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Search Results (199)

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21 pages, 11139 KiB  
Article
The Transcriptional Landscape of Berry Skin in Red and White PIWI (“Pilzwiderstandsfähig”) Grapevines Possessing QTLs for Partial Resistance to Downy and Powdery Mildews
by Francesco Scariolo, Giovanni Gabelli, Gabriele Magon, Fabio Palumbo, Carlotta Pirrello, Silvia Farinati, Andrea Curioni, Aurélien Devillars, Margherita Lucchin, Gianni Barcaccia and Alessandro Vannozzi
Plants 2024, 13(18), 2574; https://doi.org/10.3390/plants13182574 - 13 Sep 2024
Viewed by 269
Abstract
PIWI, from the German word Pilzwiderstandsfähig, meaning “fungus-resistant”, refers to grapevine cultivars bred for resistance to fungal pathogens such as Erysiphe necator (the causal agent of powdery mildew) and Plasmopara viticola (the causal agent of downy mildew), two major diseases in viticulture. These [...] Read more.
PIWI, from the German word Pilzwiderstandsfähig, meaning “fungus-resistant”, refers to grapevine cultivars bred for resistance to fungal pathogens such as Erysiphe necator (the causal agent of powdery mildew) and Plasmopara viticola (the causal agent of downy mildew), two major diseases in viticulture. These varieties are typically developed through traditional breeding, often crossbreeding European Vitis vinifera with American or Asian species that carry natural disease resistance. This study investigates the transcriptional profiles of exocarp tissues in mature berries from four PIWI grapevine varieties compared to their elite parental counterparts using RNA-seq analysis. We performed RNA-seq on four PIWI varieties (two red and two white) and their noble parents to identify differential gene expression patterns. Comprehensive analyses, including Differential Gene Expression (DEGs), Gene Set Enrichment Analysis (GSEA), Weighted Gene Co-expression Network Analysis (WGCNA), and tau analysis, revealed distinct gene clusters and individual genes characterizing the transcriptional landscape of PIWI varieties. Differentially expressed genes indicated significant changes in pathways related to organic acid metabolism and membrane transport, potentially contributing to enhanced resilience. WGCNA and k-means clustering highlighted co-expression modules linked to PIWI genotypes and their unique tolerance profiles. Tau analysis identified genes uniquely expressed in specific genotypes, with several already known for their defense roles. These findings offer insights into the molecular mechanisms underlying grapevine resistance and suggest promising avenues for breeding strategies to enhance disease resistance and overall grape quality in viticulture. Full article
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Figure 1

Figure 1
<p>(<b>A</b>) Correlation matrix heatmap showing the Euclidean distance between samples based on normalized data obtained from 18 RNA-seq samples constituted of berry skin tissues of the CC, CV, CS, SR, SN, and SB varieties in the ripening (R) phase. A darker color indicates a stronger correlation. (<b>B</b>) PCA on normalized data obtained from 18 RNA-seq samples. Colors indicate different varieties considered. (<b>C</b>) The histogram shows the number of upregulated and downregulated DEGs in white and red PIWI varieties compared to their respective noble parents (SB for white and CS for red). It includes both cumulative comparisons of all PIWI varieties of the same color against their parental variety, as well as individual comparisons (e.g., SR vs. SB). (<b>D</b>) Upset plots visualizing the intersections amongst different groups of DEGs identified in pairwise comparisons. Single points indicate a private DEG identified in each group, whereas 2 to <span class="html-italic">n</span> dot plots indicate DEGs shared by 2 to n groups.</p>
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<p>K-means-corrected WGCNA. (<b>A</b>) Cluster dendrogram of module eigengenes. Branches of the dendrogram group together eigengenes that are positively correlated. The merge threshold was set to 0.25: modules under this value were merged due to their similarity in expression profiles. (<b>B)</b> Bar graph showing the distribution of genes over the twenty-six modules identified. (<b>C</b>) Module-variety/trait association analysis. The heatmap shows the correlation between modules and varieties/traits. Each row corresponds to a module, whereas each column corresponds to a specific trait. The correlation coefficient between a given module and tissue type is indicated by the color of the cell at the row–column intersection and by the text inside the cells (squared boxes indicate significant <span class="html-italic">p</span>-values). Red and blue indicate positive and negative correlations, respectively. CC, Cabernet cortis; SN, Sauvignon nepis; SR, Sauvignon rytos; CV, Cabernet volos; SB, Sauvignon blanc; CS, Cabernet sauvignon; T/S, tolerance/susceptibility; GC, grape color. (<b>D</b>) Scatterplots of gene significance (GS) vs. module membership (MM) in the brown module associated with Cabernet cortis (CC). Genes highly significantly associated with a trait are often also the most important (central) elements of modules associated with the trait. (<b>E</b>) Heatmap visualizing gene expression within the brown module across all biological replicates of the six considered varieties, normalized using Z-scores.</p>
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<p>Modules contemporaneously associated with both tolerance/susceptibility and one or more grapevine varieties analyzed. (<b>A</b>) Table showing the orientation of correlations in all varieties/traits considered (CC, Cabernet cortis; SN, Sauvignon nepis; SR, Sauvignon rytos; CV, Cabernet volos; SB, Sauvignon blanc; CS, Cabernet sauvignon; T/S, tolerance/susceptibility; GC, grape color). Green arrows indicate a positive correlation between the specific module and the trait/genotype. Red arrows indicate a negative association between the specific module and the trait/genotype considered. (<b>B</b>) Gene Set Enrichment Analyses of the tan and blue modules showing the top 10 enriched categories based on fold change. The threshold <span class="html-italic">p</span>-value was set to 0.01 (<b>C</b>) Heatmap visualizing gene expression within the blue and tan modules across all biological replicates of the six considered varieties, normalized using Z-scores.</p>
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<p>Vesicle transport pathways in plants. COP-II vesicles mediate cargo transport from the ER to the cis-Golgi, while COP-I traffics the cargo from the Golgi to the ER and intra-Golgi as well. Clathrin-mediated endocytosis (CME) is the primary mechanism by which eukaryotic cells internalize extracellular or membrane-bound cargoes and it plays crucial roles in plant–microbe interactions Clathrin-coated vesicles (CCVs) are involved in the flow of cargo from the plasma membrane and trans-Golgi network to endosomes and retromers. Grapevine genes found to be enriched in the tan module are indicated in proximity to the related transport pathway.</p>
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<p>Identification of absolutely specific genes in different grapevine varieties. (<b>A</b>) Distribution of the variety-specificity tau parameter over the 23,847 genes considered. (<b>B</b>) Bar graph showing the distribution of absolutely specific genes (ASG; tau = 1) and highly specific genes (HSG; tau &gt; 0.85) over the six varieties considered. (<b>C</b>) Heatmap illustrating the expression of ASG in all biological replicates of the six varieties considered (Z-score normalized). (<b>D</b>) Scatterplot illustrating the relation/negative correlation r = −0.78) between specificity (tau) and expression in Sauvignon nepis. Blue dots represent all genes considered in the analysis, orange dots represent ASG in S. nepis, and red dots indicate the top optimal genes for S. nepis based on the score value. (<b>E</b>) Heatmap showing the expression of the top 10 optimal genes identified over the six varieties considered.</p>
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21 pages, 19720 KiB  
Article
Structural and Phylogenetic In Silico Characterization of Vitis vinifera PRR Protein as Potential Target for Plasmopara viticola Infection
by Sofía M. Martínez-Navarro, Xavier de Iceta Soler, Mónica Martínez-Martínez, Manuel Olazábal-Morán, Paloma Santos-Moriano and Sara Gómez
Int. J. Mol. Sci. 2024, 25(17), 9553; https://doi.org/10.3390/ijms25179553 - 3 Sep 2024
Viewed by 336
Abstract
Fungi infection, especially derived from Plasmopara viticola, causes severe grapevine economic losses worldwide. Despite the availability of chemical treatments, looking for eco-friendly ways to control Vitis vinifera infection is gaining much more attention. When a plant is infected, multiple disease-control molecular mechanisms [...] Read more.
