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249 results for “plant pathogen”

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zenodo44/100

Revealing real-time 3D in vivo pathogen dynamics in plants by label-free optical coherence tomography

This repository contains all data and code underlying the publication: J. de Wit et al. "Revealing real-time 3D in vivo pathogen dynamics in plants by label-free optical coherence tomography" in Nature Communications (2024) (https://doi.org/10.1038/s41467-024-52594-x) --------------Code description------------------ The set of scripts is largely organized around the figures. For each (sub)figure, also from supplementary materials, that involves data and plotting, there is a script that generates the plot from data that can be found in the different zip files that are present in the Zenodo repository under https://doi.org/10.5281/zenodo.11428245. The scripts use the data that is contained in the ZIP folders. The ZIP folders are organized by experiment (Experiment 1, including contrast optimization; Experiment 2), one for the other data (OtherData, the validation for with Trypan blue, and the Arabidopsis, Radish and nematode) and one as a smaller dataset to explain the method on a single B-scan (Example_Bscan_dynamicOCT). IMPORTANT: The folder where the ZIP files are unzipped should be put in the file 'basepath.txt', such that the data can be automatically loaded. Besides the figures that mention 'MakeFig...' there are a few more scripts: <ul> <li>pointcloud_generation_experiment1.py: this file makes the point clouds from the dynamic OCT images as described in Fig 2b. The resulting data is saved as maximum intensity projections and axial sums(forming the basis for Fig S4, S6 and S7) and as voxel counts (forming the basis of Fig.2c and Fig S5)</li> <li>pointcloud_generation_timelapses.py: this file does the segmentation for experiment 2 and saves the maximum intensity projections and axial sums of the different stages in the segmentation (forming the basis of Fig3a,d,e and FigS9a,b), and saves the point clouds of the data. These point clouds were refined manually in CloudCompare as described in methods. These segmented point clouds are contained in the data zip folder of experiment 2.</li> <li>StatisticalTests.R: This R file calculates the statistical tests for Fig.2cd and Fig.S5d. Here the path is not automatically updated, and should be manually set. The input file is contained in "Experiment1/SegmentationData/segmentationdata_samples.csv" and the output of the file is "D:/DataZenodo/Experiment1/SegmentationData/data_combined_Rstats_output.csv"</li> <li>example_dynamic_Bscan.py: This script gives an example of the dynamic OCT processing as proposed in this paper. First it shows the process from an OCT interference spectrum to a B-scan. Then it loads 100 B-scans and applies dynamic OCT, including normalization with histograms. Finally it gives a dynamic B-scan and plots this against the average normal OCT image. This script can be used with only the zip folder "Example_Bscan_dynamicOCT", which reduces the amount of data needed to download/unzip.</li> </ul> The list of other script files to load the data and generate the figures (guiding to the path of uncropped figures) is: <ul> <li>MakeFig1bce_Fig2e.py</li> <li>MakeFig1d.py</li> <li>MakeFig1agraphs_FigureS1.py</li> <li>MakeFig3acde_S9ab.py</li> <li>MakeFigS2_determine_dynamic_range_experiment1.py</li> <li>MakeFigS8.py</li> <li>MakeFigureS4-S6-S7.py</li> <li>MakeFigureS5.py</li> <li>MakeHistFig2b_makeFigS3b-e.py</li> <li>MakePlotsFig2ab.py</li> <li>MakePlotsFig2cd.py</li> </ul> Code was all run in Python 3 using Anaconda Spyder. Moreover, the zip file with the code contains the folder 'figures' with all subfigures. Some of them are automatically saved from the scripts, others (like photos, icons, but also the Trypan blue microscopy figure) are added in the respective folder. The are logically organized by figure number. --------------Dataset Description----------------- As mentioned above, the data is organized in four zip folders for both experiments, the other data (validation with Trypan blue, other plant-pathogens) and one for the dynamic OCT B-scan example. The data contain the following: Experiment 1:  <ul> <li>DynamicOCTimages whose subfolders (organized by date) contain a folder per volume dataset in experiment 1 with a z-stack of .tif files that form the imaged volume. The lateral sampling is 3 um and the axial sampling is 1.37 um. </li> <li>ContrastOptimization: This folder contains  <ul> <li>Bscans_with_segmentation: segmented B-scans for contrast optimization (Fig S3)</li> <li>Bscan_figS1_fig1: The B-scans and segementation for Figure S1.