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249 results for “plant pathogen”
Data from: Contrasting effects of elevation on above and belowground plant pathogens
<p>Plant fungal diseases have a great influence on both photosynthesis and ecosystem function. However, how the elevation gradient, which is one of the most biogeographic factors, affects diseases is scarce. Here, we combined a field survey and a meta-analysis to test how elevation affect foliar fungal diseases and soil fungal pathogens through different paths. We arranged 30 plots along 3200 m ~ 4000 m in a Qinghai-Tibetan alpine meadow and collected the data of foliar fungal diseases, plant composition and soil properties to study how environment-mediated (through changes in temperature and humidity), plant community-mediated (through changes in plant biomass, richness, evenness, phylogenetic structure and community composition) and soil mediated (through changes in soil properties) effects of elevation on foliar fungal diseases and soil fungal pathogens. Based on linear models, we found that elevation decreased soil fungal pathogen richness rather than community pathogen load of foliar diseases. Specifically, a combination of community proneness and Pielou's evenness index was the best model in predicting pathogen load. The structural equation model further confirmed that although elevation significantly changed both the plant community indices and soil properties, elevation mainly drove pathogen load via plant community-mediated effects, but decreased soil fungal pathogen richness through temperature. A systematic meta-analysis composed of 48 studies from 31 literatures confirmed our main conclusions that elevation did not significantly foliar fungal diseases, but decreased soil fungal pathogen richness significantly, indicating contrast effects of elevation in driving above- and belowground plant pathogens. Hence, we distinguished the different mechanisms for different parts of the plant pathogens in one system, and our study will improve the predictability of plant diseases, especially under the background of global climate change.</p>
Pollinators adjust their behavior to presence of pollinator-transmitted pathogen in plant population
<p>Interactions between pollinators and plants can be affected by the presence of plant pathogens that substitute their infectious propagules for pollen in flowers and rely on pollinators for transmission to new hosts. However, it is largely unknown how pollinators integrate cues from diseased plants such as altered floral rewards and floral traits, and how their behavior changes afterward. Understanding pollinator responses to diseased plants is crucial for predicting both pathogen transmission and pollen dispersal in diseased plant populations. In this study, we investigated pollinator responses to contact with plants of <i>Dianthus carthusianorum</i> diseased with anther smut (<i>Microbotryum carthusianorum</i>). We combined three approaches: 1) observation of individual pollinators foraging in experimental arrays of pre-grown potted plants; 2) measurements of floral rewards and floral traits of healthy and diseased plants; and 3) quantification of pollen/spore loads of pollinator functional groups. We found that pollinators showed only weak preferences for visiting healthy over diseased plants, but after landing on plants, they probed fewer flowers on the diseased ones. Since diseased flowers offered lower nectar and no pollen rewards, this behavior is consistent with the prediction of optimal foraging models that pollinators should spend less time exploring less rewarding patches or plants. Furthermore, pollen-foraging solitary bees and hoverflies responded to diseased plants more negatively than nectar-feeding butterflies did. Lastly, based on group-specific behavior and typical pollen/spore loads, we suggest that solitary bees and hoverflies contribute to both pollen and pathogen spore dispersal mainly over short distances, while butterfly visits are most important for long-distance dispersal.</p>
Complex adaptive architecture underlies adaptation to quantitative host resistance in a fungal plant pathogen
<p>Plant pathogens often adapt to plant genetic resistance so characterization of the architecture underlying such an adaptation is required to understand the adaptive potential of pathogen populations. Erosion of banana quantitative resistance to a major leaf disease caused by polygenic adaptation of the causal agent, the fungus <i>Pseudocercospora fijiensis,</i> was recently identified in the northern Caribbean region<i>. </i>Genome scan and quantitative genetics approaches were combined to investigate the adaptive architecture underlying this adaptation. Thirty-two genomic regions showing host selection footprints were identified by pool sequencing of isolates collected from seven plantation pairs of two cultivars with different levels of quantitative resistance. Individual sequencing and phenotyping of isolates from one pair revealed significant and variable levels of correlation between haplotypes in 17 of these regions with a quantitative trait of pathogenicity (the diseased leaf area). The multilocus pattern of haplotypes detected in the 17 regions was found to be highly variable across all the population pairs studied. These results suggest complex adaptive architecture underlying plant pathogen adaptation to quantitative resistance with a polygenic basis, redundancy, and a low level of parallel evolution between pathogen populations. Candidate genes involved in quantitative pathogenicity and host adaptation of <i>P. fijiensis </i>were identified in genomic regions by combining annotation analysis with available biological data.</p>
Fig. 2 in Pseudomonas capsici sp. nov., a plant-pathogenic bacterium isolated from pepper leaf in Georgia, USA
Fig. 2. Phylogenomic relationships between Pseudomonas capsici sp. nov. strains and closely related Pseudomonas species listed in Table 1. The tree was generated with FastME 2.1.6.1 [22] from GBDP distances calculated from genome sequences on the TYGS [19]. The branch lengths are scaled in terms of GBDP distance formula d5. The numbers at nodes are genome BLAST distance phylogeny approach pseudo-bootstrap support values (>60%) from 100 replications, with an average branch support of 94.9%. The tree was rooted at the midpoint [29]. GenBank accession numbers are shown within parentheses, with T indicating type strains.