Fungi infection, especially derived from Plasmopara viticola, causes severe grapevine economic losses worldwide. Despite the availability of chemical treatments, looking for eco-friendly ways to control Vitis vinifera infection is gaining much more attention. When a plant is infected, multiple disease-control molecular mechanisms are activated. PRRs (Pattern Recognition Receptors) and particularly RLKs (receptor-like kinases) take part in the first barrier of the immune system, and, as a consequence, the kinase signaling cascade is activated, resulting in an immune response. In this context, discovering new lectin-RLK (LecRLK) membrane-bounded proteins has emerged as a promising strategy. The genome-wide localization of potential LecRLKs involved in disease defense was reported in two grapevine varieties of great economic impact: Chardonnay and Pinot Noir. A total of 23 potential amino acid sequences were identified, exhibiting high-sequence homology and evolution related to tandem events. Based on the domain architecture, a carbohydrate specificity ligand assay was conducted with docking, revealing two sequences as candidates for specific Vitis vinifera–Plasmopara viticola host–pathogen interaction. This study confers a starting point for designing new effective antifungal treatments directed at LecRLK targets in Vitis vinifera. Full article
(This article belongs to the Section Molecular Plant Sciences)
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<p>Chromosomal distribution of LecRLK genes in the genome of <span class="html-italic">Vitis vinifera</span> Chardonnay variety. (<b>A</b>) Chromosomal location of proposed LecRLK genes in the <span class="html-italic">Vitis vinifera</span> genome obtained with MG2C [<a href="#B23-ijms-25-09553" class="html-bibr">23</a>]. (<b>B</b>) Phylogenetic tree obtained with ClustalW, and exon–intron distribution of LecRLK genes performed with gene structure display server. Legend: Yellow boxes represent CDS sequence, blue boxes represent UTR sequence, and black lines represent introns [<a href="#B24-ijms-25-09553" class="html-bibr">24</a>,<a href="#B25-ijms-25-09553" class="html-bibr">25</a>]. (<b>C</b>) Amino acid length distribution of LecRLKs in Chardonnay variety.</p>
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<p>Chromosomal distribution of LecRLK genes in the genome of <span class="html-italic">Vitis vinifera</span> Pinot Noir variety. (<b>A</b>) Chromosomal location of proposed LecRLK genes in the <span class="html-italic">Vitis vinifera</span> genome obtained with MG2C [<a href="#B23-ijms-25-09553" class="html-bibr">23</a>]. (<b>B</b>) Phylogenetic tree obtained with ClustalW, and exon–intron distribution of LecRLK genes performed with gene structure display server. Legend: Yellow boxes represent CDS sequence, blue boxes represent UTR sequence, and black lines represent introns [<a href="#B24-ijms-25-09553" class="html-bibr">24</a>,<a href="#B25-ijms-25-09553" class="html-bibr">25</a>]. (<b>C</b>) Amino acid length distribution of LecRLKs in Pinot Noir variety.</p>
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<p>Domain architecture of LecRLKs from <span class="html-italic">Vitis vinifera</span>. (<b>A</b>) Chardonnay proteins and (<b>B</b>) Pinot Noir proteins. SP (light cyan color): signal peptide; TM (red color): transmembrane domain; Lectin (green color): legume lectin domain (Pfam 00139); and kinase (light yellow color): kinase domain (Pfam IPR011009). Created with DOG 2.0 software [<a href="#B26-ijms-25-09553" class="html-bibr">26</a>].</p>
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<p>Multiple sequence alignment of LecRLK legume lectin domain sequences obtained with ClustalW and colored as Clustal codes. Predicted secondary structure of O80939 UniProt code protein was obtained with Jprep for comparison, and consensus logo sequence is shown at the bottom. β-strands (numbered β1–β13) are displayed as green arrows and the α-helix as red regions. Loops A–D are included in secondary structure. Essential amino acids involved in carbohydrate recognition are highlighted with an asterisk. (<b>A</b>) Chardonnay variety; (<b>B</b>) Pinot Noir variety [<a href="#B25-ijms-25-09553" class="html-bibr">25</a>,<a href="#B29-ijms-25-09553" class="html-bibr">29</a>,<a href="#B30-ijms-25-09553" class="html-bibr">30</a>].</p>
Full article ">Figure 4 Cont.
<p>Multiple sequence alignment of LecRLK legume lectin domain sequences obtained with ClustalW and colored as Clustal codes. Predicted secondary structure of O80939 UniProt code protein was obtained with Jprep for comparison, and consensus logo sequence is shown at the bottom. β-strands (numbered β1–β13) are displayed as green arrows and the α-helix as red regions. Loops A–D are included in secondary structure. Essential amino acids involved in carbohydrate recognition are highlighted with an asterisk. (<b>A</b>) Chardonnay variety; (<b>B</b>) Pinot Noir variety [<a href="#B25-ijms-25-09553" class="html-bibr">25</a>,<a href="#B29-ijms-25-09553" class="html-bibr">29</a>,<a href="#B30-ijms-25-09553" class="html-bibr">30</a>].</p>
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<p>Multiple sequence alignment of LecRLK domain sequences obtained with ClustalW and colored as Clustal codes. Predicted secondary structure of Q96285 UniProt code protein was obtained with Jprep for comparison, and consensus logo sequence is shown at the bottom. β-strands are displayed as green arrows and the α-helix as red regions. Essential amino acids involved in catalytic activity are highlighted with an asterisk. (<b>A</b>) Chardonnay variety; (<b>B</b>) Pinot Noir variety [<a href="#B25-ijms-25-09553" class="html-bibr">25</a>,<a href="#B29-ijms-25-09553" class="html-bibr">29</a>,<a href="#B30-ijms-25-09553" class="html-bibr">30</a>].</p>
Full article ">Figure 5 Cont.
<p>Multiple sequence alignment of LecRLK domain sequences obtained with ClustalW and colored as Clustal codes. Predicted secondary structure of Q96285 UniProt code protein was obtained with Jprep for comparison, and consensus logo sequence is shown at the bottom. β-strands are displayed as green arrows and the α-helix as red regions. Essential amino acids involved in catalytic activity are highlighted with an asterisk. (<b>A</b>) Chardonnay variety; (<b>B</b>) Pinot Noir variety [<a href="#B25-ijms-25-09553" class="html-bibr">25</a>,<a href="#B29-ijms-25-09553" class="html-bibr">29</a>,<a href="#B30-ijms-25-09553" class="html-bibr">30</a>].</p>
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<p>Cartoon representation of LecRLKs modeled with Phyre2 and represented by Pymol. Amino acids implicated in carbohydrate stability are shown as sticks, and GalNAc ligands are shown as green sticks. (<b>A</b>) A0A438E3M7; (<b>B</b>) A0A438J290; (<b>C</b>) F6CH85; (<b>D</b>) F6CH87 [<a href="#B47-ijms-25-09553" class="html-bibr">47</a>].</p>
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<p>Surface electrostatic potential calculated by PyMOL. A positive charge is shown in blue, and a negative charge is shown in red. (<b>A</b>) A0A438E3M7; (<b>B</b>) A0A438J290; (<b>C</b>) F6CH85; (<b>D</b>) F6CH87. GalNAc ligands are represented by green sticks [<a href="#B47-ijms-25-09553" class="html-bibr">47</a>].</p>
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15 pages, 2287 KiB  
Article
Herbal Companion Crops as an Example of Implementation of Sustainable Plant Protection Practices in Soybean Cultivation
by Adrian Sikora, Joanna Dłużniewska, Bogdan Kulig and Agnieszka Klimek-Kopyra
Agriculture 2024, 14(9), 1485; https://doi.org/10.3390/agriculture14091485 (registering DOI) - 1 Sep 2024
Viewed by 419
Abstract
This study aimed to assess the effect of using selected herbs as companion crops in soybean cultivation on the yield and overall health of soybeans. A three-year field experiment (2021–2023) was conducted using a randomized block design with three replications, where the primary [...] Read more.
This study aimed to assess the effect of using selected herbs as companion crops in soybean cultivation on the yield and overall health of soybeans. A three-year field experiment (2021–2023) was conducted using a randomized block design with three replications, where the primary experimental variable was the sowing method. The innovative cropping system involved cultivating soybeans alongside different companion herbs, such as alyssum (Lobularia maritima L.), fennel (Foeniculum vulgare Mill.), borage (Borago officinalis L.), French marigold (Tagetes patula L.), calendula (Calendula officinalis L.), and a herbal mixture referred to as ‘MIX’. The study showed that cultivation of soybean with fennel improved the quantitative and qualitative characteristics of the yield, with a significant increase in seed yield (on average by 0.27 t ha−1) as well as protein (7.67%) and oil yield (8.57%) compared to the pure soybean crop. The following fungal diseases were identified during the three-year study period (2021–2023): Cercospora leaf blight, Ascochyta blight, Fusarium wilt, and downy mildew. Cultivation of soybean with herbs as companion crops was implemented to improve the health of soybean to a varied extent. Borage, marigold, and calendula companion crops reduced infection of soybean by the fungi C. kikuchii and F. oxysporum. Cultivation with fennel and marigold was also beneficial for soybean health. On the other hand, cultivation with sweet alyssum and a mixture of herbs increased the occurrence of the fungus A. sojaecola. Cultivation of soybean in association with herbs is legitimate and requires further research given the priorities facing 21st-century agriculture. Full article
(This article belongs to the Special Issue Advances in the Cultivation and Production of Leguminous Plants)
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<p>Effect of the interaction of years and cropping system on (<b>a</b>) plant height, (<b>b</b>) height of first pod setting, (<b>c</b>) pod number, (<b>d</b>) seed number, (<b>e</b>) pod weight, (<b>f</b>) seed weight per plant. Means in columns with different letters are significantly different (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Effect of the interaction of years and cropping system on (<b>a</b>) plant height, (<b>b</b>) height of first pod setting, (<b>c</b>) pod number, (<b>d</b>) seed number, (<b>e</b>) pod weight, (<b>f</b>) seed weight per plant. Means in columns with different letters are significantly different (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Effect of the interaction of year and soybean cropping system on the occurrence of (<b>a</b>) Fusarium wilt, (<b>b</b>) Ascochyta blight, (<b>c</b>) Cercospora leaf blight, and (<b>d</b>) downy mildew during the three-year period (2021–2023). Means in columns with different letters are significantly different (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Effect of the interaction of year and soybean cropping system on the occurrence of (<b>a</b>) Fusarium wilt, (<b>b</b>) Ascochyta blight, (<b>c</b>) Cercospora leaf blight, and (<b>d</b>) downy mildew during the three-year period (2021–2023). Means in columns with different letters are significantly different (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Occurrence of Fusarium wilt depending on the developmental stage of soybean in (<b>a</b>) 2021 and (<b>b</b>) 2022. Means in columns with different letters are significantly different (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Occurrence of Ascochyta blight depending on the development stage of soybean in (<b>a</b>) 2021, (<b>b</b>) 2022, and (<b>c</b>) 2023. Means in columns with different letters are significantly different (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Occurrence of Cercospora leaf blight depending on the development stage of soybean in (<b>a</b>) 2021 and (<b>b</b>) 2022. Means in columns with different letters are significantly different (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Occurrence of downy mildew depending on the development stage of soybean in 2023.</p>
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<p>Effect of the interaction of year and soybean cropping system on the occurrence of Fusarium wilt, Ascochyta blight, and Cercospora leaf blight at the (<b>a</b>) BBCH 69 and (<b>b</b>) BBCH 89.</p>
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16 pages, 3649 KiB  
Article
Pan-Genome Analysis of TRM Gene Family and Their Expression Pattern under Abiotic and Biotic Stresses in Cucumber
by Lili Zhao, Ke Wang, Zimo Wang, Shunpeng Chu, Chunhua Chen, Lina Wang and Zhonghai Ren
Horticulturae 2024, 10(9), 908; https://doi.org/10.3390/horticulturae10090908 - 27 Aug 2024
Viewed by 368
Abstract
Cucumber (Cucumis sativus L.) is a vital economic vegetable crop, and the TONNEAU1 Recruiting Motif (TRM) gene plays a key role in cucumber organ growth. However, the pan-genomic characteristics of the TRM gene family and their expression patterns under different stresses have [...] Read more.
