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Search Results (3,083)

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25 pages, 3273 KiB  
Article
Synergistic Effect of Iron and Zinc Nanoparticles with Recommended Nitrogen Dose on Production and Grain Quality of Maize (Zea mays L.) Cultivars Under Drought Stress
by Mohamed Abbas, Chunjie Tian, Mohamed K. I. Nagy, Maryam Sabry Al-Metwally, Xuewen Chen and Hashim M. Abdel-Lattif
Nitrogen 2024, 5(4), 1156-1180; https://doi.org/10.3390/nitrogen5040074 (registering DOI) - 18 Dec 2024
Viewed by 229
Abstract
Abiotic factors, such as drought, can significantly impact the vegetative growth and productivity of maize. To investigate the effects of the combined foliar application of zinc (Zn) and iron (Fe) nanoparticles with the recommended nitrogen dose (RND) on maize production and grain chemical [...] Read more.
Abiotic factors, such as drought, can significantly impact the vegetative growth and productivity of maize. To investigate the effects of the combined foliar application of zinc (Zn) and iron (Fe) nanoparticles with the recommended nitrogen dose (RND) on maize production and grain chemical composition under different water regimes, two field experiments were conducted in El-Ayyat city, Giza, Egypt, during the summer seasons of 2022 and 2023. This study utilized a split-split-plot experimental design with three replications. The main plots were designated to different water regimes (100, 80, 60, and 40% of estimated evapotranspiration), while the sub-plots were randomly distributed with Zn and Fe nanoparticle concentrations (0, 100, and 200 mg/L). The sub-sub-plots were randomly allocated to three maize cultivars (SC-P3062, SC-32D99, and SC-P3433). The results revealed that exposure to drought conditions resulted in a significant decline in the yield and yield-related attributes across all maize cultivars examined. Grain yield decreased by 10–50% under drought conditions. However, the foliar application of Zn and Fe nanoparticles was found to significantly improve grain yield, protein content, oil content, starch content, crude fiber, ash, and macro- and micronutrient concentrations in the maize cultivars under control and drought stress conditions. The foliar application of Zn and Fe nanoparticles at a concentration of 200 mg/L to the SC-P3433 maize cultivar led to the greatest grain yield per hectare, reaching 11,749 and 11,657 kg under the irrigation regimes with 100 and 80% total evapotranspiration, respectively. According to the assessment using the relative drought index, the SC-P3062 maize cultivar demonstrated tolerance (T) to water stress conditions. In conclusion, the foliar application of Zn and Fe nanoparticles (100–200 mg/L) effectively mitigated the negative effects of drought stress on maize plants. This approach can be recommended for farmers in arid and semi-arid regions to maintain and improve maize yield and grain quality under water-deficit conditions. Full article
(This article belongs to the Special Issue Nitrogen: Advances in Plant Stress Research)
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<p>Transmission electron microscopy (TEM) of nano-zinc particles using 10,000× magnification.</p>
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<p>Transmission electron microscopy (TEM) of nano-iron particles using 10,000× magnification.</p>
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<p>(<b>A</b>–<b>C</b>) A Pearson correlation analysis depicting the strength of the relationships among the parameters of maize investigated in this study.</p>
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14 pages, 2794 KiB  
Article
Changes in Photosynthetic Efficiency, Biomass, and Sugar Content of Sweet Sorghum Under Different Water and Salt Conditions in Arid Region of Northwest China
by Weihao Sun, Zhibin He, Bing Liu, Dengke Ma, Rui Si, Rui Li, Shuai Wang and Arash Malekian
Agriculture 2024, 14(12), 2321; https://doi.org/10.3390/agriculture14122321 - 17 Dec 2024
Viewed by 291
Abstract
Sweet sorghum (Sorghum bicolor L. Moench) has significant cultivation potential in arid and saline–alkaline regions due to its drought and salt tolerance. This study aims to evaluate the mechanisms by which increased soil salinity and reduced irrigation affect the growth, aboveground biomass, [...] Read more.
