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16 results for “Venturia”

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zenodo44/100

Datasets for "The Venturia inaequalis effector repertoire is expressed in waves, and is dominated by expanded families with predicted structural similarity to avirulence proteins "

<p>Datasets for&nbsp;preprint&nbsp;entitled &quot;The <em>Venturia inaequalis</em> effector repertoire is expressed in waves, and is dominated by expanded families with predicted structural similarity to avirulence proteins from other fungi&quot;</p> <p><strong>1) ViAnnotation.gff3</strong><br> Gene annotation of&nbsp;<em>Venturia inaequalis</em> MNH120 (<a href="https://genome.jgi.doe.gov/Venin1/Venin1.home.html">https://genome.jgi.doe.gov/Venin1/Venin1.home.html</a>) generated as part of the study &quot;The <em>Venturia inaequalis</em> effector repertoire is expressed in waves, and is dominated by expanded families with predicted structural similarity to avirulence proteins from other fungi&quot;.&nbsp;&nbsp;&nbsp;</p> <p>Gene reannotation was performed to include genes that would have been missed in the previous annotation by Deng et al. (2017), especially those genes encoding putative effector proteins, which are difficult to predict.&nbsp;For this purpose, we used a three-step approach. In the first step, coding sequences (CDSs) from <em>V. inaequalis</em> isolate 05/172, which were predicted as part of a previous study by Passey et al. (2018) (<a href="https://journals.asm.org/doi/full/10.1128/MRA.01062-18">https://journals.asm.org/doi/full/10.1128/MRA.01062-18</a>), were downloaded from the National Center for Biotechnology Information (<a href="https://www.ncbi.nlm.nih.gov/nuccore/QFBF00000000.1/">https://www.ncbi.nlm.nih.gov/nuccore/QFBF00000000.1/</a>) and mapped to the MNH120 genome using GMAP v2021-02-22.&nbsp;In the second step, RNA-seq reads from one biological replicate representing each <em>in planta</em> time point of <em>Malus domestica</em> infection by <em>V. inaequalis </em>(12 hour post-inoculation [hpi], 24 hpi, 2 days post-inoculation [dpi], 3 dpi, 5 dpi, 7 dpi), as well as one time point representing growth of the fungus in culture, were mapped to the MNH120 genome using HISAT2 v2.2.1. Then, a genome-guided <em>de novo</em> transcriptome assembly was performed using&nbsp;Trinity v2.12.0 and likely CDSs were identified using Transdecoder v5.5.0 (<a href="https://github.com/TransDecoder/TransDecoder">https://github.com/TransDecoder/TransDecoder</a>) in conjunction with a minimum open frame (ORF) length of 50 amino acids. Finally, in the third step, all annotations were visualized in Geneious v9.05, together with the previous annotation from Deng et al. (2017), and a manual curation was performed to create a consensus prediction. Note: this reannotation was generated with the aim of identifying as many genes as possible, and as a result, it contains many spurious genes.&nbsp;</p> <p><strong>2) Protein_sequences_ViAnnotation.fasta</strong></p> <p><strong>3) ECs_Families_AlphaFold.zip</strong></p> <p>This dataset&nbsp;is made up of predicted protein tertiary structures representing the main member of each up-regulated&nbsp;<em>V. inaequalis</em> effector candidate family. Structures were predicted using&nbsp;Alphafold with the ColabFold server (<a href="https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/beta/AlphaFold2_advanced.ipynb#scrollTo=rowN0bVYLe9n">https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/beta/AlphaFold2_advanced.ipynb#scrollTo=rowN0bVYLe9n</a>).&nbsp;In cases where&nbsp;the effector candidate had less than 30 proteins with amino acid sequence similarity in the NCBI database, a custom multiple sequence alignment (MSA) was generated and used as input for AlphaFold2.&nbsp;Here, mature protein sequences were used.</p> <p><strong>4) singletons_AlphaFold_OpenSourceCASP14.zip</strong></p> <p>This dataset set is made up of predicted protein tertiary structures representing up-regulated<em> V. inaequalis</em> singleton effector candidates. Structures were predicted using AlphaFold&nbsp;(<a href="https://github.com/deepmind/alphafold">https://github.com/deepmind/alphafold</a>)&nbsp;open source code v2.0.1 and v2.1.0, with pre-set casp14, max_template_date: 2020-05-14. Mature protein sequences were used as input.&nbsp;</p> <p><strong>5) ECs_Avrs_phytopathogens_AlphaFold.zip</strong></p> <p>Predicted tertiary structures of avirulence (Avr) proteins or candidate Avr proteins from other fungal pathogens included in the &quot;The <em>Venturia inaequalis</em> effector repertoire is expressed in waves, and is dominated by expanded families with predicted structural similarity to avirulence&nbsp;proteins from other fungi&quot; study. These structures were predicted using&nbsp;Alphafold with the ColabFold server (<a href="https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/beta/AlphaFold2_advanced.ipynb#scrollTo=rowN0bVYLe9n">https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/beta/AlphaFold2_advanced.ipynb#scrollTo=rowN0bVYLe9n</a>). Mature protein sequences were used as input.&nbsp;</p> <p>If you have any questions about the datasets, please contact us.<br> Mercedes Rocafort: <a href="mailto:m.rocafort.ferrer@massey.ac.nz">m.rocafort.ferrer@massey.ac.nz</a><br> Carl Mesarich: <a href="mailto:c.mesarich@massey.ac.nz">c.mesarich@massey.ac.nz</a></p>