Cucumber (Cucumis sativus L.) is a vital economic vegetable crop, and the TONNEAU1 Recruiting Motif (TRM) gene plays a key role in cucumber organ growth. However, the pan-genomic characteristics of the TRM gene family and their expression patterns under different stresses have not been reported in cucumber. In this study, we identified 29 CsTRMs from the pan-genomes of 13 cucumber accessions, with CsTRM29 existing only in PI183967. Most CsTRM proteins exhibited differences in sequence length, except five CsTRMs having consistent protein sequence lengths among the 13 accessions. All CsTRM proteins showed amino acid variations. An analysis of CsTRM gene expression patterns revealed that six CsTRM genes strongly changed in short-fruited lines compared with long-fruited lines. And four CsTRM genes strongly responded to salt and heat stress, while CsTRM14 showed responses to salt stress, powdery mildew, gray mold, and downy mildew. Some CsTRM genes were induced or suppressed at different treatment timepoints, suggesting that cucumber TRM genes may play different roles in responses to different stresses, with expression patterns varying with stress changes. Remarkably, the expression of CsTRM21 showed considerable change between long and short fruits and in responses to abiotic stresses (salt stress and heat stress), as well as biotic stresses (powdery mildew and gray mold), suggesting a dual role of CsTRM21 in both fruit shape determination and stress resistance. Collectively, this study provided a base for the further functional identification of CsTRM genes in cucumber plant growth and stress resistance. Full article
(This article belongs to the Special Issue Vegetable Genomics and Breeding Research)
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Figure 1
<p>Comparison of the conserved motifs and gene structures of <span class="html-italic">CsTRM07</span> (<b>A</b>), <span class="html-italic">CsTRM17</span> (<b>B</b>), and <span class="html-italic">CsTRM24</span> (<b>C</b>) in the 13 cucumber accessions.</p>
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<p>Synteny analysis of <span class="html-italic">TRMs</span> among cucumber and other plant species: Gray lines indicate the collinear blocks, while red lines highlight the collinear gene pairs involving TRM genes. <span class="html-italic">‘C. sativus’</span>, <span class="html-italic">‘Z. mays’</span>, <span class="html-italic">‘O. sativa’</span>, <span class="html-italic">‘A. thaliana’</span>, and <span class="html-italic">‘S. lycopersicum’</span> indicate <span class="html-italic">Cucumis sativus</span>, <span class="html-italic">Zea mays</span>, <span class="html-italic">Oryza sativa</span>, <span class="html-italic">Arabidopsis thaliana</span>, and <span class="html-italic">Solanum lycopersicum</span>, respectively.</p>
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<p>Expression analysis of <span class="html-italic">CsTRMs</span> in the fruit: The transcriptional levels of <span class="html-italic">CsTRM</span> genes in GFC (carpel number = 5) and 32X (carpel number = 3) (<b>A</b>), 408 (long fruit) and 409 (short fruit) (<b>B</b>), and WT and <span class="html-italic">CsFUL1<sup>A</sup></span>-OX (<b>C</b>) are shown on the heatmaps. A color scale range of −2.0 to 2.0 and −1.5 to 1.5 was applied, based on the normalized values. The color gradient, from blue to red, represents increasing expression levels. GFC, mutant Gui Fei Cui (GFC) from South China-type cucumber 32X. The carpel number changed from 3 in 32X to 5 in GFC, despite the number of other floral organs, such as sepal, petal, and stamen, remaining unchanged. WT, empty vector/control transgenic plants. FC, fold-change. (<b>D</b>) qRT-PCR analysis of <span class="html-italic">CsTRM</span> expression of the cucumber ovary at 4 days before anthesis (4 DBA) and 0 days after anthesis (0 DAA) at the long fruit CSSL2-7 and the round fruit RNS7. The gene of cucumber Actin served as reference gene. The standard error of the mean is represented by the error bars (<span class="html-italic">n</span> = 3). Significance analysis was performed with the two-tailed Student’s <span class="html-italic">t</span>-test (ns <span class="html-italic">p</span> &gt; 0.05, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001).</p>
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<p>Expression patterns of <span class="html-italic">CsTRM</span> genes in response to abiotic stress: The heatmap displays the gene expression levels of <span class="html-italic">CsTRM</span> genes in response to salt (<b>A</b>) and heat (<b>B</b>) tolerance. A color scale range of –3.0 to 3.0 was applied, based on the normalized values. The color gradient, from blue to red, represents increasing expression levels. Abbreviations include CT for control treatment; HT for heat treatment; HT0h for HT at 0 h; HT3h for HT at 3 h; and HT6h for HT at 6 h. FC, fold-change.</p>
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<p>Expression analysis of <span class="html-italic">CsTRMs</span> under biotic stresses: The heatmaps displays the transcriptional levels of <span class="html-italic">CsTRM</span> genes in response to powdery mildew (PM) for 48 h (<b>A</b>), gray mold (GM) for 96 h (<b>B</b>), and downy mildew (DM) for 1–8 days post inoculation (<b>C</b>). A color scale range of –3.0 to 3.0 was applied, based on the normalized values. The color gradient, from blue to red, represents increasing expression levels. Abbreviations include ID for PM-inoculated susceptible cucumber line D8 leaves; NID for non-inoculated D8 leaves; IS for PM-inoculated resistant cucumber line SSL508-28 leaves; NIS for non-inoculated SSL508-28 leaves; CT for without inoculation; DPI for days post inoculation; and FC for fold-change. (<b>D</b>) qRT-PCR analysis of <span class="html-italic">CsTRM</span> expression of the cotyledons of cucumber seedlings inoculated with gray mold (GM) at 0 h, 6 h, 24 h, and 72 h, and maintaining environmental humidity after inoculation was necessary. The gene of cucumber Actin served as reference gene. The standard error of the mean is represented by the error bars (<span class="html-italic">n</span> = 3). Significance analysis was performed with the two-tailed Student’s <span class="html-italic">t</span>-test (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001).</p>
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21 pages, 7557 KiB  
Article
Vitis rotundifolia Genes Introgressed with RUN1 and RPV1: Poor Recombination and Impact on V. vinifera Berry Transcriptome
by Mengyao Shi, Stefania Savoi, Gautier Sarah, Alexandre Soriano, Audrey Weber, Laurent Torregrosa and Charles Romieu
Plants 2024, 13(15), 2095; https://doi.org/10.3390/plants13152095 - 29 Jul 2024
Viewed by 636
Abstract
Thanks to several Vitis vinifera backcrosses with an initial V. vinifera L. × V. rotundifolia (previously Muscadinia rotundifolia) interspecific cross, the MrRUN1/MrRPV1 locus (resistance to downy and powdery mildews) was introgressed in genotypes phenotypically close to V. vinifera varieties. To check the [...] Read more.
