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49 results for “host manipulation”
Manipulating a host-native microbial strain compensates for low microbial diversity by increasing weight gain in a wild bird population
<h1>Manipulating a host-native microbial strain compensates for low microbial diversity by increasing weight gain in a wild bird population</h1> <h1> </h1> <p>These files contain data on bacteria present in the guts of wild great tit (Parus major) obtained from faecal samples and sequenced using Illumina MiSeq. These data resulted from an experiment which provided supplementary mealworms at the nest during the breeding season at number of woodland sites in Cork, Ireland. Approximately half of these nests were given mealworms covered in a freeze dried bacterial powder containing the bacteria Lactobacillus kimchicus, which had been isolated from great tit faeces from the previous season. This treatment aimed to disrupt the gut microbiota of the treatment birds in order to provide evidence for the gut microbiotas role in birds health and fitness. Included here are the 3 elements necessary to create a 'phyloseq object' containing the sample metadata, ASV (Amplicon Sequence Variant) count table and a taxonomy table. The metadata file includes the alpha diversity scores for each individual. The data include all negative control samples taken during sample collection and library preparation, which were removed before the main analyses. All analyses, except for the beta-diversity analyses, were conducted in R. All R code is available on GitHub (https://github.com/shan-e-s\). Raw Sequence data are available in the European Nucleotide Archive under access number PRJEB74941, and ERS18960426-ERS18960697.</p> <h2> </h2> <h2>## Description of the data and file structure </h2> <p>Taxonomy, ASV and metadata files required to create a phyloseq object in R. metadata.csv file contains data on individual birds (i.e. individual samples). The metadata includes descriptions of the bird itself and it's environment, namely:</p> <ul> <li>Rownames: unique sample ID for each sample, corresponds with asvTable.csv. </li> <li>Nest: unique identifier for the nest box associated with the bird being sampled. </li> <li>Sample.ID: unique identifier for the faecal sample or control sample.</li> <li>Bird.ID: Identity of the bird the sample came from, note some individuals sampled twice so some bird.ID's may reoccur in metadata with different Sample.ID.</li> <li>Date: Date the sample was taken dd/mm/yyyy.</li> <li>Day: Date the sample was taken, in days since 1st March.</li> <li>Ring.Mark: British Trust for Ornithology (BTO) metal ring ID where applicable. Birds only ringed at D15 so some young birds do not have IDRings.</li> <li>Site: ID of woodland site that bird was sampled at.</li> <li>Chick.LetterID: ID letter differentiates between different birds from the same nest. Either 'A'-'F' for nestlings, 'Fe' for females or 'M' for males.</li> <li>Age.code: BTO age code.</li> <li>Age.category: Age category that bird is in. D8 = 8 days post hatching, D15 = 15 days post hatching, adult = 1+ years post hatching.</li> <li>Sex: Bird's sex, only determined for adult birds. Fe = Female, M = Male.</li> <li>Wing_mm: Wing length in mm.</li> <li>Tarsus_mm: minimum tarsus length of bird in mm.</li> <li>Weight_g: bird's weight in grams.</li> <li>Faecal.Sample: bird's age at sampling.</li> <li>newRing: whether bird was fitted with a new BTO ring. Only relevant to adults.</li> <li>Treatment: the experimental treatment group that the bird was in. Either 'Treatment' when nest given L. kimchicus treated mealworms or 'Control' when nest given plain mealworms.</li> <li>Notes: field notes.</li> <li>Main.sample: indicates whether this sample was the main sample to be used for analysis, an alternative sample taken as a backup.</li> <li>Plate: the ID of the PCR plate which the sample was amplified on.</li> <li>Azenta_noPeriod: sample ID given to sequencing facility without special characters. Corresponds to fastq files and ASV table counts.</li> <li>Qubit_prePool: samples qubit score before pooling.</li> <li>Date_extracted: date the sample was extracted on dd/mm/yyyy.</li> <li>SampleType: whehther the sample was a 'main' sample intended for downstream analysis, a 'control' sample for detecting contamination during library preparation, a 'duplicate' for detecting PCR issues, a 'label_error' where sample was suspected of being mislabelled at some point, a 'repeat' sample intended to detect errors or issues, a 'contam' sample which was suspected of being contaminated, a 'common' sample used across different PCR plates to detect issues. Extraction_notes: notes regarding the DNA extraction of the sample. </li> <li>LibPrep_notes: notes regarding the library preparation of the sample.</li> <li>Ring.Mark.lab: the ring or sample ID written on the sample tube, recorded to help detect mislabelling.</li> <li>Post_lab_notes: notes regarding issues found post sequencing.