Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
92
datasets available to search
ShareScore release 0.7.1
Dataset results
92 results for “Pseudomonas syringae”
QTL Mapping for Resistance to Cankers Induced by Pseudomonas syringae pv. actinidiae (Psa) in a Tetraploid Actinidia chinensis Kiwifruit Population
<p>Raw Illumina R1 sequence reads for individual plants genotyped for the study entitled "QTL Mapping for Resistance to Cankers Induced by <em>Pseudomonas syringae</em> pv. <em>actinidiae</em> (Psa) in a Tetraploid <em>Actinidia chinensis</em> Kiwifruit Population" accepted in MDPI Pathogen journals, Special issue <em>"</em><em>Pseudomonas syringae</em> Species Complex"</p>
QTL Mapping for Resistance to Cankers Induced by Pseudomonas syringae pv. actinidiae (Psa) in a Tetraploid Actinidia chinensis Kiwifruit Population
<p>Raw Illumina R2 sequence reads for individual plants genotyped for the study entitled "QTL Mapping for Resistance to Cankers Induced by <em>Pseudomonas syringae</em> pv. <em>actinidiae</em> (Psa) in a Tetraploid <em>Actinidia chinensis</em> Kiwifruit Population" accepted in MDPI Pathogen journals, Special issue <em>"</em><em>Pseudomonas syringae</em> Species Complex"</p>
Exploring the Exclusive Isolation of Pseudomonas syringae in Peltigera Lichens via metabolite analysis and growth assays - Appendix
<p>Lichen samples from Iceland were collected from the genera Peltigera, Cladonia, and Stereocaulon in March 2023 at Heidmork forest, Oskjuhlid hill, and the shores of Ellidaa in Arbaejarstifla. All specimens underwent morphological analysis, and corresponding vouchers have been deposited at the Icelandic Institute of Natural History.</p>
Genomic and phenotypic signatures of bacteriophage coevolution with the phytopathogen Pseudomonas syringae
Open the record for dataset details and reuse information.
Pseudomonas syringae virulence factors identified by HMMER
<p>JSON object containing Pseudomonas syringae accession numbers as primary keys, containing protein accession numbers, e-value, and NCBI annotations for every HMMER hit for common <em>P. syringae</em> virulence factors. includes: canonical type III secretion system genes, type III effector subfamilies as described by the <em>Pseudomonas syringae</em> effector compendium, and ewoody host or Pseudomonas (WHOP) genes.</p>
Data of "High-speed cryo-microscopy proves that ice-nucleating proteins of Pseudomonas syringae trigger freezing at hydrophobic interfaces"
<p>Raw data of a study titled <strong><em>"High-speed cryo-microscopy proves that ice-nucleating proteins of Pseudomonas syringae trigger freezing at hydrophobic interfaces"</em></strong>.</p> <p>The <strong>onset_locations.zip</strong> folder contains all analyzed images which are screenshots from the cryo-microscopic videos. Raw screenshots and evaluated images are included in two sub-folders per samples. The sample description is the name of the folders.</p> <p>The <strong>ice_propogation_velocity.zip</strong> folder contains all images that were used for the evaluation of the propagation velocity of ice. Every sample folder contains the original spot detection image, one image at a later time point, the subtracted image, and one image with the measured distance of the ice front indicated as white scale bar.</p> <p>The <strong>Results_(ice_propagation_velocity).xlsx</strong> contains the results from the velocity calculations, the <strong>Results_(surface tension).xlsx</strong> contains the evaluated surface tension values and the <strong>Results_(temperatures and locations).xlsx</strong> file contains all evaluated freezing locations (polar coordinates) and temperatures of all analyzed samples.</p>
Data files and taxonomic classifiers for Pseudomonas syringae classification and virulence factor prediction
