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56 results for “host-pathogen interaction”
Graphic Illustration of Kendra Phelp's Talk: A harmonized taxonomic resource is critical for accurately interpreting host-pathogen interactions
<p><a href="https://lib.ku.edu/people/courtney-foat" target="_blank" rel="noopener">Courtney Foat</a>, Advisor for Strategic Initiatives & Organizational Engagement at the University of Kansas, graphically recorded this invited talk by Kendra Phelps at an NSF-supported Workshop: Digital Collections Data and Tracking Disease.</p>
Host-pathogen interactions under pressure: a review and meta-analysis of stress-mediated effects on disease dynamics
<p>Human activities have increased the intensity and frequency of natural stressors and created novel stressors, altering host-pathogen interactions, and changing the risk of emerging infectious diseases. Despite the ubiquity of such anthropogenic impacts, predicting the directionality of outcomes has proven challenging. Here, we conduct a review and meta-analysis to determine the primary mechanisms through which stressors affect host-pathogen interactions and to evaluate the impacts stress has on host fitness (survival and fecundity) and pathogen infectivity (prevalence and intensity). We assessed 891 effect sizes from 71 host species (representing seven taxonomic groups) and 78 parasite taxa from 98 studies. We found that infected and uninfected hosts had similar sensitivity to stressors and that responses varied according to stressor type. Specifically, limited resources compromised host fecundity and decreased pathogen intensity, while abiotic environmental stressors (e.g., temperature and salinity) decreased host survivorship and increased pathogen intensity, and pollution increased mortality but decreased pathogen prevalence. We then used our meta-analysis results to develop Susceptible-Infected theoretical models to illustrate scenarios where infection rates are expected to increase or decrease in response to resource limitation or environmental stress gradients. Our results carry implications for conservation and disease emergence and reveal areas for future work. </p>
Host-pathogen interactions under pressure: a review and meta-analysis of stress-mediated effects on disease dynamics
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Host-pathogen protein interactions predicted using structure
<p>This dataset accompanies a manuscript describing a method to predict host-pathogen protein interactions using structure:</p> <p>Host-pathogen protein interactions predicted by comparative modeling.<br /> Davis FP, Barkan DT, Eswar N, McKerrow JH, Sali A. Protein Sci (2007) 16:2585-2596.<br /> http://www.proteinscience.org/cgi/doi/10.1110/ps.073228407</p> <p>The files contain predictions made for 10 human pathogens including species of Mycobacterium, Apicomplexa, and Kinetoplastida. The species.all.zip files contain all interactions predictions for each species along with the filter criteria that each interaction passed. The species.filter.zip files contains the same information, but only for the subset of interactions that passed the biological and network-level filters. These files can be viewed in any spreadsheet program, such as Excel.</p> <p> </p> <p>Predictions were made for interactions between human and </p> <ol> <li>Mycobacterium tuberculosis: mtuber</li> <li>Mycobacterium leprae: mleprae</li> <li>Leishmania major: lmajor</li> <li>Trypanosoma brucei: tbrucei</li> <li>Trypanosoma cruzi: tcruzi</li> <li>Cryptosporidium hominis: chominis</li> <li>Cryptosporidium parvum: cparvum</li> <li>Plasmodium falciparum: pfalciparum</li> <li>Plasmodium vivax: pvivax</li> <li>Toxoplasma gondii: tgondi</li> </ol>
Combined single-cell quantitation of host and SIV genes and proteins ex vivo reveals host-pathogen interactions in individual cells
<p>Single cell gene expression data for the paper "Combined single-cell quantitation of host and SIV genes and proteins ex vivo reveals host-pathogen interactions in individual cells" to appear in PLOS Pathogens.</p> <p>Package contains two single cell data sets, one consisting of single cells from PBMCs in three animals, and the other three tissues from one animal.</p> <p>The single-cells are grouped based on the SIV viral genes that they express.</p> <p> </p>
Data from: Combining in vivo and in vitro approaches to better understand host-pathogen nutritional interactions
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Data from: Genetic variation in resistance and fecundity tolerance in a natural host-pathogen interaction
Individuals vary in their ability to defend against pathogens. Determining how natural selection maintains this variation is often difficult, in part because there are multiple ways that organisms defend themselves against pathogens. One important distinction is between mechanisms of resistance that fight off infection, and mechanisms of tolerance that limit the impact of infection on host fitness without influencing pathogen growth. Theory predicts variation among genotypes in resistance, but not necessarily in tolerance. Here, we study variation among pea aphid (Acyrthosiphon pisum) genotypes in defense against the fungal pathogen Pandora neoaphidis. It has been well established that pea aphids can harbor symbiotic bacteria that protect them from fungal pathogens. However, it is unclear whether aphid genotypes vary in defense against Pandora in the absence of protective symbionts. We therefore measured resistance and tolerance to fungal infection in aphid lines collected without symbionts, and found variation among lines in survival and in the percent of individuals that formed a sporulating cadaver. We also found evidence of variation in tolerance to the effects of pathogen infection on host fecundity, but no variation in tolerance of pathogen-induced mortality. We discuss these findings in light of theoretical predictions about host-pathogen coevolution.