Thanks to several Vitis vinifera backcrosses with an initial V. vinifera L. × V. rotundifolia (previously Muscadinia rotundifolia) interspecific cross, the MrRUN1/MrRPV1 locus (resistance to downy and powdery mildews) was introgressed in genotypes phenotypically close to V. vinifera varieties. To check the consequences of introgressing parts of the V. rotundifolia genome on gene expression during fruit development, we conducted a comparative RNA-seq study on single berries from different V. vinifera cultivars and V. vinifera × V. rotundifolia hybrids, including ‘G5’ and two derivative microvine lines, ‘MV102’ (resistant) and ‘MV32’ (susceptible) segregating for the MrRUN1/RPV1 locus. RNA-Seq profiles were analyzed on a comprehensive set of single berries from the end of the herbaceous plateau to the ripe stage. Pair-end reads were aligned both on V. vinifera PN40024.V4 reference genome, V. rotundifolia cv ‘Trayshed’ and cv ‘Carlos’, and to the few resistance genes from the original V. rotundifolia cv ‘52’ parent available at NCBI. Weighted Gene Co-expression Network Analysis (WGCNA) led to classifying the differentially expressed genes into 15 modules either preferentially correlated with resistance or berry phenology and composition. Resistance positively correlated transcripts predominantly mapped on the 4–5 Mb distal region of V. rotundifolia chromosome 12 beginning with the MrRUN1/MrRPV1 locus, while the negatively correlated ones mapped on the orthologous V. vinifera region, showing this large extremity of LG12 remained recalcitrant to internal recombination during the successive backcrosses. Some constitutively expressed V. rotundifolia genes were also observed at lower densities outside this region. Genes overexpressed in developing berries from resistant accessions, either introgressed from V. rotundifolia or triggered by these in the vinifera genome, spanned various functional groups, encompassing calcium signal transduction, hormone signaling, transcription factors, plant–pathogen-associated interactions, disease resistance proteins, ROS and phenylpropanoid biosynthesis. This transcriptomic insight provides a foundation for understanding the disease resistance inherent in these hybrid cultivars and suggests a constitutive expression of NIR NBS LRR triggering calcium signaling. Moreover, these results illustrate the magnitude of transcriptomic changes caused by the introgressed V. rotundifolia background in backcrossed hybrids, on a large number of functions largely exceeding the ones constitutively expressed in single resistant gene transformants. Full article
(This article belongs to the Collection Advances in Plant Breeding)
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<p>Principal component analysis of 102 single-berry RNA-seq samples, with gene expression monitored at various dates ranging from 3 to 10 depending on genotypes. PCA was performed on variance-stabilized transforms of RNA-Seq data on 29516 genes. G represents G5. Me and M represent two different Merlot clones, and Sy represents Syrah genotypes. MV32: G5 descendant devoid of the MrRUN1/MrRPV1 locus, MV102: G5 descendant introgressed for the MrRUN1/MrRPV1 locus. Within each cultivar, samples are ranked according to their respective sampling dates, indicating that the developmental stage has a major effect on the pattern of gene expression pattern than genotypes.</p>
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<p>DEGs of berries with resistant versus non-resistant genotypes. (<b>A</b>) The <span class="html-italic">X</span>-axis represents down-regulated and up-regulated genes, while the <span class="html-italic">Y</span>-axis is the number of regulated genes. (<b>B</b>) The <span class="html-italic">X</span>-axis represents the reference genome, and 7R presents the seven resistance genes sequenced in the RUN1/RPV1 locus of <span class="html-italic">V. rotundifolia</span> 52 [<a href="#B32-plants-13-02095" class="html-bibr">32</a>], which is the right Muscadinia genetic background of G5, MV102, and MV32 genotypes. Trayshed and Carlos stands for <span class="html-italic">V. rotundifolia</span> cv ‘Trayshed’, and ‘Carlos’ respective genomes. The <span class="html-italic">Y</span>-axis represents the DEGs number in each genome.</p>
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<p>Gene ontology enrichment analysis of MrRUN1/MrRPV1 introgressed berries versus native <span class="html-italic">vinifera</span> ones. (<b>A</b>) Top ten GO terms of DEGs between non-resistant vs. resistant berries. (<b>B</b>) Top ten uniquely enriched GO terms in up-regulated genes. (<b>C</b>) Top ten uniquely enriched GO terms in down-regulated genes. CC, cellular component; MF, molecular function; BP, biological process. Gene Ontology enrichment plots show detected GO terms (under 0.05 in Fischer’s exact tests), color-coded by their adjusted <span class="html-italic">p</span>-value, and shifted in the <span class="html-italic">y</span>-axis depending on the number of genes matching this ontology.</p>
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<p>Gene ontology enrichment analysis of MrRUN1/MrRPV1 introgressed berries versus native <span class="html-italic">vinifera</span> ones. (<b>A</b>) Top ten GO terms of DEGs between non-resistant vs. resistant berries. (<b>B</b>) Top ten uniquely enriched GO terms in up-regulated genes. (<b>C</b>) Top ten uniquely enriched GO terms in down-regulated genes. CC, cellular component; MF, molecular function; BP, biological process. Gene Ontology enrichment plots show detected GO terms (under 0.05 in Fischer’s exact tests), color-coded by their adjusted <span class="html-italic">p</span>-value, and shifted in the <span class="html-italic">y</span>-axis depending on the number of genes matching this ontology.</p>
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<p>Construction of the gene co-expression network from DEGs in single berries from resistant vs. susceptible genotypes. (<b>A</b>) No glaring outlier emerged from sample clustering (<b>B</b>) Network topology analysis showed that at β = 10, the network satisfied the scale-free topology threshold of 0.9. When β = 10, network topology analysis showed that the mean connectedness was almost zero. (<b>C</b>) Gene dendrogram constructed by clustering dissimilarity (MEDissThres = 0.4). Color-coded modules represented by lines reflecting the consensus topological overlap. The cluster dendrogram at the top shows co-expressed genes. The branches and color bands at the bottom represent the assigned module. Every module has a distinct color that designates a group of co-expressed genes.</p>
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<p>A dendrogram and trait heatmap of the samples. The leaves of the tree correspond to samples (method = “average”). The first color band underneath the tree documents the tolerant and susceptible status, in red and white, respectively. The remaining colored bands from top to bottom represent malic, tartaric, and sugar concentrations, respectively.</p>
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<p><b>A</b> module-trait associations diagram, Eigengene dendrogram, and Eigengene adjacency heatmap. (<b>A</b>) A heatmap showing associations between traits and gene expression modules. Figures indicate correlations coefficient and (<span class="html-italic">p</span> values). (<b>B</b>) A diagram showing the modules’ Eigengenes’ hierarchical clustering. (<b>C</b>) A heatmap showing the hub gene network’s adjacency relationships.</p>
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<p>(<b>A</b>) Gene module membership (MM) vs. gene significance (GS) in the yellow-green module. MM represents the correlation between each gene expression profile and that of the module eigengene. GS represents the association between gene expression and resistance. GS and MM are exceptionally well correlated in this module (0.98, <span class="html-italic">p</span> &lt; 1 × 10<sup>−200</sup>). Resistance gene analogs (RGA) sequences available at NCBI in the true parental MrRUN1/MrRPV1 locus [<a href="#B30-plants-13-02095" class="html-bibr">30</a>] are indicated in red. (<b>B</b>) Eigengene expression pattern in 102 single berries.</p>
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<p>Heatmap of the expression levels of the top 40 annotated genes in the yellow-green module (Module Membership &gt; 0.8) in different samples. Rows: single gene expression and function. Columns: susceptible and tolerant samples. Side Dendrogram: gene clustered according to their expression patterns (clustering_method = “complete”).</p>
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<p>(<b>A</b>) A scatter plot of green-yellow module membership (MM) vs. gene significance (GS) (cor = 0.51, <span class="html-italic">p</span> &lt; 1 × 10<sup>−8</sup>). MM represents the correlation between gene expression and that of module eigengene. GS represents the association between gene expression and treatment. (<b>B</b>) An Eigengene expression pattern. (<b>C</b>) A heatmap of the expression levels of green-yellow module genes across various samples (MM &gt; 0.8). Rows: represent a single gene. Columns: represent different samples. clustering_method = “complete”.</p>
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<p>A representation of the genomic positions of yellow-green and green-yellow module genes (|MM| &gt; 0.8). Red label: genes from the yellow-green module, blue label: genes from the green-yellow module, specifically expressed in G5.</p>
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<p>Genomic positions of yellow-green and green-yellow module genes associated with Run1/Rpv1 locus. X-axis: gene position in chr12. Y-axis: gene MM. The red point in the black circle represents the genes mapped to PN40024.V4; the green point in the black circle represents the genes identified in <span class="html-italic">V. rotundifolia</span> ‘Carlos’; the yellow point in the black circle represents the genes identified in <span class="html-italic">V. rotundifolia</span> ‘Trayshed’; Yellow point in the red circle represents the VMC4f3.1 microsatellite marker. The green point in the red circle represents the VMC8g9 marker.</p>
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<p>Pedigree of the fungus-tolerant genotypes (adapted from Ojeda et al., 2017 [<a href="#B70-plants-13-02095" class="html-bibr">70</a>]. G5 is a macrovine phenotype and MV102 and MV32 are two microvine lines, the first one carrying the RUN1/RPV1 locus and the second without it.</p>
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13 pages, 1347 KiB  
Article
A Sustainable Strategy for Marker-Assisted Selection (MAS) Applied in Grapevine (Vitis spp.) Breeding for Resistance to Downy (Plasmopara Viticola) and Powdery (Erysiphe Necator) Mildews
by Tyrone Possamai, Leonardo Scota, Riccardo Velasco and Daniele Migliaro
Plants 2024, 13(14), 2001; https://doi.org/10.3390/plants13142001 - 22 Jul 2024
Viewed by 605
Abstract
Plant breeders utilize marker-assisted selection (MAS) to identify favorable or unfavorable alleles in seedlings early. In this task, they need methods that provide maximum information with minimal input of time and economic resources. Grape breeding aimed at producing cultivars resistant to pathogens employs [...] Read more.
Plant breeders utilize marker-assisted selection (MAS) to identify favorable or unfavorable alleles in seedlings early. In this task, they need methods that provide maximum information with minimal input of time and economic resources. Grape breeding aimed at producing cultivars resistant to pathogens employs several resistance loci (Rpv, Ren, and Run) that are ideal for implementing MAS. In this work, a sustainable MAS protocol was developed based on non-purified DNA (crude), multiplex PCR of SSR markers, and capillary electrophoresis, and its application on grapevine seedlings to follow some main resistance loci was described. The optimized protocol was utilized on 8440 samples and showed high efficiency, reasonable throughput (2–3.2 min sample), easy handling, flexibility, and tolerable costs (reduced by at least 3.5 times compared to a standard protocol). The Rpv, Ren, and Run allelic data analysis did not show limitations to loci combination and pyramiding, but segregation distortions were frequent and displayed both low (undesired) and high rates of inheritance. The protocol and results presented are useful tools for grape breeders and beyond, and they can address sustainable changes in MAS. Several progenies generated have valuable pyramided resistance and will be the subject of new studies and implementation in the breeding program. Full article
(This article belongs to the Special Issue Genetics of Disease Resistance in Horticultural Crops)
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<p>(<b>a</b>) Relative fluorescence units (RFU) recorded in capillary electrophoresis for the simple sequence repeats markers (SSR) for the marker-assisted selection. Dashed lines delimited the optimal RFU (1000–8000) for semi-automated analysis with GeneMapper 4.0 software. (<b>b</b>) Histogram summarizing the distribution of all the RFU yielded (<span class="html-italic">x</span>-axis represents the counts for each bin displayed).</p>
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<p>Proportional representation of costs per single plant sample for the optimized MAS protocol based on non-purified DNA (crude) amplification (green) and on traditional DNA extraction Kit (Plant DNeasy Mini Kit, Qiagen, Hilden, Germany), and amplification (violet). In the crude DNA-based protocol, costs are reduced by at least 3.5 times: costs associated with PCR increase for the utilization of a Taq polymerase adapted for direct amplifications, while reagents and material for DNA obtaining represent the cheapest part of the process.</p>
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<p>Number of seedlings (N) that inherited none (-), one (1), and two (2) or more than two (2+) <span class="html-italic">Rpv</span>, <span class="html-italic">Ren</span>, and <span class="html-italic">Run</span> loci. Details for the number of seedlings carrying the different combinations of resistance loci are reported in <a href="#app1-plants-13-02001" class="html-app">Table S4</a>.</p>
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<p>Schematic representation of the protocol implemented to obtain the crude DNA for the amplification of simple sequence repeats (SSRs) markers for the marker-assisted selection (MAS).</p>
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17 pages, 14181 KiB  
Article
Cucumber Downy Mildew Disease Prediction Using a CNN-LSTM Approach
by Yafei Wang, Tiezhu Li, Tianhua Chen, Xiaodong Zhang, Mohamed Farag Taha, Ning Yang, Hanping Mao and Qiang Shi
Agriculture 2024, 14(7), 1155; https://doi.org/10.3390/agriculture14071155 - 16 Jul 2024
Viewed by 488
Abstract
It is of great significance to develop early prediction technology for controlling downy mildew and promoting cucumber production. In this study, a cucumber downy mildew prediction method was proposed by fusing quantitative disease information and environmental data. Firstly, the number of cucumber downy [...] Read more.
