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Data from: Condition‐dependent co‐regulation of genomic clusters of virulence factors in the grapevine trunk pathogen Neofusicoccum parvum
,The ascomycete Neofusicoccum parvum, one of the causal agents of Botryosphaeria dieback, is a destructive wood‐infecting fungus and a serious threat to grape production worldwide. The capability to colonize woody tissue, combined with the secretion of phytotoxic compounds, is thought to underlie its pathogenicity and virulence. Here, we describe the repertoire of virulence factors and their transcriptional dynamics as the fungus feeds on different substrates and colonizes the woody stem. We assembled and annotated a highly contiguous genome using single‐molecule real‐time DNA sequencing. Transcriptome profiling by RNA sequencing determined the genome‐wide patterns of expression of virulence factors both in vitro (potato dextrose agar or medium amended with grape wood as substrate) and in planta. Pairwise statistical testing of differential expression, followed by co‐expression network analysis, revealed that physically clustered genes coding for putative virulence functions were induced depending on the substrate or stage of plant infection. Co‐expressed gene clusters were significantly enriched not only in genes associated with secondary metabolism, but also in those associated with cell wall degradation, suggesting that dynamic co‐regulation of transcriptional networks contributes to multiple aspects of N. parvum virulence. In most of the co‐expressed clusters, all genes shared at least a common motif in their promoter region, indicative of co‐regulation by the same transcription factor. Co‐expression analysis also identified chromatin regulators with correlated expression with inducible clusters of virulence factors, suggesting a complex, multi‐layered regulation of the virulence repertoire of N. parvum.,,
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Data from: Genome resources for seven fungal isolates that cause turfgrass dollar spot disease, including Clarireedia jacksonii and C. monteithiana
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,Ascomycete fungi in the genus Clarireedia are responsible for dollar spot, one of the most destructive and costly diseases affecting turfgrasses worldwide. Almost all grasses grown as turf are susceptible to dollar spot, including many high value grass species commonly used for golf courses. This Ag Data Commons dataset provides the genome sequences for seven isolates of Clarireedia fungi that cause dollar spot disease, including sequences of the two most widespread species, C. jacksonii and C. monteithiana. These data are freely available for research purposes.,,
Systemic production of grapevine phenolics in response to mixed infections by wood-colonizing fungi
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,This data is collected from two experiments, one in 2018 and one in 2019, that left untreated or inoculated grapevines with Diplodia seriata, Neofusicoccum parvum, Phaeomoniella chlamydospora, or mock-inoculated, and then two months later inoculated with one of the three pathogens. Grapevine stem phenolic levels were measured at the time of the second inoculation on a different branch, and comparisons were made between pathogen infected plants or those left non-inoculated. Lesion sizes of the second inoculations also were compared to examine the effects on the first inoculation on these. Lesion lengths were measured in mm, and all phenolic compound levels were measured in mg/g FW amounts.,,
Genome analysis of the ubiquitous boxwood pathogen Pseudonectria foliicola: A small fungal genome with an increased cohort of genes associated with loss of virulence
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,Boxwood plants are affected by many different diseases caused by fungi. Some boxwood diseases are deadly and quickly kill the infected plants, but with others, the plant can survive and even thrive when infected. The fungus that causes volutella blight is the most common of these weak boxwood pathogens. Even the healthiest boxwood plants are infected by the volutella fungus, and often there are no signs that the plants are hurt by the infection. In order to understand why the volutella blight fungus is such a weak pathogen and to understand the genetic mechanisms it uses to interact with boxwood, the complete genome of the volutella fungus was sequenced and characterized. These datasets are generated from the genome sequence of Pseudonectria foliicola, strain ATCC13545, the fungus responsible for volutella disease of boxwood. Datasets include the nuclear genome and mitochondrial genome assemblies (sequenced using Illumina technology), the predicted gene model dataset generated using MAKER, the multiple sequence alignment of single-copy orthologs used for phylogenetic analysis, CMAP files generated from SimpleSynteny analysis of mitogenomes, and high quality photographic images.,,
Data from: Genome analyses of fungal pathogens Neonectria faginata and Neonectria coccinea
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,Protein predictions using Augustus web for the fungi Neonectria coccinea and N. faginata, as well as protein prediction of closely related species N. ditissima, and Corinectria fuckeliana.,,
