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47 results for “fungal plant pathogens”
Supplementary Tables for the genome architecture of the fungal plant pathogens Cladosporium fulvum and Erysiphe necator and its relevance to pathogenicity
<p>This repository contains supplementary tables for the PhD disseration titled "The genome architecture of the fungal plant pathogens <em>Cladosporium fulvum</em> and <em>Erysiphe necator</em> and its relevance to pathogenicity".</p> <p> </p> <p> </p>
FIGURES 1–8 in New Mycodiplosis gall midge (Diptera: Cecidomyiidae) feeding on fungal rusts (Fungi: Pucciniomycetes) that are pathogenic on cultured plants
FIGURES 1–8. Mycodiplosis puccinivora. Male. (1) occipital protuberance on head dorsally, (2) 3rd flagellomere (3) mouth parts, (4) fore claw, (5) mid claw, (6) hind claw, (7) terminalia dorsally, (8) terminalia ventrally.
FIGURES 19–27 in New Mycodiplosis gall midge (Diptera: Cecidomyiidae) feeding on fungal rusts (Fungi: Pucciniomycetes) that are pathogenic on cultured plants
FIGURES 19–27. Mycodiplosis puccinivora feeding on fungal rust Maravalia pterocarpi infesting leaves of Dalbergia tonkinensis. 19–20: adult, 21: egg, 22–24: larva, 25–27: pupa. (19) female, (20) male, (21) egg on leaf surface, (22) mature larva spinning cocoon on leaf surface, (23) larvae feeding on uredinia, (24) larvae feeding on uredinia, arows indiacte larave in the distance, (25) young pupa, (26) mature pupa, (27) pupal exhiviae anchored in cocoon following emergence of adult. Fig. 24 is from Wang et al. (2017).
FIGURES 14–18. Mycodiplosis puccinivora. 14–17 in New Mycodiplosis gall midge (Diptera: Cecidomyiidae) feeding on fungal rusts (Fungi: Pucciniomycetes) that are pathogenic on cultured plants
FIGURES 14–18. Mycodiplosis puccinivora. 14–17: larva, 18: pupal exhuviae. (14) head in dorsal view, (15) sternal spatula with adjacent papillae, (16) terminal segment in dorsal view, (17, 18) habitus.
FIGURES 9–13 in New Mycodiplosis gall midge (Diptera: Cecidomyiidae) feeding on fungal rusts (Fungi: Pucciniomycetes) that are pathogenic on cultured plants
FIGURES 9–13. Mycodiplosis puccinivora. Female. (9) 3rd flagellomere, (10) wing, (11) hypoproct ventrally, (12) postabdomen from 7th segment to end dorsally, (13) postabdomen from 7th segment to end laterally.
Nitrogen enrichment and foliar fungal pathogens affect the mechanisms of multispecies plant coexistence
<p>This is the data and code repository for the manuscript entitled "Nitrogen enrichment and foliar fungal pathogens affect the mechanisms of multispecies plant coexistence".</p> <p>DATA:</p> <p><strong>data.txt</strong></p> <p>Dataset collected in the PaNDiv experiment, a large field experiment in Münchenbuchsee (near Bern) which investigates the mechanisms by which nitrogen enrichment affects ecosystem functioning. The dataset is composed by the following elements:</p> <ul> <li><em>year </em>--- 2017 or 2018</li> <li><em>block </em>--- experimental block in the PaNDiv experiment (1, 2, 3 or 4)</li> <li><em>plot </em>--- experimental plot in the PaNDiv experiment (from 1 to 336)</li> <li><em>nitrogen </em>--- addition of nitrogen to the soil (0 = no, 1 = yes)</li> <li><em>fungicide </em>--- application of fungicide to the vegetation (0 = no, 1 = yes)</li> <li><em>treatment</em> --- control, nitrogen addition, fungicide application, and their combined effect</li> <li><em>number</em> --- replicate number of the focal species; numbers are repeated because it restarts with each target focal-neighbour species combination (not shown)</li> <li><em>focal_sp</em> --- 8 possible species: <ul> <li>tar_off = <em>Taraxacum officinale</em></li> <li>cre_bie = <em>Crepis biennis</em></li> <li>rum_ace = <em>Rumex acetosa</em></li> <li>dac_glo = <em>Dactylis glomerata</em></li> <li>ant_odo = <em>Anthoxanthum odoratum</em></li> <li>cen_jac = <em>Centaurea jacea</em></li> <li>sal_pra = <em>Salvia pratensis</em></li> <li>pla_med = <em>Plantago media</em></li> </ul> </li> <li><em>biomass_i </em>--- initial biomass of the focal plant (start of the growing season; February/March)</li> <li><em>biomass_f</em> --- final biomass of the focal plant (end of the growing season; June)</li> <li><em>tar_off</em> --- visually estimated cover for this species as a neighbour</li> <li><em>cre_bie</em> --- visually estimated cover for this species as a neighbour</li> <li><em>rum_ace</em> --- visually estimated cover for this species as a neighbour</li> <li><em>dac_glo</em> --- visually estimated cover for this