</li> <li>histogramdata_dynamicrange: The histograms, bins and deducted reference data for determining the dynamic range per color channel for experiment 1 (Fig S2)</li> <li>logcompressed_3value_dOCT_example: An example data stack for obtaining histograms (see script MakeFigS2_determine_dynamic_range_experiment1.py)</li> <li>overlaps_threshold-100-98-95-92-90-85-80-75-70-65-60-55-50-45perc_red-1_2_blue_-3_0_green1_filt.npy: A file with intermediate data for the contrast optimization, which can also be generated with the script "MakeHistFig2b_makeFigS3b-e.py"</li> </ul> </li> <li>SegmentationData: This folder contains  <ul> <li>MIP_segmentation_stages: maximum intensityp projections and axial sums for all images at different stages in the segmentation (basis for Fig S4,6,7)</li> <li>processed_masks and StackMasks: the manually obtained masks (segmented in StackMasks, made into masks in the folder 'processed_masks') for filtering out stomata, veins and artefacts.</li> <li>Unmasked_axialsum_th34_formanualsegmentation: This folder contains the images of Fig.S4 and were used for the segmentation (we addes a small offset, such that the in segmentation we could set it to 0 and have a unique mask). </li> <li>overview_samples_bremiayn.csv: A dataframe with the data for all the samples in experiment 1 that is used as input for the segmentation. It also contains the result of the manual check whether it has infection (Fig2c, left).</li> <li>segmentationdata_samples.csv: This supplements the file of overview_samples_bremiayn.csv with the results from the segmentation and is output to script "pointcloud_generation_experiment1.py". It forms the basis of Fig.2a-c, and FigS5, as well as for the R-script to do the statistical testing.</li> <li>qPCR_dOCT.csv: This script contains the qPCR data and is input to Fig2d. </li> </ul> </li> </ul> Experiment 2: <ul> <li>DynamicOCTimages: This contains the z-stacks of .tif files of the volumes for experiment 2 (and one extra, where a z-slice is used in Fig.1b, bottom). Sampling step size is here again 3 um in lateral direction and 1.37 um in axial direction.</li> <li>.npy files with the histograms (with same bins as Experiment 1), maxvalues and reference values for the dynamic range calculation.</li> <li>segmentation_data: this folder contains: <ul> <li>quantification_volume_disc160_33_10.csv and quantification_volume_disc160_33_10.xlsx: data from the manually segmented point clouds that form the basis of Fig.3c.</li> <li>timelapse_sampleoverview.csv: overview of the samples that is used as input in the file "pointcloud_generation_timelapses.py"</li> <li>pointclouds: Folder with segmented point clouds for the three leaf discs. These files could  be loaded in CloudCompare.</li> <li>overviewMIPs: folder with overview maximum intensity projections for the different steps in segmentation, which also forms the input of Fig.3a, FigS9ab.</li> <li>rawpointclouds: folder with the automatically generated point clouds from file pointcloud_generation_timelapses.py which were imported into CloudCompare as the basis for the segmented point clouds.</li> </ul> </li> </ul> OtherData: This folder contains the z-stacks of dynamic OCT tif images for Arabidopsis (here both a normal contrast and one that has been increased to only contain the original 0-180 range); nematodes, radish (called radijs_test_PP_py_0002), spores for Fig1c (SporesImaging) and the dynamic OCT image of Fig1d.  example_Bscan_dynamicOCT: This folder contains data to run the script example_dynamic_Bscan.py to show the dynamic OCT imaging process from raw OCT spectra. <ul> <li>raw_spectra_exampleframe: contains interference spectra, a reference spectrum and interpolation grid to show how to get from a raw OCT spectrum to a normal single B-scan.</li> <li>abs_images: contains 100 subsequent B-scans that can be used to generate a dynamic OCT image as done in example_dynamic_Bscan.py</li> </ul>

