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23 results for “Plant disease resistance”
Silica Nanoparticles Enhance Disease Resistance in Arabidopsis Plants - RAW DATA
<p>These datasets are used to produce the figures/graphs published in our article</p> <p><strong>Silica Nanoparticles Enhance Disease Resistance in <em>Arabidopsis</em> Plants</strong></p> <p>in <em>Nat. Nanotechnol.</em> (2020). <a href="https://doi.org/10.1038/s41565-020-00812-0">https://doi.org/10.1038/s41565-020-00812-0</a></p> <p> </p><p><strong>Correspondence: </strong></p> <p></p> <p>fabienne.schwab@alumni.ethz.ch, Tel: +41 78 736 00 19;</p> <p>m.shetehy@uky.edu, Tel. +41 76 455 56 02</p> <p>Further raw data related to qPCR and microbiology are available upon reasonable request from M.H. El‑Shetehy.</p> <p>Further raw data related to the nanoparticles and plant microscopy are available upon reasonable request by F. Schwab.</p> <p> </p> <p><strong>Abstract</strong></p> <p>In plants, pathogen attack can induce an immune response known as systemic acquired resistance (SAR) that protects against a broad spectrum of pathogens. In the search for safer agrochemicals, silica nanoparticles (SiO<sub>2</sub>‑NPs, food additive E551) have recently been proposed as a new tool. However, initial results are controversial, and the molecular mechanisms of SiO<sub>2</sub>‑NP-induced disease resistance are unknown. Here, we show that SiO<sub>2</sub>‑NPs, as well as soluble orthosilicic acid (Si(OH)<sub>4</sub>), can induce SAR in a dose-dependent manner, that involves the defence hormone salicylic acid. Nanoparticle uptake and action occurred exclusively through stomata (leaf pores facilitating gas exchange) and involved extracellular adsorption in leaf air spaces of the spongy mesophyll. In contrast to treatment with SiO<sub>2</sub>‑NPs, induction of SAR by Si(OH)<sub>4 </sub>was problematic, since high concentrations caused stress. We conclude that SiO<sub>2</sub>‑NPs have the potential to serve as an inexpensive, highly efficient, safe, and sustainable alternative for plant disease protection.</p>
Can disease resistance evolve independently at different ages? Genetic variation in age-dependent resistance to disease in three wild plant species
<p>1. Juveniles are typically less resistant (more susceptible) to infectious disease than adults, and this difference in susceptibility can help fuel the spread of pathogens in age-structured populations. However evolutionary explanations for this variation in resistance across age remain to be tested.</p> <p>2. One hypothesis is that natural selection has optimized resistance to peak at ages where disease exposure is greatest. A central assumption of this hypothesis is that hosts have the capacity to evolve resistance independently at different ages. This would mean that hosts populations have a) standing genetic variation in resistance at both juvenile and adult stages, and b) that this variation is not strongly correlated between age-classes so that selection acting at one age does not produce a correlated response at the other age</p> <p>3. Here we evaluated the capacity of three wild plant species (Silene latifolia, S. vulgaris, and Dianthus pavonius) to evolve resistance to their anther-smut pathogens (Microbotryum fungi), independently at different ages. The pathogen is pollinator-transmitted, and thus exposure risk is considered to be highest at the adult flowering stage.</p> <p>4. Within each species we grew families to different ages, inoculated individuals with anther smut, and evaluated the effects of age, family and their interaction on infection.</p> <p>5. In two of the plant species, S. latifolia and D. pavonius, resistance to smut at the juvenile stage was not correlated with resistance to smut at the adult stage. In all three species, we show there are significant age*family interaction effects, indicating that age-specificity of resistance varies among the plant families.</p> <p>6. Synthesis: These results indicate that different mechanisms likely underlie resistance at juvenile and adult stages and support the hypothesis that resistance can evolve independently in response to differing selection pressures as hosts age. Taken together our results provide new insight into the structure of genetic variation in age-dependent resistance in three well-studied wild host-pathogen systems.</p>
Figure 9 in Evaluation of the Chilli veinal mottle virus CP gene expressing transgenic Nicotiana benthamiana plants for disease resistance against the virus
Figure 9. ELISA plate readings (O.D at 405nm) of leaf samples of transgenic lines (T1-T8) and control plants after 15 dpi.