Fig. 1 in Pseudomonas capsici sp. nov., a plant-pathogenic bacterium isolated from pepper leaf in Georgia, USA
Fig. 1. Phylogenetic relationships based on partial gene sequences of 16S rRNA between Pseudomonas capsici sp. nov. strains and closely related Pseudomonas species listed in Table 1. The 16S rRNA gene sequences (1266 nucleotides) were aligned using MAFFT (version 7.294b) [10]. The alignment was used to construct a phylogenetic tree using the PHYML package with the maximum-likelihood method and with the best substitution model estimated by jmodelTest version 2.1.10 [12]. The clade including Pseudomonas viciae, Pseudomonas brassicacearum and Pseudomonas mediterranea was used for outgroup rooting. Numbers at nodes represent bootstrap values from 1000 replicates. Bar, 1 nt substitution per 100 nt. GenBank accession numbers are shown within parentheses along with the strain, with T indicating type strains.
Raw data: Soil microbes drive aboveground plant–pathogen–insect interactions
<p class="MsoNormal"><span>Plants interact with a large diversity of microbes and insects, both below and above ground. While studies have shown that belowground microbes affect the performance of plants and aboveground organisms, we lack insights into how belowground microbial communities may shape interactions between aboveground pathogens and insects. We investigated how soil microbiomes and aboveground organisms affect plant growth and development, and whether differences in soil microbiomes influence interactions between aboveground organisms. We conducted a growth-chamber experiment with oak seedlings (<em>Quercus robur</em>) growing in three soils with similar abiotic soil properties but with distinct natural soil microbiomes.</span> Seedlings were subjected to single or dual attack by powdery mildew (<em>Erysiphe alphitoides</em>) and aphids (<em>Tuberculatus annulatus</em>), either in the presence or absence of prior attack by a free-feeding caterpillar (<em>Phalera bucephala</em>). <span>Soil microbiomes were associated with differences in seedling height, and seedlings with multiple aboveground organisms had more but smaller leaves than healthy seedlings. The soil microbiome affected the severity of powdery mildew infection, and mediated the impact of co-occurring aboveground organisms on aphid population size. Our study highlights that plant performance is affected by natural soil microbiomes as well as aboveground organisms, and that natural soil microbiomes can affect interactions between pathogens and insects. These findings are important to understand species interactions in natural systems, as well as for practical applications, such as manipulation of soil microbiomes to manage agricultural pests and diseases.</span></p>
Plant pathogen-mediated rapid acclimation of a host-specialized aphid to a non-host plant
<p>Polyphagous aphids often consist of host-specialized lineages which have greater fitness on their native hosts than on others. The underlying causes are important for understanding of the evolution of diet breadth and host shift of aphids. The cotton-melon aphid Aphis gossypii Glover is extremely polyphagous with many strict host-specialized lineages. Whether and how the lineage specialized on the primary host hibiscus shifts to the secondary host cucumber remains elusive. We found that the hibiscus-specialized lineage suffered high mortality and gave birth to very few nymphs developing into yellow dwarfs on fresh cucumber leaves, and did not inflict any damage symptoms on cucumber plants. The poor performance did not improve with prolonged exposure to cucumber; however, it did significantly improve when the cucumber leaves were pre-infected with a biotrophic phytopathogen Pseudoperonospora cubensis. More importantly, the hibiscus-specialized lineage with two-generation feeding experience on pre-infected cucumber leaves performed as well as