opencc-by-4.0Aug 2024View details →

edi44/100

Decomposition of Microstegium vimineum litter, plants grew through the Big Oaks National Wildlife Refuge in 2019. Litter used in this experiment naturally senesced in the fall 2019, decomposition data collected through 2020. Plants were infected or not-infected with the foliar fungal pathogen Bipolaris gigantea during the 2019 growing season.

Decomposition of plant litter, facilitated primarily by microbial decomposers, plays a critical role in biogeochemical cycling and ecosystem function. Emerging pathogens have the potential to impact litter decomposition by altering the chemical composition and associated microbial community of host tissue. Here, we compared litter decomposition of the invasive grass Microstegium vimineum collected from sites with Bipolaris leaf spot symptoms and sites with no apparent disease symptoms in a common garden experiment. Our results revealed that leaf tissue from litter from non-infected sites decomposed more rapidly through the spring than litter from infected sites. Differences in fungal composition between infected and non-infected litter at the start of the experiment largely persisted through the summer. Our work demonstrates that pathogen colonization may facilitate the persistence of infected host litter, potentially slowing the return of nutrients to the environmental pool while also promoting the survival and dispersal of primary inoculum the following season.

openCC (other)Jun 2023View details →

edi44/100

Emerging fungal pathogen of an invasive grass: Implications for competition with native plant species

This data package includes data and code from an experiment testing the effects of a leaf spot fungal infection and competition from the invasive (to the U.S.) grass Microstegium vimineum on the performance of three native grass species: Dichanthelium clandestinum, Elymus virginicus, and Eragrostis spectabilis. The experiment was performed between June and September of 2019 in a greenhouse on the University of Florida campus in Gainesville, FL, USA. The leaf spot infection is caused by the fungal pathogen Bipolaris gigantea, which has recently emerged on populations of M. vimineum in the U.S. We tested the hypothesis that infection of B. gigantea would both directly and indirectly affect the native grass species by measuring the change in biomass of each species with and without pathogen inoculation (direct effects) and by measuring the effect of pathogen inoculation on M. vimineum competition through changes in native grass biomass across a density gradient of M. vimneum (indirect effects). The code includes statistical analyses and figures. The code was run using R (version 4.0.1).

openCC (other)Feb 2021View details →

dryad40/100

Phage selection drives resistance-virulence trade-offs in Ralstonia solanacearum plant pathogenic bacterium irrespective of the growth temperature

While temperature has been shown to affect the survival and growth of bacteria and their phage parasites, it is unclear if trade-offs between phage resistance and other bacterial traits depend on the temperature. Here, we experimentally compared the evolution of phage resistance-virulence trade-offs and underlying molecular mechanisms in phytopathogenic Ralstonia solanacearum bacterium at 25 °C and 35 °C temperature environments. We found that experimental growth conditions selected for small colony variants (SCVs) with increased growth rate and mutations in the quorum-sensing (QS) signalling receptor gene, phcS. Interestingly, SCVs were also phage-resistant and reached higher frequencies in the presence of phages in both temperature environments. Evolving phage resistance was costly in terms of reduced carrying capacity, biofilm formation and reduced virulence in planta possibly due to loss of QS-mediated expression of key virulence genes. We also observed mucoid phage-resistant colonies that showed loss of virulence and reduced twitching motility likely due to parallel mutations in prepilin peptidase gene pilD. Moreover, phage-resistant SCVs from 35 °C-phage treatment had parallel mutations in genes encoding type II secretion system (T2SS) genes (gspE and gspF), indicating that defects in pseudopilus made bacterium resistant to the phage. Additional transcriptomic analysis revealed upregulation of CBASS and type Ⅰ restriction-modification phage defence systems in response to phage exposure, which coincided with reduced expression of motility and virulence-associated genes, including pilD and type II and III secretion systems. Together, these results suggest that phage resistance-virulence trade-offs are not affected by the growth temperature but can be mediated through both pre- and post-infection phage resistance mechanisms.

opencc-zeroNov 2023View details →

dryad40/100

Ant handling changes myrmecochore seed coat microbiomes and alters diversity of seed-borne plant pathogenic fungi

The putative benefits to seeds in myrmecochory (ant-mediated seed dispersal) are often cast in a reward context. However, microbes have been mostly overlooked as seed mortality agents in myrmecochory, as have potential treatments provided by ant-handling. We investigated the effects of ant handling on the diversity of seed coat fungal communities of three myrmecochorous plant species. Ant-handling altered measures of both alpha and beta diversity of fungal communities. Ant-handled seeds harbored different overall fungal communities and plant pathogen communities than non-ant-handled seeds. The myrmecochore pathogenic fungal community showed high dissimilarity (high pairwise community turnover) between ant-handled and control seeds, while beta diversity measures for ant-handled seeds and seeds with manually-removed elaiosomes were less dissimilar. Ant handling may offer an additional benefit to myrmecochorous seeds via the reduction of the seed coat pathogenic community, which may be driven by elaiosome removal or as a byproduct of ant cleaning behaviors and chemical secretions.