opencc-by-2.0Feb 2022View details →
zenodo40/100

Fig. (10-17): (10) Dichrogaster aestivalis, fore wing; (11) C. armator, areolet of fore wing; (12) Mesostenus sp., areolet of fore wing; (13) Venturia canescens, ovipositor; (14) Barichneumon sp.; ventral aspect of metasoma; (15) Ctenichneumon sp., ventral aspect of metasoma; (16) Exochus castaniventris, frontal view of head; (17) Diplazon laetatorius, frontal view of head. in Ichneumonidae from the Suez Canal region Egypt (Hymenoptera, Ichneumonoidea)

Fig. (10-17): (10) Dichrogaster aestivalis, fore wing; (11) C. armator, areolet of fore wing; (12) Mesostenus sp., areolet of fore wing; (13) Venturia canescens, ovipositor; (14) Barichneumon sp.; ventral aspect of metasoma; (15) Ctenichneumon sp., ventral aspect of metasoma; (16) Exochus castaniventris, frontal view of head; (17) Diplazon laetatorius, frontal view of head.

opencc-by-4.0Dec 2010View details →
zenodo40/100

Linked collectors and determiners for: Contributions to the taxonomy, identification, and biogeography of Casinaria Holmgren and Venturia Schrottky (Hymenoptera: Ichneumonidae: Campopleginae).

Natural history specimen data linked to collectors and determiners held within, "Contributions to the taxonomy, identification, and biogeography of Casinaria Holmgren and Venturia Schrottky (Hymenoptera: Ichneumonidae: Campopleginae)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/2464661b-4e9e-4102-9b3d-5dbd75fc2b37">https://bionomia.net/dataset/2464661b-4e9e-4102-9b3d-5dbd75fc2b37</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/2464661b-4e9e-4102-9b3d-5dbd75fc2b37">https://gbif.org/dataset/2464661b-4e9e-4102-9b3d-5dbd75fc2b37</a>. Formatted as a Frictionless Data package.

opencc-zeroJan 2024View details →
zenodo40/100

Fig. 1 in Description of a new species of the genus Venturia Schrottky (Hymenoptera: Ichneumonidae: Campopleginae) from South Korea

Fig. 1. Venturia tenuiabdominalis sp. nov. A. Habitus in lateral view; B. Head in frontal view; C. Mesosoma in lateral view; D. Propodeum in dorsal view; E. Wings; F. Metasoma in lateral view; G. Tergite 3 and 4 in dorsal view. Scale bars: A, 1.0 mm; B, D, G, 0.2 mm; C, E, F, 0.5 mm.

opencc-by-4.0May 2022View details →
dryad36/100

Foraging behaviour variations, for gene expression and transcriptomic divergence in parasitic wasp populations of Venturia canescens

<p><span>Foraging behaviours encompass strategies to locate resources and to exploit them. In many taxa, these behaviours are driven by a major gene called <em>for</em>, but mechanisms vary </span><span>between species. In the parasitoid wasp <em>Venturia</em> <em>canescens</em>, sexual and asexual populations coexist in sympatry but differ in life-history trait, physiology and behaviours,</span> <span>which could impact their foraging strategies. </span><span>Here, we explored the molecular bases underpinning divergence in behaviours by testing two mutually nonexclusive hypotheses: first, </span><span>the divergence in the <em>for</em> gene correlates with difference in foraging strategies, and second, the latter rely on a divergence in whole-genome expression. Using comparative genomics, we showed that the <em>for</em> gene was conserved across insects considering both sequence and gene model complexity. Polymorphism analysis did not support the occurrence of two allelic variants diverging across the two populations, yet the asexual population exhibited less polymorphism than the sexual population. Sexual and asexual transcriptomes sharply split, with 10.9% of differentially expressed genes, but these were not enriched in behavioural-related genes. We showed that the <em>for</em> gene was more highly expressed in asexual female heads than in sexual heads and that those differences correlate with divergence in foraging behaviours in our experiment since asexuals explored the environment more and exploited more host patches. Overall, these results suggested that fine tuning of <em>for</em> gene expression between populations may have led to distinct foraging behaviours. We hypothesized that reproductive polymorphism and coexistence in sympatry of sexual and asexual populations specialized to different ecological niches via divergent optima on phenotypic traits could imply adaptation through different expression patterns of the for gene and at many other loci throughout the genome.</span></p>