species as a neighbour</li> <li><em>ant_odo</em> --- visually estimated cover for this species as a neighbour</li> <li><em>cen_jac</em> --- visually estimated cover for this species as a neighbour</li> <li><em>sal_pra</em> --- visually estimated cover for this species as a neighbour</li> <li><em>pla_med</em> --- visually estimated cover for this species as a neighbour</li> <li><em>herbs </em>--- visually estimated cover for non-target herb species in the PaNDiv experiment</li> <li><em>grasses </em>--- visually estimated cover for non-target grass species in the PaNDiv experiment</li> <li><em>legumes </em>--- visually estimated cover for non-target legume species in the PaNDiv experiment</li> </ul> <p> </p> <p>CODE:</p> <p><strong>001-optimx.R</strong></p> <p>Code that uses maximum likelihood to fit population models to the data. Produces several datasets with model coefficients and AIC values.</p> <p> </p> <p><strong>002-model_sel_coefs.R</strong></p> <p>Code to select the coefficients based on the best model and add the size effect of the focal plants when needed. Creates all interaction matrices and intrinsic growth rate vectors.</p> <p> </p> <p><strong>003-final_matrices.R</strong></p> <p>Code to adjust the matrices for coexistence computing.</p> <p> </p> <p><strong>004-coexistence.R</strong></p> <p>Computes structural coexistence outputs. Provides a clean dataset with structural niche differences, structural fitness differences, and other multispecies coexistence metrics.</p>
Single-cell RNA-seq dataset to determine cell-type specific response to fungal pathogen infection in plant leaves
<p>Single-cell RNA-seq dataset to determine cell-type specific response to fungal pathogen infection in plant leaves</p>
Data from: The chestnut blight fungus world tour: successive introduction events from diverse origins in an invasive plant fungal pathogen.
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Data from: Shifts in diversification rates and host jump frequencies shaped the diversity of host range among Sclerotiniaceae fungal plant pathogens
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Data from: Fungal pathogen species richness: why do some plant species have more pathogens than others?
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Complex adaptive architecture underlies adaptation to quantitative host resistance in a fungal plant pathogen
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An invasive plant rapidly increased the similarity of soil fungal pathogen communities
<p><strong>Background and Aims:</strong> Plant invasions can change soil microbial communities and affect subsequent invasions directly or indirectly via foliar herbivory. It has been proposed that invaders promote uniform biotic communities that displace diverse, spatially variable communities ("Biotic Homogenization Hypothesis") but this has not been experimentally tested for soil microbial communities, so the underlying mechanisms and dynamics are unclear. Here, we compared density-dependent impacts of the invasive plant Alternanthera philoxeroides and its native congener A.sessilis on soil fungal communities, and their feedback effects on plants and a foliar beetle.</p> <p><strong>Methods: </strong>We conducted a plant-soil feedback (PSF) experiment and a lab bioassay to examine PSFs associated with the native and invasive plants and a beetle feeding on them. We also characterized the soil fungal community using high-throughput sequencing.</p> <p><strong>Key Results: </strong>We found locally differentiated soil fungal pathogen assemblages associated with high densities of the native plant Alternanthera sessilis but little variation in those associated with the invasive congener A. philoxeroides regardless of plant density. In contrast, AM fungal assemblages associated with high densities of the invasive plant were more variable. Soil biota decreased plant shoot mass but their effect was weak for the invasive plant growing in native plant conditioned soils. PSFs increased the larval biomass of a beetle reared on leaves of the native plant only. Moreover, PSFs on plant shoot, root and beetle mass were predicted by different pathogen taxa in a plant species-specific manner.</p> <p><strong>Conclusion:</strong> Our results suggest that plant invasions can rapidly increase the similarity of soil pathogen assemblages even at low plant densities leading to taxonomically and functionally homogeneous soil communities that may limit negative soil effects on invasive plants.</p>