<p>PSSC.tree : core-genome tree of 2,161 high quality <em>Pseudomonas syringae</em> genomes</p> <p>metadata.csv: A CSV file containing taxonomic data, type strain designations, phylogroups as assigned in this study, LIN clusters assigned for classification purposes, presence/absence of key virulence factors, and metadata found in each genome’s Biosample record for all genomes found in PSSC.tree</p> <p>CLASSIFIER_xxx: QIIME 2 classifier artifacts trained on amplicons generated from primer sets indicated in file name</p> <p>xxx_VFOC.JSON: HMMER results for T3SS and effectors and WHOP genes, structured with both genome and gene product accession numbers as primary key, depending on file</p> <p> </p> <p> </p>
Supplementary data for Competition and Virulence in Pseudomonas syringae
<p><strong>Supplementary data 2.1.1</strong></p><p>CSV file describing 2,161 PSSC genomes used in the evaluation of PCR primers in chapter 2.1 and used as reference genomes for classification of isolates at syringae.org in chapter 2.2. file contains RefSeq accession numbers for each genome, T3E family repertoires, and phylogroup and ANI clusters assigned to each genome (metadata.csv) </p><p> </p><p><strong>Supplementary data 2.1.2</strong></p><p>Table describing columns contained in Supplementary data 2.1.1</p><p> </p><p><strong>Supplementary data 2.2</strong></p><p>Excel file with in-silico amplification rates for all 16 primer sets evaluated in chapter 2.1, both overall and by phylogroup.</p><p> </p><p><strong>Supplementary data 2.3</strong></p><p>Newick tree file containing the phylogenetic tree of 2,161 PSSC genomes used throughout chapters 2 and 3, with bootstrap values.</p><p> </p><p><strong>Supplementary data 2.4</strong></p><p>HMM file containing hidden Markov models for all VFOCs identified in chapter 2.1 and 2.2.</p><p> </p><p><strong>Supplementary data 2.5.1</strong></p><p>JSON file describing all canonical T3SS, T3E, and WHOP genes in the 2,161 genomes used in chapter 2.1 and 2.2, as detected by HMMER, with genome accessions as top-level keys. Additional keys found in each file are described in Table 2.2.3.</p><p> </p><p><strong>Supplementary data 2.5.2</strong></p><p>Table containing description of JSON structure for Supplementary data 2.5.1</p><p> </p><p><strong>Supplementary data 2.6.1</strong></p><p>JSON file describing all canonical T3SS, T3E, and WHOP genes in the 2,161 genomes used in chapter 2.1 and 2.2, as detected by HMMER, with protein accessions as top-level keys. Additional keys found in each file are described in Table 2.2.3.</p><p> </p><p><strong>Supplementary data 2.6.2</strong></p><p>Table containing description of JSON structure for Supplementary data 2.6.1</p><p> </p><p><strong>Supplementary data 2.7</strong></p><p>TSV file containing LIN numbers associated with each reference genome, used as input for training classifiers. </p><p> </p><p><strong>Supplementary data 2.8</strong></p><p>FASTA file containing in-silico amplicons generated from primer set CTS_Hwang, used as input for training a Naïve Bayes classifier.</p><p> </p><p><strong>Supplementary data 2.9</strong></p><p>FASTA file containing in-silico amplicons generated from primer set gapA_Hwang, used as input for training a Naïve Bayes classifier. </p><p> </p><p><strong>Supplementary data 2.10</strong></p><p>FASTA file containing in-silico amplicons generated from primer set gyrB_Hwang, used as input for training a Naïve Bayes classifier. </p><p> </p><p><strong>Supplementary data 2.11</strong></p><p>FASTA file containing in-silico amplicons generated from primer set pgi_Yan, used as input for training a Naïve Bayes classifier. </p><p> </p><p><strong>Supplementary data 2.12</strong></p><p>FASTA file containing in-silico amplicons generated from primer set rpoD_Hwang, used as input for training a Naïve Bayes classifier. </p><p> </p><p><strong>Supplementary data 2.13</strong></p><p>Naïve Bayes classifier for primer set CTS_Hwang</p><p> </p><p><strong>Supplementary data 2.14</strong></p><p>Naïve Bayes classifier for primer set gapA_Hwang</p><p> </p><p><strong>Supplementary data 2.15</strong></p><p>Naïve Bayes classifier for primer set