Figure 7 in Evaluation of the Chilli veinal mottle virus CP gene expressing transgenic Nicotiana benthamiana plants for disease resistance against the virus
Figure 7. PCR Products of the hpt gene from T0 transgenic plants. Lane 1-9 are transgenic. Lane 10, +ve control. Lane 11, control (untransformed) plant.
Figure 6. DNA bands from T0 in Evaluation of the Chilli veinal mottle virus CP gene expressing transgenic Nicotiana benthamiana plants for disease resistance against the virus
Figure 6. DNA bands from T0 transgenic and Agro-infilterated plants. Lane 1-10, transgenic plants. Lane 11-13, agro-infilterated plants.
Figure 3 in Evaluation of the Chilli veinal mottle virus CP gene expressing transgenic Nicotiana benthamiana plants for disease resistance against the virus
Figure 3. Symptoms development on propagative host plants after mechanical inoculation with ChiVMV isolate ATIPK. (a) N. tabacum showing mosaic, mottling and vein clearing (b) C. annum (cv. Loungi) displays the symptoms of mottling, mosaic, leaf deformation and vein clearing.
Figure 2 in Evaluation of the Chilli veinal mottle virus CP gene expressing transgenic Nicotiana benthamiana plants for disease resistance against the virus
Figure 2. Symptoms of ChiVMV on chilli leaves collected from Islamabad. (a) Shows mottling and severe vein clearing and distortion. (b) Shows reduced leaf size with mottling and distortion.
Can disease resistance evolve independently at different ages? Genetic variation in age-dependent resistance to disease in three wild plant species
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Microbiome network connectivity and composition linked to disease resistance in strawberry plants
<p>The two R data files correspond phyloseq objects used in the study. The file phyloseq_nochim_silva.RData and phyloseq_nochim_unite.RData correspond to bacterial and fungal phyloseq objects, respectively. Both objects include count table, taxonomy table and metadata information. </p>
Durable resistance or efficient disease control? Adult Plant Resistance (APR) at the heart of the dilemma [Dataset]
<p>Dataset of simulation results used in this study (https://doi.org/10.24072/pcjournal.271):</p> <p><strong>Durable resistance or efficient disease control? Adult Plant Resistance (APR) at the heart of the dilemma.</strong></p> <p>published in Peer Community Journal.</p> <p> </p> <p>All simulations are computed using the R package landsepi<br> Package webpage: https://csiro-inra.pages.biosp.inrae.fr/landsepi/<br> To download the package: https://cran.r-project.org/web/packages/landsepi/index.html</p> <p>Raw data used for this study are stored in three folders corresponding to<br> the three numerical experiments (APR_1, APR_2 and APR_3, respectively).</p> <p>Every folder includes:<br> - an R script for the generation of a simulation plan<br> - 10 folders containing simulation results for 10 stochastic replicates<br> - an R script for the analysis of the results and generation of graphics</p> <p>Every folder containing the simulation results includes:<br> - a text file "myDesign.txt" containing the simulation plan and outputs (1 line per simulation)<br> - a text file "parameters.txt" containing a summary of all parameter values<br> - a text file "param_disp_patho.txt" containing the dispersal matrix of the pathogen<br> - a text file "param_landscape.txt" containing the landscape structure<br> (i.e. for every polygon [lines] and every year [columns] the index of the cultivated host)</p>
Wheat Enhanced Disease Resistance EMS-Mutants Include Lesion-mimics With Adult Plant Resistance to Stripe Rust
<h3>The datasets for '<strong>Wheat Enhanced Disease Resistance EMS-Mutants Include Lesion-mimics With Adult Plant Resistance to Stripe Rust</strong>'.</h3> <p> </p> <p><strong>Main datasets</strong></p> <ul> <li>Kr620_GATK.zip</li> <li>Kr620_QTLseq.zip</li> </ul> <p><strong>Kr620_GATK.zip</strong> includes vcf files generated by mapping wild type Kronos and resistant or susceptible Kr620 into the Kronos genome. <strong>Kr620_QTLseq.zip </strong>includes the bulk segregation analysis outputs using QTL-seq. </p>
Figure 10 in Evaluation of the Chilli veinal mottle virus CP gene expressing transgenic Nicotiana benthamiana plants for disease resistance against the virus
Figure 10. Symptoms development in transgenic plants after challenging with ChiVMV isolate ATIPK at15dpi. A) Resistance transgenic lines. B) Moderately resistance transgenic lines. C) Moderately susceptible transgenic lines.