</li> <li>NumberOfReads: number of sequence reads associated with the sample. </li> <li>DistanceToEdge: distance between nest and woodland edge in metres. </li> <li>BroodSize.D8: number of nestlings in the nest at day-8 post hatching. </li> <li>BroodSize.D15: number of nestlings in the nest at day-15 post hatching.</li> <li>firstEggLayDate: Date the first egg in the clutch was laid, in days since 1st March.</li> <li>lastEggLayDate: Date the last egg in the clutch was laid, in days since 1st March.</li> <li>Observed: number of unique ASV's (or taxa) detected in the sample.</li> <li>Chao1: Chao1 diversity of the sample.</li> <li>Shannon: Shannon diversity of the sample.</li> </ul> <p>The file 'taxonomy.csv' contains the taxonomic breakdown of each bacterial Amplicon Sequence Variant (ASV) found in the dataset from Phylum to Species. Obtained by using the Naive Bayes Classifier against the Silva (v138) taxonomic database.</p> <p>The file 'asvTable.csv' contains counts of each amplicon sequence variant's occurrence for each individual sample. Samples are rows and taxa are columns.</p> <p> </p> <h2>Sharing/Access information </h2> <p>All R code is available on GitHub (https://github.com/shan-e-s\). Raw Sequence data are available in the European Nucleotide Archive under access number PRJEB74941, and ERS18960426-ERS18960697.</p>
Data from: Host manipulation by an ichneumonid spider ectoparasitoid that takes advantage of preprogrammed web-building behaviour for its cocoon protection
Host manipulation by parasites and parasitoids is a fascinating phenomenon within evolutionary ecology, representing an example of extended phenotypes. To elucidate the mechanism of host manipulation, revealing the origin and function of the invoked actions is essential. Our study focused on the ichneumonid spider ectoparasitoid Reclinervellus nielseni, which turns its host spider (Cyclosa argenteoalba) into a drugged navvy, to modify the web structure into a more persistent cocoon web so that the wasp can pupate safely on this web after the spider's death. We focused on whether the cocoon web originated from the resting web that an unparasitized spider builds before moulting, by comparing web structures, building behaviour and silk spectral/tensile properties. We found that both resting and cocoon webs have reduced numbers of radii decorated by numerous fibrous threads and specific decorating behaviour was identical, suggesting that the cocoon web in this system has roots in the innate resting web and ecdysteroid-related components may be responsible for the manipulation. We also show that these decorations reflect UV light, possibly to prevent damage by flying web-destroyers such as birds or large insects. Furthermore, the tensile test revealed that the spider is induced to repeat certain behavioural steps in addition to resting web construction so that many more threads are laid down for web reinforcement.
Fig. 2 in Host manipulation in the face of environmental changes: Ecological consequences
Fig. 2. Examples of the impacts of temperature on a system of gammarid species infected by acanthocephalan parasites. Final host varies depending on parasite species (either a fish or a bird). Solid lines represent assumption supported by studies, while dotted lines are expectations that remain to be investigated. In this system, (1) the temperature widely influences the time of development or parasites within the intermediate hosts, which is likely to be driven by the metabolic rate of parasites (Tokeson and Holmes, 1982). Several studies suggested that (2) the time of development of parasites is linked to the intensity of their manipulation (Franceschi et al., 2010b, 2008), which in turn might (3) influence the increase of predation rate between the final host and the intermediate host. (4) Temperature is also likely to influence the final host metabolism (Bystr¨om et al., 2006), (5) influencing its predation rate (Bystr¨om et al., 2006). Altogether, (6) modifications in manipulation and predation rates are likely to induce changes in parasites' population. Meanwhile, (7) temperature also affects the metabolism of gammarid hosts (Issartel et al., 2005), inducing changes in their food consumption (Pellan et al., 2015). (8) Given that infection depends on food consumption, the risk of infection might vary accordingly, affecting parasites' population. Although its direct effect has not been investigated yet, (9) temperature is also likely to alter the intensity of manipulation, for instance through its effect on hosts' metabolism and activity, and therefore (10) secondarily impact parasite population dynamic.
Fig. 1 in Host manipulation in the face of environmental changes: Ecological consequences
Fig. 1. Schematic representation of all the interacting factors in a system involving parasite manipulation. The intensity of host manipulation induced by parasites is likely to be influenced by a variety of parameters concerning the parasites, their hosts and environmental properties. In return, manipulation can also have an impact on those parameters. Moreover, all components in the systems also interact with each other.