gyrB_Hwang</p><p> </p><p><strong>Supplementary data 2.16</strong></p><p>Naïve Bayes classifier for primer set pgi_Yan</p><p> </p><p><strong>Supplementary data 2.17</strong></p><p>Naïve Bayes classifier for primer set rpoD_Hwang</p><p> </p><p><strong>Supplementary data 3.1</strong></p><p>HMM1, representing tailocin tail fibers associated with killing class 1</p><p> </p><p><strong>Supplementary data 3.2</strong></p><p>HMM2, representing tailocin tail fibers associated with killing class 2</p><p> </p><p><strong>Supplementary data 3.3</strong></p><p>HMM3, representing tailocin tail fibers from PSSC strain UB246</p><p> </p><p><strong>Supplementary data 3.4</strong></p><p>Amino acid sequence for WP_044313553.1, representative of type 1a tailocin-associated tail fiber used for protein structure prediction in Supplementary data 3.5</p><p> </p><p><strong>Supplementary data 3.5</strong></p><p>PDB file containing predicted structure of WP_044313553.1, representative of type 1a tailocin-associated tail fiber </p><p> </p><p><strong>Supplementary data 3.6</strong></p><p>Amino acid sequence for WP_122688044.1, representative of type 1b tailocin-associated tail fiber used for protein structure prediction in Supplementary data 3.7</p><p> </p><p><strong>Supplementary data 3.7</strong></p><p>PDB file containing predicted structure of WP_122688044.1, representative of type 1b tailocin-associated tail fiber</p><p> </p><p><strong>Supplementary data 3.8</strong></p><p>Amino acid sequence for WP_005768002.1, representative of type 2 tailocin-associated tail fiber used for protein structure prediction in Supplementary data 3.9</p><p> </p><p><strong>Supplementary data 3.9</strong></p><p>PDB file containing predicted structure of WP_005768002.1, representative of type 2 tailocin-associated tail fiber</p><p> </p><p><strong>Supplementary data 3.10</strong></p><p>Amino acid sequence for WP_024674765.1, representative of type 3 tailocin-associated tail fiber used for protein structure prediction in Supplementary data 311</p><p> </p><p><strong>Supplementary data 3.11</strong></p><p>PDB file containing predicted structure of WP_024674765.1, representative of type 3 tailocin-associated tail fiber</p><p> </p><p><strong>Supplementary data 3.12</strong></p><p>Amino acid sequence for WP_198721597.1, representative of RSA1-like prophage-associated tail fiber used for protein structure prediction in Supplementary data 3.13</p><p> </p><p><strong>Supplementary data 3.13</strong></p><p>PDB file containing predicted structure of WP_198721597.1, representative of RSA1-like prophage-associated tail fiber</p><p> </p><p><strong>Supplementary data 3.14</strong></p><p>CSV file containing HMM1 and HMM2 genomic screen results, with accession numbers and identities of tail fibers detected in each genome.</p><p> </p><p><strong>Supplementary data 3.15</strong></p><p>CSV file containing HMM3 genomic screen results, with copy number of tail fibers detected in each genome and the phylogroup the genome belongs to</p>
Three genomes of Pseudomonas syringae pv. actinidiae and their annotation files were used for comparative genomic studies
Open the record for dataset details and reuse information.
Fig. 7 in Metabolite profiling reveals a role for intercellular dihydrocamalexic acid in the response of mature Arabidopsis thaliana to Pseudomonas syringae
Fig. 7. Exogenous infiltration of dihydrocamalexic acid (DHCA) and bacterial quantification of Pseudomonas syringae in rosette leaves of 7-week old Col-0 and cyp71a12/cyp71a13. DHCA (0.07 μg/mL or 0.25 μg/mL) was applied via pressure infiltration to 7-week-old plants at 24 h postinoculation with P. syringae or mock solution (0.06% DMSO in 10 mM MgCl2). Bacterial levels were quantified at 3 days postinoculation with Pst. Values represent the mean ± standard deviation of three sample replicates (n = 3) consisting of 8 plants each. Different letters indicate statistically significant differences (ANOVA, Tukey's honestly significant difference [HSD], P <0.05).