the cucumber-specialized lineage did on fresh cucumber leaves, and inflicted typical damage symptoms on intact cucumber plants. Electrical penetration graph (EPG) indicated that the hibiscus-specialized lineage did not ingest phloem sap from fresh cucumber leaves but succeeded in ingesting phloem sap from pre-infected cucumber leaves, which explained the performance improvement of the hibiscus-specialized lineage on pre-infected cucumber leaves. This study revealed a new pathway for the hibiscus-specialized lineage to quickly acclimate to cucumber under the assistance of the phytopathogen. We considered that the short feeding experience on pre-infected cucumber may activate expression of effector genes that are related to specific host utilization. We suggest to identify host-specific effectors by comparing proteomes or/and transcriptomes of the hibiscus-specialized lineage before and after acclimating to cucumber.</p>
ROS and SGI data for manuscript "The perception and evolution of flagellin, cold shock protein, and elongation factor Tu from vector-borne bacterial plant pathogens"
<p>This contains raw data for the ROS and seedling growth inhibition (SGI) assays collected for the manuscript "The perception and evolution of flagellin, cold shock protein, and elongation factor Tu from vector-borne bacterial plant pathogens". For a quick reference, there are two spreadsheets listing all the Max RLUs and Z-scores for the experiments, but the actual output of each plate reader is also included. </p>
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.
FIGURE 11 in A taxonomic and phylogenetic re-appraisal of the genus Curvularia (Pleosporaceae): human and plant pathogens
FIGURE 11. Curvularia subpapendorfii (CBS 656.74). A–C) Conidiophores and conidia D–I) Conidia J) Germinating conidia. (A–C = 5 μm D = 10 μm E–J = 5 μm).
FIGURE 10 in A taxonomic and phylogenetic re-appraisal of the genus Curvularia (Pleosporaceae): human and plant pathogens
FIGURE 10. Curvularia ryleyi (IMI 261918).A) Conidiophores and conidia on the inflorescence of Sporobolus creber B–D) Conidiophores E–I) Conidia. (Scale bars A = 500 μm, B–D = 5 μm E–I = 10 μm).
FIGURE 9. Curvularia nodulosa BPI 626679 A in A taxonomic and phylogenetic re-appraisal of the genus Curvularia (Pleosporaceae): human and plant pathogens
FIGURE 9. Curvularia nodulosa BPI 626679 A). Ascoma produced by pairing culture on Hordeum vulgare B) A cross section of the ascoma C) Asci and pseudoparaphysis D–H) Asci I) Conidiophores J–N) Conidia. (Scale bars A = 500μm B = 100 μm C = 50 μm D–H = 20 μm I–N = 10 μm).
FIGURE 8 in A taxonomic and phylogenetic re-appraisal of the genus Curvularia (Pleosporaceae): human and plant pathogens
FIGURE 8. Curvularia nicotiae (CBS 655.74). A) Conidiophore and conidia B–E) Conidia (All scale bars = 5 μm).
FIGURE 7 in A taxonomic and phylogenetic re-appraisal of the genus Curvularia (Pleosporaceae): human and plant pathogens
FIGURE 7. Curvularia neergaardii (IMI 335219). A) Ascomata on Dactyloctenium aegyptium leaves B) Ascomata C) Cross section of ascomata D) Asci and pseudoparaphyses E–G) Asci. (Scale bars A = 200 μm B = 100 μm C, D = 20 μm E = 5 μm F,G = 10 μm).
FIGURE 3 in A taxonomic and phylogenetic re-appraisal of the genus Curvularia (Pleosporaceae): human and plant pathogens
FIGURE 3. Curvularia buchloës (BPI 428763, BPI 428756). A) Conidiophores on Buchloë dactylidis B) Conidia on Buchloë dactylidis C, D) Conidiophores E–K) Conidia. (Scale bars A = 500 μm b = 100 μm C = 10 μm D–K = 5 μm).
FIGURE 6 in A taxonomic and phylogenetic re-appraisal of the genus Curvularia (Pleosporaceae): human and plant pathogens
FIGURE 6. Curvularia neoindica (IMI 129790). A) Conidia and conidiophores B) Conidia germinating from both ends C) Conidia. (Scale bars A, B = 5 μm C = 10 μm).
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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.