Data from: Host manipulation by an ichneumonid spider ectoparasitoid that takes advantage of preprogrammed web-building behaviour for its cocoon protection
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A bacterial effector manipulates host lysosomal protease activity-dependent plasticity in cell death modalities to facilitate infection
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Wolbachia endosymbionts manipulate the self-renewal and differentiation of germline stem cells to reinforce fertility of their fruit fly host
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When fiction becomes fact: exaggerating host manipulation by parasites
<p>In an era where some find fake news around every corner, the use of sensationalism has inevitably found its way into the scientific literature. This is especially the case for host manipulation by parasites, a phenomenon in which a parasite causes remarkable change in the appearance or behaviour of its host. This concept, which has deservedly garnered popular interest throughout the world in recent years, is nearly 50-years old. In the past two decades, the use of scientific metaphors, including anthropomorphisms and science fiction, to describe host manipulation has become more and more prevalent. It is possible that the repeated use of such catchy, yet misleading words in both the popular media and the scientific literature could unintentionally hamper our understanding of the complexity and extent of host manipulation, ultimately shaping its narrative in part or in full. In this commentary, the impacts of exaggerating host manipulation are brought to light by examining trends in the use of embellishing words. By looking at key examples of exaggerated claims from widely reported host-parasite systems found in the recent scientific literature, it would appear that some of the fiction surrounding host manipulation has since become fact.</p>
When fiction becomes fact: exaggerating host manipulation by parasites
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Data from: Parasite-manipulated host dispersal: evidence from population genetics and mark-recapture experiment
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Data from: Pathogens manipulate the preference of vectors, slowing disease spread in a multi-host system
The spread of vector‐borne pathogens depends on a complex set of interactions among pathogen, vector, and host. In single‐host systems, pathogens can induce changes in vector preferences for infected vs. healthy hosts. Yet it is unclear if pathogens also induce changes in vector preference among host species, and how changes in vector behaviour alter the ecological dynamics of disease spread. Here, we couple multi‐host preference experiments with a novel model of vector preference general to both single and multi‐host communities. We show that viruliferous aphids exhibit strong preferences for healthy and long‐lived hosts. Coupling experimental results with modelling to account for preference leads to a strong decrease in overall pathogen spread through multi‐host communities due to non‐random sorting of viruliferous vectors between preferred and non‐preferred host species. Our results demonstrate the importance of the interplay between vector behaviour and host diversity as a key mechanism in the spread of vectored‐diseases.
Data from: A host immune hormone modifies parasite species interactions and epidemics: insights from a field manipulation.
Parasite epidemics can depend on priority effects, and parasite priority effects can result from the host immune response to prior infection. Yet we lack experimental evidence that such immune-mediated priority effects influence epidemics. To address this research gap, we manipulated key host immune hormones, then measured the consequences for within-host parasite interactions, and ultimately parasite epidemics in the field. Specifically, we applied plant immune-signaling hormones to sentinel plants, embedded into a wild host population, and tracked foliar infections caused by two common fungal parasites. Within host individuals, priority effects were altered by the immune-signaling hormone, salicylic acid. Scaling up from within-host interactions, hosts treated with salicylic acid experienced lower prevalence of a less aggressive parasite, increased burden of infection by a more aggressive parasite, and experienced fewer coinfections. Together, these results indicate that by altering within-host priority effects, host immune hormones can drive parasite epidemics. This study therefore experimentally links host immune hormones to within-host priority effects and parasite epidemics, advancing a more mechanistic understanding of how interactions among parasites alter their epidemics.
Data from: Multidimensionality in host manipulation mimicked by serotonin injection
Manipulative parasites often alter the phenotype of their hosts along multiple dimensions. 'Multidimensionality' in host manipulation could consist in the simultaneous alteration of several physiological pathways independently of one another, or proceed from the disruption of some key physiological parameter, followed by a cascade of effects. We compared multidimensionality in 'host manipulation' between two closely related amphipods, Gammarus fossarum and Gammarus pulex, naturally and experimentally infected with Pomphorhynchus laevis (Acanthocephala), respectively. To that end, we calculated in each host–parasite association the effect size of the difference between infected and uninfected individuals for six different traits (activity, phototaxis, geotaxis, attraction to conspecifics, refuge use and metabolic rate). The effects sizes were highly correlated between host–parasite associations, providing evidence for a relatively constant 'infection syndrome'. Using the same methodology, we compared the extent of phenotypic alterations induced by an experimental injection of serotonin (5-HT) in uninfected G. pulex to that induced by experimental or natural infection with P. laevis. We observed a significant correlation between effect sizes across the six traits, indicating that injection with 5-HT can faithfully mimic the 'infection syndrome'. This is, to our knowledge, the first experimental evidence that multidimensionality in host manipulation can proceed, at least partly, from the disruption of some major physiological mechanism.