Fig. 6 in Metabolite profiling reveals a role for intercellular dihydrocamalexic acid in the response of mature Arabidopsis thaliana to Pseudomonas syringae
Fig. 6. Effect of dihydrocamalexic acid (DHCA) and salicylic acid (SA) on biofilm formation of Pseudomonas syringae (Pst) in vitro. Dose-dependent effect of (A) SA and (B) DHCA on Pst biofilm formation in Hrp-inducing minimal medium as measured by crystal violet staining of surface-adherent cells and de-staining with acetic acid (OD570) after stationary incubation for 24, 32, 48, or 60 h. Each data point is the mean ± SD of five wells per concentration from a 96-well non-tissueculture-treated plate. Different letters indicate statistically significant differences (one-way ANOVA, Tukey's honestly significant difference [HSD], P <0.05). Ns indicates not significant. Bars (from left to right) within each timepoint are: 18 μg/mL, 4.5 μg/mL, 1.2 μg/mL, 0.3 μg/mL, and 0 μg/mL. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 5 in Metabolite profiling reveals a role for intercellular dihydrocamalexic acid in the response of mature Arabidopsis thaliana to Pseudomonas syringae
Fig. 5. Growth of Pseudomonas syringae (Pst) in the presence of dihydrocamalexic acid (DHCA), camalexin, or DHCA analogs in vitro. Dose-dependent effect of (S)-dihydrocamalexic acid (A), camalexin (B), (1) (R)-2-(phenyl)-4,5-dihydrothiazole-4-carboxylic acid (C), (2) (S)-2-(phenyl)-4,5-dihydrothiazole-4-carboxylic acid (D), (3) (R)-2-(4-hydroxyphenyl)-4,5-dihydrothiazole-4-carboxylic acid (E), and (4) (S)-2-(4-hydroxyphenyl)-4,5-dihydrothiazole-4-carboxylic acid (F) on the growth of Pst in Hrp-inducing minimal medium as measured by turbidity (OD) after incubation for 68 h at room temperature (approximately 25 ◦ C). Each data 600 point is the mean ± SD of three wells per concentration from a 96-well non-tissue-culture-treated plate.
Fig. 4 in Metabolite profiling reveals a role for intercellular dihydrocamalexic acid in the response of mature Arabidopsis thaliana to Pseudomonas syringae
Fig. 4. Quantification of dihydrocamalexic acid (DHCA) (m/z 247.0541, [C12H10N2O2S þ H]) in intercellular washing fluids (IWFs). DHCA levels measured in IWFs from Col-0, cyp71a12/cyp71a13, and cyp71b15 (all 7-weeks post-germination) 24 h after inoculation with P. syringae (Pst) or 10 mM MgCl2 (mock-inoculation) measured by UPLC-MS electrospray ionization in positive mode (ESI+). Values represent the mean ± standard deviation of three sample replicates (n = 3). Different letters indicate statistically significant differences (one-way ANOVA, Tukey's honestly significant difference [HSD], P <0.05). Standard curves were prepared using synthetic DHCA (2 pg–6 μg on column).
Fig. 3 in Metabolite profiling reveals a role for intercellular dihydrocamalexic acid in the response of mature Arabidopsis thaliana to Pseudomonas syringae
Fig. 3. Identification of dihydrocamalexic acid (DHCA) in intercellular washing fluids. Extracted ion chromatograms and mass spectra for DHCA (m/z 247.0541, [C12H10N2O2S + H] in intercellular washing fluids from Pseudomonas syringae-inoculated leaves compared to a synthetic standard. (A) Extracted ion chromatograms and (B) MSMS (25 eV). n. d. Indicates compound not detected. Samples were run in positive electrospray ionization mode.
Fig. 1 in Metabolite profiling reveals a role for intercellular dihydrocamalexic acid in the response of mature Arabidopsis thaliana to Pseudomonas syringae
Fig. 1. Biosynthesis pathway of tryptophan-derived specialised metabolism in Arabidopsis thaliana (simplified). Dashed arrows indicate potential nonenzymatic reactions. Multiple arrows indicate multiple reaction steps simplified for presentation. IAOx: indole-3-acetaldoxime, I3M: indole-3- methylglucosinolate, IAN: indole-3-acetonitrile, ICHO: indole-3-carbaldehyde, ICOOH: indole-3-carboxylic acid, ICN: indole-3-carbonyl nitrile, 4-OH-ICN: 4- hydroxyindole-3-carbonyl nitrile, NSP: nitrile-specifier protein, FOX1: flavin-dependent oxidoreductase, AAO1: Arabidopsis aldehyde oxidase I, GGP: gammaglutamyl peptidase, GGT: gamma-glutamyl transpeptidase DHCA: dihydrocamalexic acid. Modified from Rajniak et al. (2015); Müller et al. (2019).