opencc-zeroJan 2024View details →

zenodo40/100

Implications of the three-dimensional chromatin organization for genome evolution in a fungal plant pathogen

The spatial organization of eukaryotic genomes is linked to their biological functions, although it is not clear how this impacts the overall evolution of a genome. Here, we uncover the three-dimensional (3D) genome organization of the phytopathogen Verticillium dahliae, known to possess distinct genomic regions, designated adaptive genomic regions (AGRs), enriched in transposable elements and genes that mediate host infection. Short-range DNA interactions form clear topologically associating domains (TADs) with gene-rich boundaries that show reduced levels of gene expression and reduced genomic variation. Intriguingly, TADs are less clearly insulated in AGRs than in the core genome. At a global scale, the genome contains bipartite long-range interactions, particularly enriched for AGRs and more generally containing segmental duplications. Notably, the patterns observed for V. dahliae are also present in other Verticillium species. Thus, our analysis links 3D genome organization to evolutionary features conserved throughout the Verticillium genus.

opencc-by-4.0Dec 2023View details →

zenodo40/100

Plant pathogens provide clues to the origin of bat white-nose syndrome Pseudogymnoascus destructans

Phylogenomic analyses of P. destructans. This is a snapshot of the GitLab repository available at https://gitlab.gwdg.de/molsysevol/pseudogymnoascus-destructans-phylogeny/.

opencc-by-4.0Jan 2022View details →

dryad40/100

Deciphering the interactions between plant species and their main fungal root pathogens in mixed grassland communities

1. Plant diversity can reduce the risk of plant disease, but positive, and neutral effects have also been reported. These contrasting relationships suggest that plant community composition, rather than diversity per se, affects disease risk. Here, we investigated how diversity and composition of plant communities drive root-associated pathogen accumulation belowground. 2. In a temperate grassland biodiversity experiment, containing 16 plant species (forbs and grasses), we determined the abundance of root-associated fungal pathogens in individual plant species growing in monocultures and in 4-species mixtures through Illumina MiSeq amplicon sequencing. 3. In the plant monocultures, we identified three major fungal pathogens that differed in host range: Paraphoma chrysanthemicola, associated with roots of forb species of the Asteraceae family, Slopeiomyces cylindrosporus, associated with grass species, and Rhizoctonia solani, associated with multiple forb and grass species. In mixtures, there was no significant reduction in relative abundance of these pathogens in their host species as compared to monocultures. However, in mixtures, there was a significant increase in relative abundance of each pathogen in several non-host and host plant species. Across mixtures, plant community composition affected pathogen relative abundance in individual plant species. This effect was driven by the presence of a particular neighbouring plant species (depending on the pathogen), rather than functional group composition (i.e. grass/forb ratio) or averaged pathogen pressure (based on monocultures) of all neighbours. Specifically, the presence of neighbour host species Achillea millefolium significantly increased P. chrysanthemicola, but decreased R. solani relative abundance in several host and non-host plant species in mixtures. 4. Synthesis: Our results indicate that interactions between different plant species – both host and non-hosts – and fungal pathogens underlie effects of plant diversity on root pathogen abundance. Non-host species may act as pathogen reservoirs in diverse plant communities, as they harboured certain pathogens in mixtures, but not in monocultures. Additionally, particular host species can strongly affect pathogen abundance in other (host and non-host) plant species in plant mixtures, suggesting clear effects of species identity in the diversity-disease relationship. Belowground disease risk thus depends on plant community composition rather than diversity per se, via specific interactions between plant species and their root-associated pathogens.

opencc-zeroDec 2021View details →

dryad40/100

High temperatures reduce growth, infection, and transmission of a naturally occurring fungal plant pathogen