Dataset of the study on field evidence for manipulation of mosquito host selection by the human malaria parasite
<p>This repository contains the dataset from host preferences assays to determine odour-mediated mosquito host preference as well as mosquito host selection determination through identification of the blood meal origin from indoor-resting blood-fed mosquito females in Burkina Faso.</p>
Figure 1 in Two new species of Hymenoepimecis (Hymenoptera: Ichneumonidae: Pimplinae) with notes on their spider hosts and behaviour manipulation
Figure 1. (A–E) Hymenoepimecis japi sp. nov. (A) Female, dorsal view; (B) fore- and hind wings; (C) metasoma dorsal view; (D) sternite I, lateral view; (E) head. (F–L). Webs of Leucauge roseosignata. (F) Normal web; (G) egg of H. japi sp. nov. attached on the abdomen of a female; (H) third instar larva consuming the host; (I) third instar larva holding the threads of the web hub; (J) cocoon web; (K) fixation point of the axial threads of the cocoon web on vegetation; (L) hub of the cocoon web. Scale bars: (A, G, H) 5 mm; (B, C, I, K, L) 1 mm; (D, E) 0.5 mm; (F) 10 cm; (J) 1 cm.
Figure 3 in Two new species of Hymenoepimecis (Hymenoptera: Ichneumonidae: Pimplinae) with notes on their spider hosts and behaviour manipulation
Figure 3. Webs of Manogea porracea. (A) Normal web showing the positions occupied by the female and by the male. (B) Cocoon web showing the positions occupied by the male, the horizontal sheet of the female (arrow close to male), and the cocoon (arrow below). Scale bars: 1 cm.
Figure 2 in Two new species of Hymenoepimecis (Hymenoptera: Ichneumonidae: Pimplinae) with notes on their spider hosts and behaviour manipulation
Figure 2. (A–F) Hymenoepimecis sooretama sp. nov. (A) Male lateral view; (B) female lateral view; (C) fore- and hind wings; (D) metasoma dorsal view; (E) sternite I lateral view; (F) head. (G–I) Webs of Manogea porracea. (G) Parasitoid invading a web; (H) larva of H. sooretama sp. nov. attached on the abdomen of a host female; (I) cocoon. Scale bars: (A–D) 1 mm; (E) 0.5 mm; (F) 0.1 mm; (G–I) 5 mm.
Figure 3 in Regional Distribution Of A Brain-Encysting Parasite Provides Insight On Parasite-Induced Host Behavioral Manipulation
Figure 3. Correlation between the number of encysted Euhaplorchis californiensis metacercariae on the whole brain of California killifish (Fundulus parvipinnis) and the number of parasites on the diencephalon/ mesencephalon region in experimentally infected F. parvipinnis individuals. The correlation was checked using Spearman's rho (¼ 0.98).
Figure 1 in Regional Distribution Of A Brain-Encysting Parasite Provides Insight On Parasite-Induced Host Behavioral Manipulation
Figure 1. Figure displaying the number of Euhaplorchis californiensis parasites (A), weight (B), and mass parasite density (parasites/g body mass) (C) in experimentally infected and naturally infected fish. Different letters indicate differences in Tukey post-hoc test performed after a 1-way ANOVA; stars indicate significant difference in the Mann–Whitney Utest. On the y-axis, we show the number of parasites (A), body mass in + grams (B), and parasite density (parasites/g body mass) (C), and on the x- axis, we show the different treatment groups: low-infection treatment (n ¼ 8), high-infection treatment (n ¼ 8), experimentally infected (n ¼ 16), and naturally infected (n ¼ 25).
Figure 2 in Regional Distribution Of A Brain-Encysting Parasite Provides Insight On Parasite-Induced Host Behavioral Manipulation
Figure 2. Contrasting Euhaplorchis californiensis brain surface parasite density (A) and parasite numbers (B) in 3 brain regions in both high- and low-infection treatment groups. Different letters indicate the statistical difference between the different brain regions given by a Tukey post-hoc test, and the stars indicate a statistical difference between the 2 infection treatments (low and high). In all groups, n ¼ 8. Brain surface parasite density is defined as the number of parasites per square millimeter of brain surface area.
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