It is of great significance to develop early prediction technology for controlling downy mildew and promoting cucumber production. In this study, a cucumber downy mildew prediction method was proposed by fusing quantitative disease information and environmental data. Firstly, the number of cucumber downy mildew spores during the experiment was collected by a portable spore catcher, and the proportion of cucumber downy mildew leaf area to all cucumber leaf area was recorded, which was used as the incidence degree of cucumber plants. The environmental data in the greenhouse were monitored and recorded by the weather station in the greenhouse. Environmental data outside the greenhouse were monitored and recorded by a weather station in front of the greenhouse. Then, the influencing factors of cucumber downy mildew were analyzed based on the Pearson correlation coefficient method. The influencing factors of the cucumber downy mildew early warning model in greenhouse were identified. Finally, the CNN-LSTM (Convolutional Neural Network-Long Short-Term Memory) algorithm was used to establish the cucumber downy mildew incidence prediction model. The results showed that the Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and determination coefficient (R2) of the CNN-LSTM network model were 0.069, 0.0098, 0.0991, and 0.9127, respectively. The maximum error between the predicted value and the true value for all test sets was 16.9398%. The minimum error between the predicted value and the true value for all test sets was 0.3413%. The average error between the predicted and true values for all test sets was 6.6478%. The Bland–Altman method was used to analyze the predicted and true values of the test set, and 95.65% of the test set data numbers were within the 95% consistency interval. This work can serve as a foundation for the creation of early prediction models of greenhouse crop airborne diseases. Full article
(This article belongs to the Section Digital Agriculture)
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<p>Cucumber plant cultivation.</p>
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<p>Capture of cucumber downy mildew spores.</p>
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<p>Weather stations: (<b>a</b>) indoor weather station; (<b>b</b>) outdoor weather station.</p>
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<p>Number of spores and incidence of cucumber: (<b>a</b>) number of spores and incidence of cucumber during the test period from 6 July to 5 August 2020; (<b>b</b>) number of spores and incidence of cucumber during the test period from 31 August to 30 September 2020; (<b>c</b>) number of spores and incidence of cucumber during the test period from 10 April to 10 May 2021; (<b>d</b>) number of spores and incidence of cucumber during the test period from 15 October to 14 November 2021.</p>
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<p>The average temperature and humidity inside the greenhouse, the average temperature and humidity outside the greenhouse, and the rainfall outside the greenhouse: (<b>a</b>) the average temperature and humidity inside the greenhouse, the average temperature and humidity outside the greenhouse, and the rainfall outside the greenhouse during the test period from 6 July to 5 August 2020; (<b>b</b>) the average temperature and humidity inside the greenhouse, the average temperature and humidity outside the greenhouse, and the rainfall outside the greenhouse during the test period from 31 August to 30 September 2020; (<b>c</b>) the average temperature and humidity inside the greenhouse, the average temperature and humidity outside the greenhouse, and the rainfall outside the greenhouse during the experiment period from 1 April to 1 May 2021; (<b>d</b>) the average temperature and humidity inside the greenhouse, the average temperature and humidity outside the greenhouse, and the rainfall outside the greenhouse during the experiment period from 15 October to 14 November 2021.</p>
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<p>Correlation analysis of model factors.</p>
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<p>Structure diagram of CNN-LSTM.</p>
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<p>Running results of CNN network model: (<b>a</b>) loss for the training and validation sets; (<b>b</b>) error for the training and validation sets; (<b>c</b>) comparison between predicted and true values for the test set; (<b>d</b>) comparison between the training and validation sets; (<b>e</b>) comparison between training, validation, and test sets.</p>
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<p>Running results of LSTM network model: (<b>a</b>) loss for the training and validation sets; (<b>b</b>) error for the training and validation sets; (<b>c</b>) comparison between predicted and true values for the test set; (<b>d</b>) comparison between the training and validation sets; (<b>e</b>) comparison between training, validation, and test sets.</p>
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<p>Running results of CNN-LSTM network model: (<b>a</b>) loss for the training and validation sets; (<b>b</b>) error for the training and validation sets; (<b>c</b>) comparison between predicted and true values for the test set; (<b>d</b>) comparison between the training and validation sets; (<b>e</b>) comparison between training, validation, and test sets.</p>
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<p>Error between the true and predicted values of the test set.</p>
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<p>Bland–Altman analysis for true and predicted values of the test set.</p>
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14 pages, 4468 KiB  
Brief Report
New Strain of Bacillus amyloliquefaciens G1 as a Potential Downy Mildew Biocontrol Agent for Grape
by Wenyan Qiao, Xingjiao Kang, Xiwei Ma, Longxian Ran and Zhixian Zhen
Agronomy 2024, 14(7), 1532; https://doi.org/10.3390/agronomy14071532 - 15 Jul 2024
Viewed by 525
Abstract
To obtain effective biocontrol strains for downy mildew of grape, 38 endophytic bacteria were isolated from fruits, seeds, and old stems of six grape varieties. Using spot inoculation mixtures of sporangial suspensions of Plasmopara viticola and biocontrol bacterial suspension, this screen yielded three [...] Read more.
To obtain effective biocontrol strains for downy mildew of grape, 38 endophytic bacteria were isolated from fruits, seeds, and old stems of six grape varieties. Using spot inoculation mixtures of sporangial suspensions of Plasmopara viticola and biocontrol bacterial suspension, this screen yielded three strains (G1, G5, and G9) with good antagonistic effects against P. viticola. The growth inhibition rate was 100%, which was comparable to the effect of the positive control Bacillus subtilis strain CN181. The enzyme activity and the metabolites of strain G1 were examined on casein hydrolysate medium, sodium carboxymethyl cellulose agar plates, and chrome azurol sulfonate (CAS) agar plates. The antifungal protein component was identified by liquid chromatography–mass spectrometry (LC–MS). The results showed that strain G1 was more effective against Plasmopara viticola after two field trials, and the inhibition rates of strain G1 on the seventh day of the two field trials were 47.5% and 36.9%, respectively. Strain G1 was identified as Bacillus amyloliquefaciens based on morphological examination and 16S rDNA sequence analysis. It produced proteases, cellulases, and siderophores. Crude protein of the strain mainly included the putative segregation protein SpoVG, which inhibited P. viticola. Full article
(This article belongs to the Special Issue Advances in Plant Pathology of Viticulture)
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<p>Effect of endophytic antagonistic strains on the control of grape downy mildew on 19 August 2024. Note: (<b>A</b>) means disease index, (<b>B</b>) means relative control effect. The lowercase letters in <a href="#agronomy-14-01532-f001" class="html-fig">Figure 1</a> show significant differences (<span class="html-italic">p</span> &lt; 0.05). The letters a, b, and c represent the results 3 d after treatment, and a’, b’, and c’ represent the results after 7 d of treatment. The letter d’ represent the results 7 d after CK treatment.</p>
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<p>Effect of endophytic antagonistic strains on grape downy mildew in Field 2 on 2 September 2024. Note: (<b>A</b>) means disease index, (<b>B</b>) means relative control effect. The lowercase letters in <a href="#agronomy-14-01532-f001" class="html-fig">Figure 1</a> show significant differences (<span class="html-italic">p</span> &lt; 0.05). The letters a, b, and c represent the results 3 d after treatment, and a’, b’, and c’; represent the results after 7 d of treatment. The letter d represents the results 3 d after Control treatment, and the letter d’ represents the results 7 d after Control treatment.</p>
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<p>Colony morphology of endophyte G1.</p>
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<p>Phylogenetic tree of 16S rDNA of endophyte G1.</p>
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<p>Endogenous bacteria G1 Protease (<b>a</b>), Cellulase (<b>b</b>), and Siderophore (<b>c</b>) activity detection.</p>
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<p>Colony diameter and inhibition rate of <span class="html-italic">Botrytis cinerea</span> by salting-out of ammonium sulfate with different saturation. a~e representative colony diameter range.</p>
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<p>Inhibitory ability of <span class="html-italic">B. amyloliquefaciens</span> G1 against <span class="html-italic">B. cinerea.</span> (<b>A</b>): Control hyphae; (<b>B</b>–<b>D</b>): Hyphae treated with crude protein extraction solution produced by G1.</p>
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<p>Results of anion exchange chromatography.</p>
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<p>Antifungal test of five protein elution peaks. Note: I–V are protein agar blocks of five different NaCl concentrations.</p>
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25 pages, 4039 KiB  
Article
Broad-Spectrum Efficacy and Modes of Action of Two Bacillus Strains against Grapevine Black Rot and Downy Mildew
by Robin Raveau, Chloé Ilbert, Marie-Claire Héloir, Karine Palavioux, Anthony Pébarthé-Courrouilh, Tania Marzari, Solène Durand, Josep Valls-Fonayet, Stéphanie Cluzet, Marielle Adrian and Marc Fermaud
J. Fungi 2024, 10(7), 471; https://doi.org/10.3390/jof10070471 - 9 Jul 2024
Viewed by 830
Abstract
Black rot (Guignardia bidwellii) and downy mildew (Plasmopara viticola) are two major grapevine diseases against which the development of efficient biocontrol solutions is required in a context of sustainable viticulture. This study aimed at evaluating and comparing the efficacy [...] Read more.