Data from: Environmentally dependent host-pathogen and vector-pathogen interactions in the barley yellow dwarf virus pathosystem
1. Understanding environmentally dependent variation in interspecific interactions is needed for evaluating how agroecosystems respond to abiotic stressors, including climate change. Both biotic and abiotic conditions shape crop responses to stress events, but interactions between environmental conditions and insect borne plant pathogens remain poorly understood. 2. We tested the hypothesis that drought stress, as applied by experimental water deprivation, drives conditional outcomes in host–pathogen and host–vector interactions using a cereal–aphid–virus association and greenhouse experiments. 3. Under conditions of ample water supply, infection of wheat plants with Barley yellow dwarf virus (BYDV) resulted in reduced above-ground growth, seed set, seed yields and seed germination compared with plants exposed only to non-infected (non-viruliferous) aphids or control plants not subjected to aphid infestation. However, when water was chronically limiting, infection with Barley yellow dwarf virus did not significantly affect plant performance. 4. When wheat was subjected to acute drought stress, plants infected with Barley yellow dwarf virus surpassed both control plants and plants exposed to non-infected aphids in all measured performance traits. 5. Feeding experiments with aphid vectors (Rhopalosiphum padi) and subsequent life table analysis revealed that aphid fecundity improved by 47% when feeding on Barley yellow dwarf virus-infected plants when water inputs were chronically low. However, when plants received ample water, aphid fecundity was enhanced by only 23% from feeding on BYDV-infected plants. 6. Synthesis and applications. Collectively, our experiments suggest that wheat– Barley yellow dwarf virus interactions shift along gradients of water stress severity and duration. When Barley yellow dwarf virus infection preceded water deprivation, plant performance was not reduced from virus infection, and infected plants recovered from severe stress events more readily than non-infected plants. However, vector–pathogen mutualism resulting in enhanced reproduction of aphids on virus-infected plants is likely to amplify direct plant injury from herbivory in the field. Our findings indicate that during periods of drought, management of Barley yellow dwarf virus infection may not be needed and infection could benefit wheat under conditions of acute water stress.