Figure 8 in Evaluation of the Chilli veinal mottle virus CP gene expressing transgenic Nicotiana benthamiana plants for disease resistance against the virus
Figure 8. PCR products from transgenic plants. (a) Lane 1-3, represent PCR product from agro-infilterated plants. Lane 4, control plant. (b) Lane 1-9, RT- PCR product from transgenic plants represent the presence of CP gene transcription. Lane 10, represents untransformed control sample.
Figure 5 in Evaluation of the Chilli veinal mottle virus CP gene expressing transgenic Nicotiana benthamiana plants for disease resistance against the virus
Figure 5. Maximum likelihood tree of ChiVMV isolate ATIPK on nucleotide sequence of CP gene with 22 ChiVMV isolates from the world. The bootstrap analysis was conducted in 1000 replications. ZYMV (AB127936) sequence is used as out-group.
Figure 4 in Evaluation of the Chilli veinal mottle virus CP gene expressing transgenic Nicotiana benthamiana plants for disease resistance against the virus
Figure 4. RT-PCR amplification of the CP gene with CVMV1037/ oligo(dT). Lane 1, 2, 3 and 4 contains 1.2kb amplified product of NIb and CP gene.
Accompanying images to the paper NLR immune receptor–nanobody fusions confer plant disease resistance
<p>The following images accompany the paper <strong>NLR immune receptor–nanobody fusions confer plant disease resistance</strong>, Kourelis J., Marchal C., Posbeyikian A., Harant A., Kamoun S. (2023) Science Vol. 379, No. 6635. <a href="https://doi.org/10.1126/science.abn4116">DOI: 10.1126/science.abn4116</a></p> <p> </p> <p><strong>Figure captions:</strong></p> <p><strong>Figure 1. </strong>Leaves photographed 12 days after infection with a Potato Virus X variant expressing the fluorescent protein EGFP. Left: <em>Nicotiana benthamiana</em> leaf that doesn't express the pikobody system. Right: Leaf from <em>N. benthamiana</em> line stably expressing the Pikobody system recognizing EGFP. </p> <p><strong>Figure 2. </strong>Leaves photographed 12 days after infection with a Potato Virus X variant expressing the fluorescent protein mCherry. Both leaves correspond to a <em>N. benthamiana</em> line stably expressing the Pikobody system recognizing EGFP. These results accompany the observations that the Pikobody system targetting EGFP specifically recognizes EGFP, but not mCherry.</p> <p><strong>Figure 3. </strong>Side by side comparison of representative adult <em>N. benthamiana</em> plants, from left to right: wild-type (WT), stably transformed with the rice NLR pair Pikm-1/2 (Pikm) or Pikobody<sup>Enhancer </sup>(Lines #4 - #1 - #9 - #10). </p>
Data from: Food plant-derived disease tolerance and resistance in a natural butterfly-plant-parasite interaction
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Volatile organic compounds emitted by the biocontrol agent Pythium oligandrum contribute to ginger plant growth and disease resistance
GEO Series GSE235182. Zingiber officinale. 6 samples. Type: Expression profiling by high throughput sequencing.
Comparative transcriptomics reveals the different gene expression pattern related to wilt disease resistance and susceptibility in biodiesel plants of Vernicia
GEO Series GSE80228. Vernicia montana; Vernicia fordii. 24 samples. Type: Expression profiling by high throughput sequencing.
A MKP-MAPK protein phosphorylation cascade controls vascular disease resistance in plants
GEO Series GSE161152. Arabidopsis thaliana. 12 samples. Type: Expression profiling by high throughput sequencing.
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Allen Brain Atlas
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International Brain Laboratory public data
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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.