Black rot (Guignardia bidwellii) and downy mildew (Plasmopara viticola) are two major grapevine diseases against which the development of efficient biocontrol solutions is required in a context of sustainable viticulture. This study aimed at evaluating and comparing the efficacy and modes of action of bacterial culture supernatants from Bacillus velezensis Buz14 and B. ginsengihumi S38. Both biocontrol agents (BCA) were previously demonstrated as highly effective against Botrytis cinerea in grapevines. In semi-controlled conditions, both supernatants provided significant protection against black rot and downy mildew. They exhibited antibiosis against the pathogens by significantly decreasing G. bidwellii mycelial growth, but also the release and motility of P. viticola zoospores. They also significantly induced grapevine defences, as stilbene production. The LB medium, used for the bacterial cultures, also showed partial effects against both pathogens and induced plant defences. This is discussed in terms of choice of experimental controls when studying the biological activity of BCA supernatants. Thus, we identified two bacterial culture supernatants as new potential biocontrol products exhibiting multi-spectrum antagonist activity against different grapevine key pathogens and having a dual mode of action. Full article
(This article belongs to the Section Fungal Pathogenesis and Disease Control)
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<p>BCA supernatant preventive efficacy against <span class="html-italic">G. bidwellii</span> foliar symptoms originating from pycniospore-based (<b>A</b>) and ascospore-based (<b>B</b>) inocula, when applied 24 h pre-inoculation. Results are means ± SD (n = 27 and n = 8, resp.). Means followed by the same either lowercase or capital letter are not significantly different, by two-way and one-way ANOVA (α = 0.05) tests, respectively.</p>
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<p>BCA supernatant preventive efficacy against <span class="html-italic">G. bidwellii</span> foliar symptoms originating from both pycniospore- and ascospore-based inocula, when applied 48 h pre-inoculation. Results are means ± SD (n = 6). Means followed by the same lowercase or capital letter are not significantly different, by two-way ANOVA (α = 0.05).</p>
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<p>Effect of BCA supernatants on <span class="html-italic">P. viticola</span> sporulation. Leaves of cv. Marselan were sprayed with water (control) or LB medium, Buz14, and S38 supernatants at 10% and 25%. <span class="html-italic">P. viticola</span> was inoculated 48 h post-treatment, and sporulation was evaluated at 7 days post-inoculation. Results are means ± SD (n = 3). Means followed by the same letter are not significantly different, by the non-parametric Kruskal–Wallis test and pairwise Wilcoxon post-hoc test (α = 0.05). Photographs correspond to representative leaf discs.</p>
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<p>BCA supernatant <span class="html-italic">in planta</span> direct effect against <span class="html-italic">G. bidwellii</span> foliar symptoms originating from an ascospore-based inoculum, when applied 2 h pre-inoculation. Results are means ± SD (n = 14). Means followed by the same letter are not significantly different, by two-way ANOVA (α = 0.05).</p>
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<p><span class="html-italic">In planta</span> direct effect of BCA supernatants against <span class="html-italic">P. viticola</span>. Leaves (cv. Marselan) were sprayed with water (control) or LB medium, S38, and Buz14 supernatants at 25%. Inoculation was performed 2 h post-treatment. Observation by epifluorescence microscopy was performed 24 h post-inoculation, and infection sites (i.e., encysted zoospores) were counted. Results are means ± SD (n = 3). Means followed by the same letter are not significantly different, by the non-parametric Kruskal–Wallis test and pairwise Wilcoxon post-hoc test (α = 0.05).</p>
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<p><span class="html-italic">G. bidwellii</span> pycniospore germination rates determined 16 h after inoculation. Values are means ± SD (n = 8). Means followed by the same lowercase or capital letter are not significantly different, by one-way ANOVA comparison (α = 0.05).</p>
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<p><span class="html-italic">In vitro</span> direct effect against <span class="html-italic">P. viticola.</span> (<b>A</b>) Percentage of empty and full <span class="html-italic">P. viticola</span> sporangia observed 2 h after treatment. (<b>B</b>) Number of mobile <span class="html-italic">P. viticola</span> zoospores observed 2 min post-treatment. Treatments: water (control), LB medium, S38 and Buz14 supernatants at 25%. Values are means ± SD (n = 9). Means followed by the same letter are not significantly different, by the non-parametric Kruskal–Wallis test and pairwise Wilcoxon post-hoc test (α = 0.05).</p>
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<p>Relative expression of defence-related genes in grapevine leaves. Leaves of cv. Marselan were sprayed with water (as control), LB medium, S38 or Buz14 supernatants at 25% and collected at 10 hpt. Means (n = 9) followed by the same letter are not significantly different by parametric ANOVA test and Tukey post-hoc test (α = 0.05). * Fold change in gene expression was calculated with the Common Base Method (fold change = 10 −ΔΔC(w)q). ROMT: <span class="html-italic">trans</span>-resveratrol di-<span class="html-italic">O</span>-methyltransferase; STS: stilbene synthase.</p>
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<p>Principal component analysis (PCA) on all stilbenes quantified (15 in total) in treated grapevine leaves. (<b>A</b>) PCA score plot; (<b>B</b>) PCA loading plot.</p>
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<p>Stilbene content of the 12 stilbenes studied in grapevine leaves. Leaves of cv. Marselan were sprayed with water (as control), LB medium, S38 or Buz14 supernatants at 25% and collected at 24 hpt. Means followed by the same letter are not significantly different by parametric ANOVA test or non-parametric Kruskal–Wallis and Tukey post-hoc test or pairwise Wilcoxon post-hoc test (α = 0.05).</p>
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2 pages, 878 KiB  
Correction
Correction: Bleyer et al. Together for the Better: Improvement of a Model Based Strategy for Grapevine Downy Mildew Control by Addition of Potassium Phosphonates. Plants 2020, 9, 710
by Gottfried Bleyer, Fedor Lösch, Stefan Schumacher and René Fuchs
Plants 2024, 13(10), 1369; https://doi.org/10.3390/plants13101369 - 15 May 2024
Viewed by 430
Abstract
Error in Figure [...] Full article
(This article belongs to the Collection Feature Papers in Plant Protection)
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<p>Potassium phosphonates improved the effect of contact fungicides against grapevine downy mildew (GDM). Graphs show the disease incidence and severity of <span class="html-italic">P. viticola</span> in leaves and berries of <span class="html-italic">V. vinifera</span> cv. Mueller–Thurgau after the application of different fungicides in the years 2014 (<b>C</b>,<b>D</b>), 2015 (<b>E</b>,<b>F</b>), and 2016 (<b>G</b>,<b>H</b>). Green bars show results for leaves, red bars for berries. Different letters indicate significant differences between the treatments while black letters refer to disease incidence and grey letters to disease severity (one-way ANOVA; <span class="html-italic">p</span> ≤ 0.05). (<b>A</b>,<b>B</b>) show average values from all three years which were subject to large variability and therefore show no significant differences between the treatments. Cu = Cuprozin progress<sup>®</sup>, Fol = Folpan<sup>®</sup>, PP = potassium phosphonates.</p>
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12 pages, 2693 KiB  
Review
Bibliographic Analysis of Scientific Research on Downy Mildew (Pseudoperonospora humuli) in Hop (Humulus lupulus L.)
by Marcia Magalhães de Arruda, Fabiana da Silva Soares, Marcelle Teodoro Lima, Eduardo Lopes Doracenzi, Pedro Bartholo Costa, Duane Nascimento Oliveira, Thayse Karollyne dos Santos Fonsêca, Waldir Cintra de Jesus Junior and Alexandre Rosa dos Santos
Agriculture 2024, 14(5), 714; https://doi.org/10.3390/agriculture14050714 - 30 Apr 2024
Viewed by 979
Abstract
This study focused on downy mildew in hop caused by the pathogen Pseudoperonospora humuli. A systematic literature review was conducted using bibliometric analysis to explore trends in publishing, prominent research themes, and where research is being conducted on hop downy mildew. The [...] Read more.
This study focused on downy mildew in hop caused by the pathogen Pseudoperonospora humuli. A systematic literature review was conducted using bibliometric analysis to explore trends in publishing, prominent research themes, and where research is being conducted on hop downy mildew. The databases Scopus, Web of Science, and ScienceDirect were used to identify publications spanning from 1928 to 2023. The analysis yielded 54 publications, with the most cited studies primarily focusing on disease management and host resistance. Additionally, these studies explored the genetic and pathogenic relationship between P. cubensis and P. humuli. A word co-occurrence map revealed that the main themes addressed in the publications included “hop”, “disease”, “downy”, “humuli”, “mildew”, and “Pseudoperonospora”. Notably, there was a particular emphasis on subtopics such as disease management, the disease reaction of hop cultivars, and the influence of weather factors on hop downy mildew. Notably, there was limited knowledge about the disease in regions with tropical climates. This study provides valuable information that can support and guide future research endeavors concerning downy mildew in hop cultivation. Full article
(This article belongs to the Section Crop Protection, Diseases, Pests and Weeds)
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<p>Process of selection and analysis of scientific literature on hop downy mildew.</p>
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<p>Comparison of the number of articles published on hop downy mildew in the Scopus, Web of Science, and ScienceDirect databases from 1928 to 2023.</p>
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<p>Spatial distribution of the selected articles on hop downy mildew.</p>
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<p>Bibliometric map of co-occurrence networks of keywords used in this review.</p>
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<p>Similitude graph (<b>A</b>) and point cloud (<b>B</b>) of the 50 articles published on downy mildew in hops.</p>
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20 pages, 16993 KiB  
Article
Metabolically Tailored Selection of Ornamental Rose Cultivars through Polyamine Profiling, Osmolyte Quantification and Evaluation of Antioxidant Activities
by Marko Kebert, Milena Rašeta, Saša Kostić, Vanja Vuksanović, Biljana Božanić Tanjga, Olivera Ilić and Saša Orlović
Horticulturae 2024, 10(4), 401; https://doi.org/10.3390/horticulturae10040401 - 15 Apr 2024
Viewed by 1131
Abstract
Roses (genus Rosa), renowned for their economic significance and aesthetic appeal, face multifaceted challenges in cultivation due to biotic and abiotic stressors. To address these challenges, this study explores the role of osmolytes, particularly polyamines, proline and glycine betaine, as well as [...] Read more.