Host-Pathogen Interactions in the Plasmodium-Infected Mouse Liver at Spatial and Single-Cell Resolution
<p>Dataset created in the study "A Spatial Transcriptomics Atlas of the Malaria-infected Liver Indicates a Crucial Role for Lipid Metabolism and Hotspots of Inflammatory Cell Infiltration" </p><p><strong>Structure</strong></p><p><strong>ST_berghei_liver</strong></p><p>contains data generated during <i>stpipeline </i>analysis and imaging on 2k arrays Spatial Transcriptomics platform as well as data necessary for and from hepaquery analysis. These samples include 38 sections in total of which 8 are from mice (n=4) infected with sporozoites for 12h, 5 sections from control mice (n=3) at 12h, 7 sections from mice (n=4) infected with sporozoites for 24h and 4 sections from control mice (n=3) for 24 as well as 8 samples of mice (n=2) infected with sporozoites for 38h and control mice (n =2) for 38h. </p><ul><li><i><strong>count</strong></i> contains gene expression matrix output from stpipeline in .tsv format</li><li><i><strong>spotfiles</strong> </i>contains coordinate files for count matrices</li><li><i><strong>images </strong></i>contains scaled H&E, Fluorescence (FL) and annotated H&E images (from FL annotations) scaled to 10% of the original image size.</li><li><i><strong>masks </strong></i>contains image masks for hepaquery analysis</li><li><i><strong>distances </strong></i>contains distance measurements from original section sorted by timepoint as well as combined across timepoints</li><li><strong>cluster</strong> contains clustering information across spatial positions used in spatial enrichment analysis</li></ul><p><strong>STUtiility_mus_pb_ST.RDS </strong>describes seurat object generated using the STUtility package using ST data of the 38 liver sections of which the data is stored in <strong>ST_berghei_liver</strong></p><p><strong>h5ad</strong></p><p>contains anndata files of ST data (normalized read counts), spot information, distance measurements, images and masks generated using the hepaquery package. </p><p><strong>visium_berghei_liver</strong></p><p>contains data generated with the <i>spaceranger </i>pipeline and imaging using the Visium spatial transcriptomics platform. These samples include 8 sections in total, of which 1 was infected with sporozoites for 12h, 1 control section at 12h, 1 section infected with sporozoites for 24h and 1 control section at 24 as well as 2 sporozoite infected sections, and 2 control sections at 38h. </p><ul><li><i><strong>V10S29-135_A1</strong></i> contains spaceranger output for section 1 for infected and control sections at 38h post-infection</li><li><i><strong>V10S29-135_B1</strong></i> contains spaceranger output for section 1 for infected and control sections at 12h post-infection </li><li><i><strong>V10S29-135_C1 </strong></i>contains spaceranger output for section 1 for infected and control sections at 24h post-infection </li><li><i><strong>V10S29-135_D1 </strong></i>contains spaceranger output for section 2 for infected and control sections at 38h post-infection </li></ul><p><strong>se_visium.RDS </strong>describes seurat object generated using the STUtility package using ST data of the 38 liver sections of which the data is stored in <strong>visium_berghei_liver</strong></p><p><strong>snSeq_berghei_liver</strong></p><p>contains data generated with the <i>cellranger </i>pipeline and imaging using the Visium spatial transcriptomics platform. These samples include single nuclei of 2 infected and control mice after 12h, 2 infected and control mice after 24h, 2 infected and control mice after 38h, and 2 uninfected mice prior to a challenge.</p><p><i><strong>cellranger_cnt_out</strong> </i>contains feature count matrix information from cell ranger output</p><p><strong>final_merged_curated_annotations_270623.RDS </strong>describes seurat object generated using the STUtility package using ST data of the 38 liver sections of which the data is stored in <strong>snSeq_berghei_liver.tar.gz</strong></p><p><strong>raw images.zip </strong>contains raw images for supplementary figures 20-22</p><p><strong>adjusted images.zip </strong>contains brightness and contrast adjusted images for supplementary figures 20-22</p>
Comparative analyses of compatible and incompatible host-pathogen interactions provide insight into divergent host specialization of closely related pathogens