Climate change is rapidly altering the distribution of suitable habitats for many species as well as their pathogenic microbes. For many pathogens, including vector-borne diseases of humans and agricultural pathogens, climate change is expected to increase transmission and lead to pathogen range expansions. However, if pathogens have a lower heat tolerance than their host, increased warming could generate 'thermal refugia' for hosts. Predicting the outcomes of warming on disease transmission requires detailed knowledge of the thermal tolerances of both the host and the pathogen. Such thermal tolerance studies are generally lacking for fungal pathogens of wild plant populations, despite the fact that plants form the base of all terrestrial communities. Here, we quantified three aspects of the thermal tolerance (growth, infection, and propagule production) of the naturally occurring fungal pathogen Microbotryum lychnidis-dioicae, which causes a sterilizing anther-smut disease on the herbaceous plant Silene latifolia. We also quantified two aspects of host thermal tolerance: seedling survival and flowering rate. We found that temperatures >30 degreeC reduced the ability of anther-smut spores to germinate, grow, and conjugate in vitro. In addition, we found that high temperatures (30 degreeC) during, or shortly after the time of inoculation strongly reduced the likelihood of infection in seedlings. Finally, we found that high summer temperatures in the field temporarily cured infected plants, likely reducing transmission. Notably, high temperatures did not reduce survival or flowering of the host plants. Taken together, our results show that the fungus is considerably more sensitive to high temperatures than its host plant. A warming climate could therefore result in reduced disease spread or even local pathogen extirpation, leading to thermal refugia for the host.

opencc-zeroMay 2024View details →

zenodo40/100

Рис. 1. КоΛичество макрокониΑий грибов роΑа Fusarium (% от общего чисΛа эΛементов морфоΛогии) на органах и в физиоΛогических жиΑкостях картофеΛьной коровки Fig. 1. Number of macroconidia of fungus species from the genus Fusarium (% of the total number of morphological elements) on organs and in physiological fluids of the potato ladybird beetle in On the vector characteristics of the potato ladybird beetle Henosepilachna Vigintioctomaculata (Motsch.) (Coleoptera, Coccinellidae) in the system "phytophagous insect - plant pathogen - plant"

Рис. 1. КоΛичество макрокониΑий грибов роΑа Fusarium (% от общего чисΛа эΛементов морфоΛогии) на органах и в физиоΛогических жиΑкостях картофеΛьной коровки Fig. 1. Number of macroconidia of fungus species from the genus Fusarium (% of the total number of morphological elements) on organs and in physiological fluids of the potato ladybird beetle

opencc-by-4.0Jul 2024View details →

zenodo40/100

Linked collectors and determiners for: Mediterranean Plant Endophyte and Pathogens Culture Collection (IBBR-MEPP-01).

Natural history specimen data linked to collectors and determiners held within, "Mediterranean Plant Endophyte and Pathogens Culture Collection (IBBR-MEPP-01)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/b6a44169-5845-4fc8-905c-e79102121dce">https://bionomia.net/dataset/b6a44169-5845-4fc8-905c-e79102121dce</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/b6a44169-5845-4fc8-905c-e79102121dce">https://gbif.org/dataset/b6a44169-5845-4fc8-905c-e79102121dce</a>. Formatted as a Frictionless Data package.

opencc-zeroAug 2024View details →

zenodo40/100

Contrasting genome-wide signatures of selection in two closely related Epichloe plant pathogen species

Deposited here composite plots for each species, each pairwise population combination and each of the seven chromosomes as shown and referred to in the manuscript. The filename contains [species abbrevation]_[chromosome number]_[population 1]_[population 2]. Chromosome-wide SNP data and sweeps identified for the population pair are shown. The top panel shows pairwise FST values, averaged across 5kb windows. Shaded rectangles represent the locations of AT-rich regions. The second panel shows the absolute values of the integrated haplotype score (iHS) calculated at each SNP locus for which the ancestral allele state was known. Scores for pop1 are shown at the top and scores for pop2 are negatively transformed and showed at the bottom. Horizontal dashed lines indicate the 99.9% percentile threshold which was used as a cutoff to identify outlier SNPs and inferred iHS sweeps are shown as shaded rectangles. The third panel shows the cross-population extended haplotype homozygosity (XP-EHH) scores calculated between the two populations. Dashed lines indicate 99.9% percentile threshold which was used as a cutoff to identify outlier SNPs and inferred divergent sweeps are shown as shaded rectangles. Positive and negative XP-EHH values refer to the direction of selection: positive values indicate selection in pop1 negative values indicate selection in pop2. In the bottom panel, composite likelihood ratio (CLR) scores are plotted for pop1 (black) and pop2 (blue), colored dashed lines indicate respective 99.9% threshold and colored rectangles highlight inferred CLR-sweeps.

opencc-by-4.0Jan 2023View details →

dryad40/100