opencc-zeroDec 2022View details →
dryad36/100

Foraging behaviour variations, for gene expression and transcriptomic divergence in parasitic wasp populations of Venturia canescens

Open the record for dataset details and reuse information.

publicDec 2022View details →
zenodo32/100

FIGURES 4–6 in Contributions to the taxonomy, identification, and biogeography of Casinaria Holmgren and Venturia Schrottky (Hymenoptera: Ichneumonidae: Campopleginae)

FIGURES 4–6. Casinaria scalaris sp. nov.: 4, female, holotype; 5, propodeal carination from dorsal view (only carinae are depicted, surface structures such as rugosity, punctures and wrinkles are not illustrated); 6, male, paratype.

opennotspecifiedSep 2019View details →
zenodo32/100

FIGURES 1–3 in Contributions to the taxonomy, identification, and biogeography of Casinaria Holmgren and Venturia Schrottky (Hymenoptera: Ichneumonidae: Campopleginae)

FIGURES 1–3. Casinaria camura sp. nov.: 1, female, holotype; 2, metasoma of holotype from fourth tergite to apex (cardmounted on the same pin); 3, epicnemial carina (transversal part arrowed, specimen facing left).

opennotspecifiedSep 2019View details →
dryad32/100

Data from: Population structure of Venturia inaequalis, a causal agent of apple scab, in response to heterogeneous apple tree cultivation

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publicJan 2018View details →
zenodo28/100

FIGURE 8 in Contributions to the taxonomy, identification, and biogeography of Casinaria Holmgren and Venturia Schrottky (Hymenoptera: Ichneumonidae: Campopleginae)

FIGURE 8. Venturia crassicaput (Morley), female.

opennotspecifiedSep 2019View details →
zenodo28/100

FIGURE 7 in Contributions to the taxonomy, identification, and biogeography of Casinaria Holmgren and Venturia Schrottky (Hymenoptera: Ichneumonidae: Campopleginae)

FIGURE 7. Venturia aquila sp. nov., female, holotype.

opennotspecifiedSep 2019View details →
dryad28/100

Data from: The genetic structure of a Venturia inaequalis population in a heterogeneous host population composed of different Malus species

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publicMar 2013View details →
geo24/100

Time series transcriptome of Venturia inaequalis (apple scab) infecting Malus domestica (apple)

GEO Series GSE198244. Malus domestica; Venturia inaequalis. 28 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenMar 2022View details →
dryad24/100

Data from: When virulence originates from nonagricultural hosts: evolutionary and epidemiological consequences of introgressions following secondary contacts in Venturia inaequalis

In pathogens, introgressions through secondary contacts between divergent populations from agricultural and nonagricultural disease reservoirs are expected to have crucial evolutionary and epidemiological implications. Despite the importance of this question for disease management, experimental demonstrations of these implications remain scarce. Recently, we identified a virulent population of the apple scab pathogen Venturia inaequalis that migrated from nonagricultural hosts to European domestic apple orchards. Here, we investigated the occurrence of gene flow between agricultural and nonagricultural populations sampled in two orchards, and thereafter its consequences on the pathogenicity of hybrids. Population genetic structure and demographic inferences based on the genotypes of 104 strains revealed a high amount of gene flow between the two populations in one orchard. In this site, mating between populations was made possible by the presence of a common host. Our results revealed an invasion of the virulent trait in the agricultural population; a main direction of introgression in hybrids from the agricultural to nonagricultural genetic backgrounds; and a population of hybrids with transgressive traits. We demonstrate a secondary contact with gene flow between divergent populations of pathogens. Our findings highlight evolutionary and epidemiological changes in pathogens and have concrete implications for sustainable disease management.

opencc-zeroDec 2015View details →
dryad24/100

Data from: When virulence originates from nonagricultural hosts: evolutionary and epidemiological consequences of introgressions following secondary contacts in Venturia inaequalis

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publicDec 2016View details →
geo20/100

Modulation of apple leaf transcriptome by QTLs and infection by Venturia inaequalis

GEO Series GSE250309. Malus domestica. 16 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenFeb 2024View details →

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