Data from: Registration of conventional soybean germplasm JTN-5110 with resistance to nematodes and fungal pathogens
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,This dataset was generated from soybean (Glycine max) field trials conducted at the West Tennessee Research and Education Center in Jackson, TN and at the Research and Education Center at Milan in Milan, TN as well as from molecular marker screening conducted at the West Tennessee Research and Education Center in Jackson, TN.,Table 3 includes measured data for height, yield, and seed size, and rating data for lodging and seed quality for JTN-5110, 5601T, and select other released germplasm lines and cultivars tested in replicated breeder yield trials in Jackson and Milan, TN from 2010-2016, excluding 2014. This data may be useful in measuring yield gain in future releases of soybean germplasm or cultivars with broad resistance to soybean cyst nematode (SCN; Heterodera glycines). This data should not be used to measure yield gain for elite high-yielding cultivars that do not have broad cyst nematode resistance.,Table 5 includes rating data for JTN-5110 and soybeans with established SCN resistance from simple sequence repeat (SSR) markers: Satt309 and Sat_168, associated with rhg1 on chromosome 18; Sat_162, associated with Rhg4 on chromosome 8; and Satt574, associated with cqSCN-005 on chromosome 17. This data may be useful in understanding the role of these molecular regions in SCN resistance for JTN-5110 and parent line Anand. This data should not be used to draw broad conclusions about cyst nematode resistance, in general.,Table 7 includes rating data for JTN-5110 and check cultivars from frogeye leafspot (caused by Cercospora sojina) field disease screenings conducted in Milan, TN from 2010-2012. This data may be useful in measuring changes in frogeye leafspot incidence and severity in West Tennessee. This data should not be used to draw broad conclusions or represent different geographic areas.,,
A ‘Chambourcin’ x Vitis vinifera ‘Cabernet Sauvignon’ mapping population to genetically map powdery mildew resistance and other traits
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,This Vitis mapping population consisted of F1 hybrids derived from a cross between female parent ‘Chambourcin’ with a pollen donor ‘Cabernet Sauvignon’. These hybrids were planted at the Missouri State Fruit Experiment Station (MSFES), Mountain Grove, MO 65711 (latitude 37°09'14.8"N; longitude 92°14'46.8" W). An initial planting in 2014 contained 100 progeny, followed by 26 progeny in 2015 and 180 progeny in 2016. The data set includes microsatellite (SSR) and core genome rhAmpSeq haplotype marker data that was used to develop genetic map and phenotype data generated from Blackbird microscopy robot analysis of grapevine powdery mildew (Erysiphe necator) severity.,
Data from: Genome-wide association mapping of resistance to the foliar diseases septoria nodorum blotch and tan spot in a global winter wheat collection
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,Phenotypic Data A subset of 264 lines from the National Small Grains Collection global hexaploid winter wheat germplasm collection was evaluated under controlled growth chamber conditions for reaction to the pathogens Parastagonospora nodorum and Pyrenophora tritici-repentis. Both infiltrations and inoculations were performed on plants planted in plastic cones and when seedlings were at the second leaf stage. Plants were infiltrated with the P. nodorum necrotrophic effectors (NEs) SnTox1, SnToxA, SnTox3, SnTox267, and SnTox5; and the P. tritici-repentis NE Ptr ToxB. The scoring system was 0-3, with reaction types of 2 and 3 considered sensitive and 0 to 1 were insensitive. Plants were inoculated with the P. nodorum isolates Sn4, Sn2000, AR2-1, SnIr05H71a, and NOR4 and P. tritici-repentis isolates Pti2, 86-124, DW5, and AR CrossB10. After inoculation, plants were placed in a 100 % humidity growth chamber at 21 °C for 24 hours under constant light, then moved to a controlled growth chamber at 21 °C with a 12 h photoperiod. Plants were scored at 7 days post inoculation. For P. nodorum, plants were scored using a 0 to 5 scale, with 0 being highly resistant and 5 being highly susceptible. For P. tritici-repentis, plants were scored using a 1 to 5 scale, with 1 being highly resistance and 5 being highly susceptible. Three homogeneous replicates (determined by Bartlett’s chi squared analysis) were used to calculate an average value for each trait. This value was used for the rest of the analysis.,Genotypic Data DNA of the winter wheat panel was extracted and genotyped using the Illumina iSelect 90k wheat SNP array. Clustering data was analyzed using GenomeStudio 2.0.5 from Illumina, Inc. SNPs were ordered based on their physical position in the Chinese Spring IWGSC RefSeq v2.0. In TASSEL v5.2, SNP markers were filtered with a minor allele frequency greater than 0.01 and missing data less than 50%. For the remaining markers, missing values were imputed using the LD-KNNi method.,Genome-wide association analysis data Association mapping was conducted using the R package GAPIT v.3. The filtered hapmap file was used for the association mapping, along with the average value for each phenotypic trait. The models GLM, MLM, MLMM, FarmCPU, and Blink were run on the averages for each trait. ** Resources in this dataset:,