<p>The following animations accompany the manuscript "Comparative analyses of compatible and incompatible host-pathogen interactions provide insight into divergent host specialization of closely related pathogens". These animations are of confocal microscopy images of wheat leaves infected with isolates of <em>Zymoseptoria pseudodarliae </em>and <em>Z. ardabiliae, </em>at two infection stages (early and late), scrolling through different planes of focus into the wheat leaf. Two isolates of <em>Zymoseptoria pseudodarliae (</em>Zp13 and Zp72) and three isolates of <em>Zymoseptoria ardabiliae </em>(Za17, Za48, and Za94) on wheat, were screened in this study. Each isolate is expressing GFP in order to visualise hyphae. <em>Z. pseudodarliae </em>and <em>Z. ardabiliae </em>are avirulent on wheat, and infections do not pentrate beyond the stomata and into the wheat tissue. These differ from previously obtained images of <em>Z. tritici </em>(virulent on wheat), which penetrate into the wheat tissue (<a href="https://doi.org/10.1002/ece3.4724">https://doi.org/10.1002/ece3.4724</a>).</p>
Comparative analyses of compatible and incompatible host-pathogen interactions provide insight into divergent host specialization of closely related pathogens
<p><strong><span>Supplementary Animations</span></strong></p> <p><span> </span></p> <p><strong><span>Animation S1. Directed growth of <em>Z. pseudotritici</em> Zp13 hyphae towards wheat stomata 7 dpi. </span></strong><span>Tomographic animation of confocal image z-stack showing spore germination, filament development, and penetration of wheat stomata by hyphae of <em>Z. pseudotritici</em> isolate Zp13 at 7 dpi. Nuclei and wheat cells displayed in <em>purple</em> and fungal structures in <em>green</em>. Scale bar = 50 µm.</span></p> <p><span> </span></p> <p><strong><span>Animation S2. Directed growth of <em>Z. ardabiliae </em>Za17 hyphae towards wheat stomata 17 dpi. </span></strong><span>Tomographic animation of confocal image z-stack showing spore germination, filament development, and penetration of wheat stomata by hyphae of <em>Z. ardabiliae</em><strong> </strong>isolate Za17 at 17 dpi. Nuclei and wheat cells displayed in <em>purple</em> and fungal structures in <em>green</em>. Scale bar = 25 µm.</span></p> <p><span> </span></p> <p><strong><span>Animation S3. <em>Z. pseudotritici</em> Zp13 hyphae penetrate wheat stoma 14 dpi. </span></strong><span>Tomographic animation of confocal image z-stack showing penetration of wheat stoma by two hyphae of <em>Z. pseudotritici</em> isolate Zp13 at 14 dpi. Nuclei and wheat cells displayed in <em>purple</em> and fungal structures in <em>green</em>. Scale bar = 20 µm.</span></p> <p><span> </span></p> <p><strong><span>Animation S4. <em>Z. ardabiliae </em>Za94 hyphae penetrate wheat stoma 8 dpi.</span></strong><span> Tomographic animation of confocal image z-stack showing penetration of wheat stoma by two hyphae of <em>Z. ardabiliae</em> isolate Za94 at 8 dpi. Nuclei and wheat cells displayed in <em>purple</em> and fungal structures in <em>green</em>. Reference transmitted images in <em>grey</em>. Scale bar = 25 µm.</span></p> <p><span> </span></p> <p><strong><span>Animation S5.</span></strong><span> <strong><em>Z. pseudotritici </em>Zp72<em> </em>hypha arrested between guard cells 10 dpi. </strong>Tomographic animation of confocal image z-stack showing infecting hypha of <em>Z. pseudotritici</em> isolate Zp72 that is arrested at wheat stomatal guard cells at 10 dpi. Nuclei and wheat cells displayed in <em>purple</em> and fungal structures in <em>green</em>. Reference transmitted images in <em>grey</em>. Scale bar = 25 µm.</span></p> <p><span> </span></p> <p><strong><span>Animation S6. <em>Z. ardabiliae </em>Za94<em> </em>hyphae arrested between guard cells 17 dpi. </span></strong><span>Tomographic animation of confocal image z-stack showing infecting hyphae of <em>Z. ardabiliae</em> isolate Za94 that are arrested between wheat stomatal guard cells at 17 dpi. Nuclei and wheat cells displayed in <em>purple</em> and fungal structures in <em>green</em>. Scale bar = 25 µm.</span></p> <p><span> </span></p> <p><strong><span>Animation S7. <em>Z. ardabiliae </em>Za48<em> </em>hypha arrested in sub-stomatal cavity 8 dpi. </span></strong><span>Tomographic animation of confocal image z-stack showing infecting hypha of <em>Z. ardabiliae</em> isolate Za48 that is arrested in a wheat sub-stomatal cavity at 8 dpi. Nuclei and wheat cells displayed in <em>purple</em> and fungal structures in <em>green</em>. Reference transmitted images in <em>grey</em>. Scale bar = 25 µm.</span></p> <p><span> </span></p> <p><strong><span>Animation S8. <em>Z. pseudotritici </em>Zp13<em> </em>hypha arrested in sub-stomatal cavity 17 dpi. </span></strong><span>Tomographic animation of confocal image z-stack showing infecting hypha of <em>Z. pseudotritici</em> isolate Zp13 that is arrested in a wheat sub-stomatal cavity at 17 dpi. Nuclei and wheat cells displayed in <em>purple</em> and fungal structures in <em>green</em>. Scale bar = 25 µm.</span></p>