Data from: Host plant phylogeny and abundance predict root-associated fungal community composition and diversity of mutualists and pathogens
• Interactions between plants and their root-associated fungi (RAF) may influence the relative abundance of tree species and determine forest community diversity. Such plant-soil feedbacks in turn depend on the degree to which spatial distance and phylogenetic relatedness of host trees structure pathogen and mutualist communities, but research detailing these aspects of RAF communities is lacking. Here, we characterize plant-RAF associations across a diverse plant community, focusing on the degree to which RAF communities are structured by spatial distance, host phylogenetic relatedness, and host abundance. We compare results for different functional groups, including both putative mutualists and pathogens, an aspect poorly examined hitherto. • We collected roots at regular intervals along ten 50 m by 2 m transects, then used DNA barcoding to identify host plants, and characterize the associated fungal community. Variance partitioning was used to measure the relative contributions of host phylogenetic relatedness and spatial distance to explaining RAF community composition. A weighted linear regression was used to measure the correlation between host abundance and RAF diversity. • Phylogenetic distance among hosts was a better predictor of RAF community composition than spatial distance, but this relationship was stronger for putative pathogens than for mutualists, suggesting that pathogens show stronger host preference than mutualists. Across all functional groups, RAF showed similar levels of spatial structure. Additionally, RAF communities of locally abundant plants were less diverse than RAF communities of rare plants. • Synthesis: We found that RAF communities are structured by the phylogenetic relatedness of hosts and, to a lesser extent, by spatial distance, with pathogens showing stronger host preference than mutualists. Abundant hosts had less diverse RAF communities than rare hosts, which is notable because abundant plants tend to experience weaker negative plant-soil feedback. Going forward, mechanisms underlying the host abundance-RAF diversity relationship warrant further investigation. Additionally, the survey approach presented here could be paired with experiments linking RAF community composition to plant recruitment.
Data from: Host plant phylogeny and abundance predict root-associated fungal community composition and diversity of mutualists and pathogens
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Comparative transcriptomics reveal contrasting fungal strategies in a plant pathogen versus an endophyte during initial host colonization
GEO Series GSE277627. Metarhizium anisopliae. 24 samples. Type: Expression profiling by high throughput sequencing.
Fungal plant pathogen “mutagenomics” reveals tagged and untagged mutations in Zymoseptoria tritici and identifies SSK2 as key morphogenesis and stress-responsive virulence factor
GEO Series GSE225623. Zymoseptoria tritici IPO323; Triticum aestivum. 12 samples. Type: Expression profiling by high throughput sequencing.
Global transcriptome of the fungal pathogen Sclerotinia sclerotiorum (strain 1980) during the colonization of six plant species - Global transcriptome of S. sclerotiorum and S. trifoliorum during grow
GEO Series GSE159792. Sclerotinia trifoliorum; Sclerotinia sclerotiorum 1980 UF-70. 57 samples. Type: Expression profiling by high throughput sequencing.
Combined pangenomics and transcriptomics reveals core and redundant virulence processes in a rapidly evolving fungal plant pathogen
GEO Series GSE222164. Zymoseptoria tritici; Triticum aestivum. 114 samples. Type: Expression profiling by high throughput sequencing.
Comparative transcriptomics reveal contrasting fungal strategies in a plant pathogen (Fusarium graminearum) versus an endophyte (Metarhizium anisopliae) during initial host colonization
GEO Series GSE277787. Fusarium graminearum. 24 samples. Type: Expression profiling by high throughput sequencing.
Global transcriptome of Helianthus annuus, Beta vulgaris, Ricinus communis and Phaseolus vulgaris healthy plants and plant infected by the fungal pathogen Sclerotinia sclerotiorum (strain 1980)
GEO Series GSE138039. Beta vulgaris subsp. vulgaris; Helianthus annuus; Phaseolus vulgaris; Ricinus communis. 24 samples. Type: Expression profiling by high throughput sequencing.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.