Roses (genus Rosa), renowned for their economic significance and aesthetic appeal, face multifaceted challenges in cultivation due to biotic and abiotic stressors. To address these challenges, this study explores the role of osmolytes, particularly polyamines, proline and glycine betaine, as well as antioxidant capacities and condensed tannins, in enhancing stress tolerance in roses. Despite the genetic diversity inherent in roses, the metabolic aspect of stress tolerance has been underexplored in breeding programs. This paper investigates the intraspecific variability among 22 rose cultivars, focusing on osmolyte content (proline and glycine betaine), individual polyamines (putrescine, spermine and spermidine), as well as antioxidant activities, measuring radical scavenging capacity against 2,2′-azinobis(3-ethylbenzothiozoline-6-sulfonic acid (ABTS•+) and NO radicals. Employing a targeted metabolomic approach, we quantified the levels of individual polyamines in both the petals and leaves of rose cultivars. This was achieved through high-performance liquid chromatography coupled with fluorescent detection following a derivatization pretreatment process. Within the evaluated cultivars, “Unique Aroma”, “Andre Rieu”, “Aroma 3”, “Frayla Marija” and “Trendy Fashion” stood out for their significantly elevated levels of total foliar polyamines. The predominant polyamine detected at both petal and leaf levels was putrescine, with concentrations ranging from 335.81 (“Zora Frayla”) to 2063.81 nmol g−1 DW (“Unique Aroma”) at the leaf level. Following putrescine, foliar spermidine levels varied from 245.08 (“Olivera Frayla”) to 1527.16 nmol g−1 DW (“Andre Rieu”). Regarding antioxidant capacity, the leaf extracts of rose cultivars “Zora Frayla” and “Natalija Frayla” were prominent by showing 68.08 and 59.24 mmol Trolox equivalents (TE) g−1 DW, respectively. The results highlight the intricate biochemical variability across rose cultivars and show that osmolytes, such as glycine betaine, proline and polyamines, and other biochemical markers can be used as reliable criteria for the selection of rose cultivars that are more resilient to biotic stress factors, especially powdery and downy mildew. Bridging fundamental research with practical applications, this study aims to contribute to the development of stress-tolerant rose cultivars adaptable to dynamic environmental conditions. Full article
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<p>Intraspecific variability in main osmolytes at leaf and petal level regarding (<b>a</b>) free proline and (<b>b</b>) glycine betaine across 22 rose cultivars. Distinct lowercase letters denote significant differences among rose cultivars based on Tukey’s honestly significant difference (HSD) post hoc test (<span class="html-italic">p</span> ≤ 0.05). The data are presented as mean ± standard deviation (SD).</p>
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<p>Intraspecific variability at leaf and petal level in major polyamines (PAs) within roses: (<b>a</b>) putrescine (PUT), (<b>b</b>) spermidine (SPD) and (<b>c</b>) spermine (SPM), across 22 rose cultivars. Distinct lowercase letters denote significant differences among rose cultivars based on Tukey’s honestly significant difference (HSD) post hoc test (<span class="html-italic">p</span> ≤ 0.05). The data are presented as mean ± standard deviation (SD).</p>
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<p>Intraspecific variability in antioxidant activities at leaf and petal level regarding (<b>a</b>) radical scavenging capacity against ABTS<sup>•+</sup>, (<b>b</b>) radical scavenging capacity against NO radical and (<b>c</b>) total condensed tannins (CTs) across 22 rose cultivars. Distinct lowercase letters denote significant differences among rose cultivars based on Tukey’s honestly significant difference (HSD) post hoc test (<span class="html-italic">p</span> ≤ 0.05). The data are represented as mean ± standard deviation (SD).</p>
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<p>Intraspecific variability in (<b>a</b>) carbon and (<b>b</b>) nitrogen amounts at leaf and petal level among 22 rose cultivars. Distinct lowercase letters denote significant differences among rose cultivars based on Tukey’s honestly significant difference (HSD) post hoc test (<span class="html-italic">p</span> ≤ 0.05). The data are represented as mean ± standard deviation (SD).</p>
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<p>Principal component analysis (PCA) of all assessed parameters across different rose cultivars at (<b>a</b>) leaf and (<b>b</b>) petal levels. Abbreviations correspond to following parameters: SPD: spermidine; SPM: spermine; PUT: putrescine; RSC ABTS: radical scavenger capacity against 2,2′-azinobis(3-ethylbenzothiozoline)-6-sulfonic acid ABTS<sup>•+</sup>; PRO: free proline; GB: glycine betaine; C. tann: condensed tannins; C: carbon; N: nitrogen content.</p>
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<p>The Pearson correlation coefficient matrix of the analyzed parameters in 22 rose cultivars. Blue squares indicate a strong and significant correlation among the examined parameters, whereas red squares indicate a lower level of interaction based on the corresponding Pearson coefficient. The abbreviations correspond to the following parameters: SPD: spermidine; SPM: spermine; PUT: putrescine; RSC ABTS: radical scavenger capacity against 2,2′-azinobis(3-ethylbenzothiozoline)-6-sulfonic acid ABTS<sup>•+</sup>; PRO: free proline; GB: glycine betaine; C. tann: condensed tannins; C: carbon; N: nitrogen content.</p>
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<p>Hierarchical clustering of 22 analyzed rose cultivars according to inspected parameters at (<b>a</b>) leaf and (<b>b</b>) petal level.</p>
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18 pages, 1075 KiB  
Article
In Vitro Collection for the Safe Storage of Grapevine Hybrids and Identification of the Presence of Plasmopara viticola Resistance Genes
by Natalya V. Romadanova, Moldir M. Aralbayeva, Alina S. Zemtsova, Alyona M. Alexandrova, Saule Zh. Kazybayeva, Natalya V. Mikhailenko, Svetlana V. Kushnarenko and Jean Carlos Bettoni
Plants 2024, 13(8), 1089; https://doi.org/10.3390/plants13081089 - 13 Apr 2024
Cited by 2 | Viewed by 957
Abstract
This paper focuses on the creation of an in vitro collection of grapevine hybrids from the breeding program of the Kazakh Scientific Research Institute of Fruit Growing and Viticulture and investigates the presence of Plasmopara viticola resistance mediated by Rpv3 and Rpv12 loci. [...] Read more.
This paper focuses on the creation of an in vitro collection of grapevine hybrids from the breeding program of the Kazakh Scientific Research Institute of Fruit Growing and Viticulture and investigates the presence of Plasmopara viticola resistance mediated by Rpv3 and Rpv12 loci. We looked at the optimization of in vitro establishment using either shoots taken directly from field-grown plants or from budwood cuttings forced indoors. We further screened for the presence of endophyte contamination in the initiated explants and optimized the multiplication stage. Finally, the presence of the resistance loci against P. viticola was studied. The shoots initiated from the field-sourced explants were the more effective method of providing plant sources for in vitro initiation once all plant accessions met the goal of in vitro establishment. The concentration of phytohormones and the acidity of the culture medium have a great effect on the multiplication rate and the quality of in vitro stock cultures. Out of 17 grapevine accessions, 16 showed the presence of single or combined resistance loci against P. viticola. The grapevine accessions identified as carrying Rpv3 and Rpv12 alleles represent important genetic resources for disease resistance breeding programs. These accessions may further contribute to the creation of new elite cultivars of economic interest. Full article
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<p>Field collection of grapevine hybrids grown in the experimental area of Kazakh Scientific Research Institute of Fruit Growing and Viticulture in Almaty (<b>A</b>). Cuttings of grapevine hybrids harvested in November (<b>B</b>) and cold-treated in a refrigerated room at 4 °C for 2 months (<b>C</b>) before moving to laboratory conditions at 24 ± 1 °C and with a photoperiod of 10 h 30 min for bud sprouting (<b>D</b>,<b>E</b>). Bar in E = 1 cm.</p>
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16 pages, 3462 KiB  
Article
A Genome-Wide Association Study of Seed Morphology-Related Traits in Sorghum Mini-Core and Senegalese Lines
by Ezekiel Ahn, Sunchung Park, Zhenbin Hu, Vishnutej Ellur, Minhyeok Cha, Yoonjung Lee, Louis K. Prom and Clint Magill
Crops 2024, 4(2), 156-171; https://doi.org/10.3390/crops4020012 - 11 Apr 2024
Viewed by 1187
Abstract
Sorghum (Sorghum bicolor L.) ranks fifth as the most crucial cereal crop globally, yet its seed morphology remains relatively unexplored. This study investigated seed morphology in sorghum based on 115 mini-core and 130 Senegalese germplasms. Eight seed morphology traits encompassing size, shape, [...] Read more.