Data from: Tomato Sl3-MMP, a member of the Matrix metalloproteinase family, is required for disease resistance against Botrytis cinerea and Pseudomonas syringae pv. tomato DC3000
Background: Matrix metalloproteinases (MMPs) are a family of zinc-dependent endopeptidases. MMPs have been characterized in detail in mammals and shown to play key roles in many physiological and pathological processes. Although MMPs in some plant species have been identified, the function of MMPs in biotic stress responses remains elusive. Results: A total of five MMP genes were identified in tomato genome. qRT-PCR analysis revealed that expression of Sl-MMP genes was induced with distinct patterns by infection of Botrytis cinerea and Pseudomonas syringae pv. tomato (Pst) DC3000 and by treatment with defense-related hormones such as salicylic acid, jasmonic acid and ethylene precursor 1-amino cyclopropane-1-carboxylic acid. Virus-induced gene silencing (VIGS)-based knockdown of individual Sl-MMPs and disease assays indicated that silencing of Sl3-MMP resulted in reduced resistance to B. cinerea and Pst DC3000, whereas silencing of other four Sl-MMPs did not affect the disease resistance against these two pathogens. The Sl3-MMP-silenced tomato plants responded with increased accumulation of reactive oxygen species and alerted expression of defense genes after infection of B. cinerea. Transient expression of Sl3-MMP in leaves of Nicotiana benthamiana led to an enhanced resistance to B. cinerea and upregulated expression of defense-related genes. Biochemical assays revealed that the recombinant mature Sl3-MMP protein had proteolytic activities in vitro with distinct preferences for specificity of cleavage sites. The Sl3-MMP protein was targeted onto the plasma membrane of plant cells when transiently expressed in onion epidermal cells. Conclusion: VIGS-based knockdown of Sl3-MMP expression in tomato and gain-of-function transient expression of Sl3-MMP in N. benthamiana demonstrate that Sl3-MMP functions as a positive regulator of defense response against B. cinerea and Pst DC3000.
Data from: Pseudomonas syringae enhances herbivory by suppressing the reactive oxygen burst in Arabidopsis
Plant-herbivore interactions have evolved in the presence of plant-colonizing microbes. These microbes can have important third-party effects on herbivore ecology, as exemplified by drosophilid flies that evolved from ancestors feeding on plant-associated microbes. Leaf-mining flies in the genus Scaptomyza, which is nested within the paraphyletic genus Drosophila, show strong associations with bacteria in the genus Pseudomonas, including Pseudomonas syringae. Adult females are capable of vectoring these bacteria between plants and larvae show a preference for feeding on P. syringae-infected leaves. Here we show that Scaptomyza flava larvae can also vector P. syringae to and from feeding sites, and that they not only feed more, but also develop faster on plants previously infected with P. syringae. Our genetic and physiological data show that P. syringae enhances S. flava feeding on infected plants at least in part by suppressing anti-herbivore defenses mediated by reactive oxygen species.
Data from: Bacillus cereus AR156 activates defense responses to Pseudomonas syringae pv. tomato in Arabidopsis thaliana similarly to flg22
Bacillus cereus AR156 (AR156) is a plant growth promoting rhizobacterium capable of inducing systemic resistance to Pseudomonas syringae pv. tomato (Pst) in Arabidopsis thaliana (Arabidopsis). Here we show that when applied to Arabidopsis leaves, AR156 acted similarly to flg22, a typical pathogen-associated molecular pattern (PAMP), in initiating PAMP-triggered immunity (PTI). AR156-elicited PTI responses included phosphorylation of MPK3 and MPK6, induction of the expression of defense-related genes PR1, FRK1, WRKY22, and WRKY29; production of reactive oxygen species; and callose deposition. Pretreatment with AR156 still significantly reduced Pst multiplication and disease severity in NahG transgenic plants and mutants sid2-2, jar1, etr1, ein2, npr1, and fls2. This suggests that AR156-induced PTI responses require neither salicylic acid, jasmonic acid, and ethylene signaling; nor flagella receptor kinase FLS2, the receptor of flg22. On the other hand, AR156 and flg22 acted in concert to differentially regulate a number of AGO1-bound miRNAs that function to mediate PTI. A full-genome transcriptional profiling analysis indicated that AR156 and flg22 activated similar transcriptional programs, co-regulating the expression of 117 genes; their concerted regulation of 16 genes was confirmed by real-time quantitative PCR analysis. These results suggest that AR156 activates basal defense responses to Pst in Arabidopsis similarly to flg22.
Single-cell resolution of the plant response to Pseudomonas syringae infection
<p>Original images for figures and quantifications. </p>
Fig. 2 in Isobenzofuranones and isocoumarins from kiwi endophytic fungus Paraphaeosphaeria sporulosa and their antibacterial activity against Pseudomonas syringae pv. actinidiae
Fig. 2. Key HMBC and ROESY correlations of compounds 1, 4, and 5.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.