Data and Scripts for "Timing matters in Macrophage / CD4+ T cell interactions: An agent-based model comparing Mycobacterium tuberculosis host-pathogen interactions between latently infected and naïve individuals"
<p>This contains the data and graphing scripts necessary to recreate all figures in the paper "Timing matters in Macrophage / CD4+ T cell interactions: An agent-based model comparing Mycobacterium tuberculosis host-pathogen interactions between latently infected and naïve individuals". Supplemental Material for the paper is also provided here. Please refer to the README.md for instructions on how to use. The model can be found at: https://github.itap.purdue.edu/ElsjePienaarGroup/LTBINaiveinvitroModel/ along with the uncalibrated parameter files and scripts to run on HPCs.</p>
Host-pathogen Interactions, Immune Response, and Clinical Prognosis at COVID-19 - the CoVUm Trial
ClinicalTrials.gov study NCT04368013. IPD Sharing: NO. Countries: 1. Publications: 6.
Data from: Genetic variation in resistance and fecundity tolerance in a natural host-pathogen interaction
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Data from: Environmentally dependent host-pathogen and vector-pathogen interactions in the barley yellow dwarf virus pathosystem
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Data from: Thermal variability and plasticity drive the outcome of a host-pathogen interaction
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Data from: Integrated molecular imaging reveals tissue heterogeneity driving host-pathogen interactions
All diseases are characterized by distinct changes in tissue molecular distribution. Molecular analysis of intact tissues traditionally requires pre-existing knowledge of, and reagents for, the targets of interest. Conversely, label-free discovery of disease-associated tissue analytes requires destructive processing for downstream identification platforms. Tissue-based analyses therefore sacrifice discovery to gain spatial distribution of known targets, or sacrifice tissue architecture for discovery of unknown targets. To overcome these obstacles, we developed a multi-modality imaging platform for discovery-based molecular histology. We apply this platform to a model of disseminated infection triggered by the important pathogen Staphylococcus aureus, leading to the discovery of infection-associated alterations in the distribution and abundance of proteins and elements in tissue. These data provide an unbiased, three-dimensional analysis of how disease impacts the molecular architecture of complex tissues, enable culture-free diagnosis of infection through imaging-based detection of bacterial and host analytes, and reveal molecular heterogeneity at the host-pathogen interface.
Data from: Integrated molecular imaging reveals tissue heterogeneity driving host-pathogen interactions
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['Evaluating the effect of spaceflight on the host-pathogen interaction between human intestinal epithelial cells and Salmonella Typhimurium']
['Spaceflight uniquely alters the physiology of both human cells and microbial pathogens, stimulating cellular and molecular changes directly relevant to infectious disease. However, the influence of this environment on host-pathogen interactions remains poorly understood. Here we report our results from the STL-IMMUNE study flown aboard Space Shuttle mission STS-131, which investigated multi-omic responses (transcriptomic, proteomic) of human intestinal epithelial cells to infection with Salmonella Typhimurium when both host and pathogen were simultaneously exposed to spaceflight. To our knowledge, this was the first in-flight infection and dual RNA-seq analysis using human cells. Additionally, it is the first global transcriptomic and proteomic profiling of human intestinal epithelial cultures during spaceflight (either infected or uninfected).']
A next generation three-dimensional hydrogel-based culture system for studying host-pathogen interaction and drug efficacy in tuberculosis
GEO Series GSE216503. Homo sapiens; Mycobacterium tuberculosis H37Rv. 11 samples. Type: Expression profiling by high throughput sequencing.
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International Brain Laboratory public data
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OpenNeuro
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