which represent the epidemiological unit of interest (usually individuals) connected together by a series of edges representing the measure of association (the potential for parasite transmission). In context of understanding the ecology of parasite transmission, edges represent a 'contact' between two hosts that provides an opportunity for parasite transfer. The weighting of edges represents the likelihood of parasite transmission (e.g., the frequency or intensity of contact among hosts). The definition of a contact will depend on the type of parasite considered, and how it is passed from one host to another.
Fig. 5 in Heartworm and seal louse: Trends in prevalence, characterisation of impact and transmission pathways in a unique parasite assembly on seals in the North and Baltic Sea
Fig. 5. Scanning electron microscopic images of E. horridus attached to seal skin and fur. A: E. horridus utilizing a hair follicle of harbour seal skin. B: E. horridus attached to seal hair with the head pointing towards seal skin. C: E. horridus with six claws attached to seal fur. D: Close up of an unattached claw of E. horridus. Asterisks positioned on nits of E. horridus. Sale bars: A 200 μm, B 400 μm, C 400 μm, D 100 μm.
Fig. 4 in Heartworm and seal louse: Trends in prevalence, characterisation of impact and transmission pathways in a unique parasite assembly on seals in the North and Baltic Sea
Fig. 4. Histological sections and staining of E. horridus revealing filarial stages in E. horridus. A: Filarial stages (arrowheads) in the pharynx. bar = 20 μm. B: Filarial stage (arrow) in the mouth region. bar = 40 μm. C: Filarial stage (arrowhead) in the intestine (in) surrounded by erythrocytes (e). bar = 15 μm. D: Filarial stage in the haemocoel (hc) of the abdomen of E. horridus (square). E: Close up of filarial stage. mp = mouthparts, mo = mouth, cu = cuticula. A–C: Haematoxylin - Eosin stain, D: Giemsa stain.
Fig. 3. A in Heartworm and seal louse: Trends in prevalence, characterisation of impact and transmission pathways in a unique parasite assembly on seals in the North and Baltic Sea
Fig. 3. A: Prevalence of A. spirocauda and E. horridus in harbour seals in the North and Baltic Sea from 1996 to 2021, data from 1996 to 2013 according to Lehnert et al. (2016). B: Prevalence of A. spirocauda and E. horridus in harbour seals during the seasons in the North and Baltic Sea from 2014 to 2021.
Fig. 2 in Heartworm and seal louse: Trends in prevalence, characterisation of impact and transmission pathways in a unique parasite assembly on seals in the North and Baltic Sea
Fig. 2. Sampling routine of E. horridus infected seal skin for histological and bacteriological examinations. A: Mild E. horridus infection of a harbour seal yearling, asterisk pointing at E. horridus. B: Close up of E. horridus C: Removing of E. horridus D: Cutting and removing of the infected skin with a sterile forceps for further investigations. Scale bars: A-D 1 cm.
Fig. 1 in Heartworm and seal louse: Trends in prevalence, characterisation of impact and transmission pathways in a unique parasite assembly on seals in the North and Baltic Sea
Fig. 1. Levels of infection with E. horridus in P. vitulina. A: Mild E. horridus infection of a harbour seal yearling, asterisk pointing at E. horridus B: Close up of E. horridus in the head area of a harbour seal C: Severe E. horridus infection of a harbour seal D: Close up of severe E. horridus infection. Scale bars: A-D 1 cm.
Fig. 4 in A community analysis approach to parasite transmission in multi-host systems: Assemblages of small mammal prey and Echinococcus multilocularis in an urban area in North America
Fig. 4. Map showing the geographic distribution of three small mammal assemblage types predicted for the City of Calgary area by a multinomial logistic regression (MLR) model associating the environmental variables to assemblage types, developed from data collected in 2012 and 2013 (Liccioli et al., 2014). Note how large portion of BWM and NHP were classified as assemblage 1 as expected, but also large portion of FCPP, where it was not expected.
Fig. 1 in A community analysis approach to parasite transmission in multi-host systems: Assemblages of small mammal prey and Echinococcus multilocularis in an urban area in North America
Fig. 1. Study sites for the characterization of the small mammal assemblages in urban Calgary, AB, Canada in 2012–2013, showing the location of five areas in Urban Calgary and detailed map of Bowmont, Southland Lowlands, and Weaselhead. Bowmont (BM), Fishcreek Provincial Park (FCPP), Nose Hill Park (NHP), Southland Lowlands (SL), and Weaselhead (WSH).
Fig. 2 in A community analysis approach to parasite transmission in multi-host systems: Assemblages of small mammal prey and Echinococcus multilocularis in an urban area in North America
Fig. 2. Dendrograms derived from the Bray-Curtis similarity of small mammal assemblages in five parks and natural areas in urban Calgary, AB, Canada, 2012–2013. a) Dendrogram using abundance data and group-average clustering algorithm. The dashed line indicates the cluster cut-off line of 45% similarity. Symbols for each site indicate the prevalence of definitive hosts (EmDH) and presence (1) or absence (0) of infected small mammals (EmIH). b) Dendrogram using abundance data and complete-linkage clustering algorithm. Note how it is similar to the dendrogram using group-average algorithm. c) Dendrogram using proportion data and group-average clustering algorithm. Note how all BM sites are in single cluster and all NHP sites and most sites are in another cluster, similar to the dendrogram using abundance data.
Fig. 3 in The trade-off between the transmission of chemical cues and parasites: behavioral interactions between leaf-cutting ant workers of different age classes
Fig. 3. Mean ± s.e. frequencies that young and old ants were observed giving or receiving allogrooming during a 15 s observation period.
Fig. 2 in The trade-off between the transmission of chemical cues and parasites: behavioral interactions between leaf-cutting ant workers of different age classes
Fig. 2. Mean ± s.e. frequencies that young and old ants were: (a) observed selfgrooming and (b) observed engaged in mandible scraping with another ant, during a 15 s observation period.
Fig. 1 in The trade-off between the transmission of chemical cues and parasites: behavioral interactions between leaf-cutting ant workers of different age classes
Fig. 1. Overall activity levels. Mean ± s.e. frequencies that young and old ants were: (a) observed engaging in one of the focal behaviors and (b) observed engaged in antennation with another ant, during a 15 s observation period.
Fig. 1 in Lack of evidence of vertical transmission of Karyolysus blood parasites in Iberian green lizards (Lacerta schreiberi)
Fig. 1. Karyolysus sp. Trophozoite (a–c) and gamonts (d–f) found in blood smears of L. schreiberi lizards. Scalebar = 10 μm.
Data from: Parasite transmission stage abundance varies in lakes over time and space
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