Sorghum (Sorghum bicolor L.) ranks fifth as the most crucial cereal crop globally, yet its seed morphology remains relatively unexplored. This study investigated seed morphology in sorghum based on 115 mini-core and 130 Senegalese germplasms. Eight seed morphology traits encompassing size, shape, and color parameters were assessed. Statistical analyses explored potential associations between these traits and resistance to three major sorghum diseases: anthracnose, head smut, and downy mildew. Furthermore, genome-wide association studies (GWAS) were conducted using phenotypic data from over 24,000 seeds and over 290,000 publicly available single nucleotide polymorphisms (SNPs) through the Genome Association and Prediction Integrated Tool (GAPIT) R package. Significant SNPs associated with various seed morphology traits were identified and mapped onto the reference sorghum genome to identify novel candidate defense genes. Full article
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<p>A comparison of the area sizes for IS11473 (PI329738) and IS12697 (PI302116). The seed of (<b>a</b>) IS11473 has one of the largest areas among the seeds compared, while the seed of (<b>b</b>) IS12697 has one of the smallest areas. The scale bars on the bottom right corner indicate 1 cm for (<b>a</b>,<b>b</b>).</p>
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<p>A contrast of the seed colors for IS9108 (PI682465) and IS7987 (PI685210). The seed of (<b>a</b>) IS9108 has one of the darkest colors among the mini-core and Senegalese germplasms, while the seed of (<b>b</b>) IS7987 has one of the brightest colors. The scale bar represents 1 cm in both (<b>a</b>,<b>b</b>).</p>
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<p>Scatter plots displaying correlations (Pearson’s r) between two traits. The correlations are additionally shown with a heatmap and fit lines.</p>
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<p>The principal component analysis of all seed morphology-related traits from tested sorghum germplasms. The plot displays PC1 vs. PC2.</p>
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<p>The partial contributions of variables to seed morphology in sorghum accessions comprised of sorghum mini-core and Senegalese lines are shown. The partial contributions toward PC1 (red), PC2 (green), and PC3 (blue) are displayed for each trait.</p>
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<p>Manhattan plots of GWAS results: significant SNPs associated with eight phenotypic traits across the genome. The traits included the following: (<b>A</b>) area size; (<b>B</b>) brightness; (<b>C</b>) circularity; (<b>D</b>) distance between IS and CG; (<b>E</b>) length; (<b>F</b>) length-to-width ratio; (<b>G</b>) perimeter length; (<b>H</b>) width. The colored dots represent SNP markers. The green line indicates a Bonferroni-corrected <span class="html-italic">p</span>-value threshold of 1.7 × 10<sup>-7</sup> (-log<sub>10</sub>(<span class="html-italic">p</span>) <span class="html-italic">=</span> 6.8).</p>
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18 pages, 3540 KiB  
Article
Distribution of Plasmopara viticola Causing Downy Mildew in Russian Far East Grapevines
by Nikolay N. Nityagovsky, Alexey A. Ananev, Andrey R. Suprun, Zlata V. Ogneva, Alina A. Dneprovskaya, Alexey P. Tyunin, Alexandra S. Dubrovina, Konstantin V. Kiselev, Nina M. Sanina and Olga A. Aleynova
Horticulturae 2024, 10(4), 326; https://doi.org/10.3390/horticulturae10040326 - 27 Mar 2024
Viewed by 1120
Abstract
Downy mildew is a severe disease that leads to significant losses in grape yields worldwide. It is caused by the oomycete Plasmopara viticola. The study of the distribution of this agent and the search for endophytic organisms that inhibit the growth of P. [...] Read more.
Downy mildew is a severe disease that leads to significant losses in grape yields worldwide. It is caused by the oomycete Plasmopara viticola. The study of the distribution of this agent and the search for endophytic organisms that inhibit the growth of P. viticola are essential objectives to facilitate the transition to sustainable and high-yield agriculture, while respecting the environment. In this study, high-throughput sequencing of the ITS (ITS1f/ITS2 region) and 16S (V4 region) amplicons was employed to analyze 80 samples of leaves and stems from different grapevine species and cultivars grown in the Russian Far East (Vitis amurensis Rupr., Vitis coignetiae Pulliat, and several grapevine cultivars). The analysis revealed the presence of P. viticola in 53.75% of the grape samples. The pathogen P. viticola was not detected in V. amurensis samples collected near Vladivostok and Russky Island. Among the P. viticola-affected samples, only two (out of the eighty analyzed grape samples) from the Makarevich vineyard in Primorsky Krai exhibited disease symptoms, while the majority appeared visually healthy. We also found six distinct P. viticola ASVs in our metagenomic data. Based on phylogenetic analysis, we hypothesize that the P. viticola population in the Russian Far East may have originated from the invasive P. viticola clade aestivalis, which has spread around the world from North America. To identify putative microbial antagonists of P. viticola, a differential analysis of high-throughput sequencing data was conducted using the DESeq2 method to compare healthy and P. viticola-affected samples. The in silico analysis revealed an increased representation of certain taxa in healthy samples compared to P. viticola-affected ones: fungi—Kabatina sp., Aureobasidium sp., and Vishniacozyma sp.; bacteria—Hymenobacter spp., Sphingomonas spp., Massilia spp., Methylobacterium-Methylorubrum spp., and Chryseobacterium spp. This in-silico-obtained information on the potential microbial antagonists of P. viticola serves as a theoretical basis for the development of biocontrol agents for grapevine downy mildew. Full article
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<p>Plant material collection sites. The numbers indicate the collection sites of the plant material, which are listed in <a href="#app1-horticulturae-10-00326" class="html-app">Supporting Information Table S1</a>. The geographical map used: National Geographic World Map (esri) [<a href="#B25-horticulturae-10-00326" class="html-bibr">25</a>].</p>
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<p>Relative representation of <span class="html-italic">ITS1</span> sequences of <span class="html-italic">Plasmopara viticola</span> in grape samples: (<b>a</b>) geographic map with mean relative abundance of <span class="html-italic">P. viticola</span> in sample locations; (<b>b</b>) relative abundance of <span class="html-italic">P. viticola</span> in samples. The marks in the form of numbers on the map (<b>a</b>) correspond to the data in (<b>b</b>). The geographical map used: National Geographic World Map (esri) [<a href="#B25-horticulturae-10-00326" class="html-bibr">25</a>]. L—leaf; S—stem. Gh—<span class="html-italic">V. amurensis</span> in greenhouse at the Federal Scientific Center of the East Asia Terrestrial Biodiversity; M—<span class="html-italic">V. amurensis</span> in the commercial vineyard «Makarevich»; M-dm—<span class="html-italic">V. amurensis</span> with visible symptoms of <span class="html-italic">P. viticola</span> in «Makarevich» vineyard; S-Va—<span class="html-italic">V. amurensis</span> in the botanical garden on Sakhalin Island; P-1—<span class="html-italic">V. amurensis</span> in Vladivostok; P-2—<span class="html-italic">V. amurensis</span> in Vladivostok; P-3—<span class="html-italic">V. amurensis</span> in Russky Island; P-4—<span class="html-italic">V. amurensis</span> in Rikord Island; P-5—<span class="html-italic">V. amurensis</span> in Ivanovka village; P-6—<span class="html-italic">V. amurensis</span> in the Verkhne-Ussuriysky Research Station (SSA); Kh-1—<span class="html-italic">V. amurensis</span> in Litovko village, the southern Khabarovsky region of the Russian Far East; Kh-2—<span class="html-italic">V. amurensis</span> in the Silinsky forest; S-1—<span class="html-italic">V. coignetiae</span> in the botanical garden on Sakhalin Island; S-2—<span class="html-italic">V. coignetiae</span> near the city Kholmsk on Sakhalin Island; S-3—<span class="html-italic">V. coignetiae</span> near the city Nevelsk on Sakhalin Island; Pr-St—<span class="html-italic">Vitis</span> Elmer Swenson 2-7-13 cv. Prairie Star from commercial vineyard PRIM ORGANICA; Alfa- <span class="html-italic">Vitis labrusca</span> × <span class="html-italic">Vitis riparia</span> cv. Alfa from PRIM ORGANICA; Ad- <span class="html-italic">Vitis vinifera</span> × <span class="html-italic">V. amurensis</span> cv. Adele from commercial vineyard Makarevich; Muk—<span class="html-italic">V. riparia</span> × <span class="html-italic">V. vinifera</span> cv. Mukuzani.</p>
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<p>Evolutionary analysis of <span class="html-italic">Plasmopara viticola</span> ASVs in our NGS dataset with previously described cryptic species of <span class="html-italic">P. viticola</span> [<a href="#B42-horticulturae-10-00326" class="html-bibr">42</a>,<a href="#B43-horticulturae-10-00326" class="html-bibr">43</a>] using a maximum likelihood method. The ML method and the GTR model were utilized to deduce the evolutionary history. The tree with the highest log likelihood (−644,81) is shown. The branches display the percentage of trees in which the related taxa formed clusters, as determined by the bootstrap test (with 1000 replicates). Initial trees for the heuristic search were obtained automatically by applying the MP method. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. This analysis involved 12 nucleotide sequences. The final dataset consisted of a sum of 249 positions. The phylogenetic tree is rooted with the <span class="html-italic">Phytophthora sojae ITS</span> sequence. Evolutionary analyses were conducted in MEGA X. The original sequences, aligned sequences, and the MEGA tree session file are presented in the <a href="#app1-horticulturae-10-00326" class="html-app">Supplementary Materials</a>.</p>
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<p>The alpha diversity metrics between samples, which are grouped based on the presence of <span class="html-italic">Plasmopara viticola</span>. (<b>a</b>,<b>b</b>) Number of ASVs and Pielou’s evenness index for the endophytic bacterial community; (<b>c</b>,<b>d</b>) number of ASVs and Pielou’s evenness index for the endophytic fungal community.</p>
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<p>The comparison of endophytic bacterial and fungi communities of grapevines samples based on the presence of <span class="html-italic">Plasmopara viticola</span><b>:</b> (<b>a</b>) Bray–Curtis beta diversity NMDS plot of grape endophytic bacteria; (<b>b</b>) Bray–Curtis beta diversity NMDS plot of grape endophytic fungi. The ellipses assume a multivariate normal distribution. The central points of ellipses are mean points.</p>
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<p>Significantly different abundant (adjusted <span class="html-italic">p</span> &lt; 0.01) bacterial ASVs between grape samples, identified by the DESeq2 tool, which were grouped based on the presence of <span class="html-italic">Plasmopara viticola</span>. Dots mean ASVs, which were identified as genus-level taxa.</p>
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<p>Significantly different abundant (adjusted <span class="html-italic">p</span> &lt; 0.01) fungal ASVs between grape samples, identified by the DESeq2 tool, which were grouped based on the presence of <span class="html-italic">Plasmopara viticola</span>. Dots mean ASVs, which were identified as genus-level taxa.</p>
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