Sweet sorghum (Sorghum bicolor L. Moench) has significant cultivation potential in arid and saline–alkaline regions due to its drought and salt tolerance. This study aims to evaluate the mechanisms by which increased soil salinity and reduced irrigation affect the growth, aboveground biomass, and stem sugar content of sweet sorghum. A two-year field experiment was conducted, with four salinity levels (CK: 4.17 dS/m, S1: 5.83 dS/m, S2: 7.50 dS/m, and S3: 9.17 dS/m) and three irrigation levels (W1: 90 mm, W2: 70 mm, and W3: 50 mm). The results showed that increased salinity and reduced irrigation significantly reduced both the emergence rate and aboveground biomass, with the decreases in the emergence rate ranging from 11.0% to 36.2% and the reductions in the aboveground biomass ranging from 15.9% to 43.8%. Additionally, increased soil salinity led to reductions in stem sugar content of 6.3% (S1), 8.8% (S2), and 12.8% (S3), respectively. The results also indicated that photosynthetic efficiency, including the net photosynthetic rate (Pn), stomatal conductance (Gs), and chlorophyll content (SPAD), was significantly hindered under increased water and salt stress, with the Pn decreasing by up to 50.4% and the SPAD values decreasing by up to 36.3% under the highest stress conditions. These findings underscore the adverse impacts of increased soil salinity and reduced irrigation on sweet sorghum’s growth, photosynthetic performance, and sugar accumulation, offering critical insights for optimizing its cultivation in arid and saline environments. Full article
(This article belongs to the Special Issue Crop Response and Tolerance to Salinity and Water Stress)
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<p>The total monthly rainfalls and mean monthly temperatures during the experiment.</p>
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<p>Plant height (<b>a</b>,<b>b</b>), stem diameter (<b>c</b>,<b>d</b>), internode number (<b>e</b>,<b>f</b>) and blade number (<b>g</b>,<b>h</b>) of sweet sorghum under different water and salt stress treatments in 2021 and 2022 (<span class="html-italic">n</span> = 9). Different letters indicate significant differences between treatments at a level of α = 0.05 with Duncan’s multiple range test. Error bars represent standard deviation.</p>
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<p>Photosynthetic rate (Pn, (<b>a</b>,<b>b</b>)), stomatal conductance (Gs, (<b>c</b>,<b>d</b>)), intercellular CO<sub>2</sub> concentration (Ci, (<b>e</b>,<b>f</b>)), and chlorophyll content (SPAD, (<b>g</b>,<b>h</b>)) of sweet sorghum under different water and salt stress treatments in 2021 and 2022 (<span class="html-italic">n</span> = 9). Different letters indicate significant differences between treatments at a level of α = 0.05 with Duncan’s multiple range test. Error bars represent standard deviation.</p>
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<p>Aboveground biomass accumulation and distribution of sweet sorghum in 2021 and 2022 (<span class="html-italic">n</span> = 9). Different letters indicate significant differences between treatments at a level of α = 0.05 with Duncan’s multiple range test. Error bars represent standard deviation.</p>
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<p>Total sugar content and distribution of sweet sorghum stems in 2021 and 2022 (<span class="html-italic">n</span> = 9). Different letters indicate significant differences between treatments at a level of α = 0.05 with Duncan’s multiple range test. Error bars represent standard deviation.</p>
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<p>The Principal Component Analysis (PCA) and correlation matrix illustrating the relationships between environmental factors (irrigation quota and salinity), growth characteristics (plant height, stem diameter, internode number, and blade number), photosynthetic parameters (Pn, Gs, Ci, and SPAD), aboveground biomass, and sugar content in sweet sorghum during the 2021 and 2022 growing seasons. The PCA biplot (<b>left</b>) shows the loadings and distribution of the variables across the first two principal components (PC1: 86.1%, PC2: 5.1%). The correlation matrix (<b>right</b>) reveals significant relationships (* <span class="html-italic">p</span> ≤ 0.05) between key variables, with positive correlations shown in red and negative correlations in blue.</p>
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18 pages, 5383 KiB  
Article
Unraveling the Molecular Mechanisms of Blueberry Root Drought Tolerance Through Yeast Functional Screening and Metabolomic Profiling
by Xinyu Fan, Beijia Lin, Yahong Yin, Yu Zong, Yongqiang Li, Youyin Zhu and Weidong Guo
Plants 2024, 13(24), 3528; https://doi.org/10.3390/plants13243528 - 17 Dec 2024
Viewed by 309
Abstract
Blueberry plants are among the most important fruit-bearing shrubs, but they have shallow, hairless roots that are not conducive to water and nutrient uptake, especially under drought conditions. Therefore, the mechanism underlying blueberry root drought tolerance should be clarified. Hence, we established a [...] Read more.
Blueberry plants are among the most important fruit-bearing shrubs, but they have shallow, hairless roots that are not conducive to water and nutrient uptake, especially under drought conditions. Therefore, the mechanism underlying blueberry root drought tolerance should be clarified. Hence, we established a yeast expression library comprising blueberry genes associated with root responses to drought stress. High-throughput sequencing technology enabled the identification of 1475 genes potentially related to drought tolerance. A subsequent KEGG enrichment analysis revealed 77 key genes associated with six pathways: carbon and energy metabolism, biosynthesis of secondary metabolites, nucleotide and amino acid metabolism, genetic information processing, signal transduction, and material transport and catabolism. Metabolomic profiling of drought-tolerant yeast strains under drought conditions detected 1749 differentially abundant metabolites (DAMs), including several up-regulated metabolites (organic acids, amino acids and derivatives, alkaloids, and phenylpropanoids). An integrative analysis indicated that genes encoding several enzymes, including GALM, PK, PGLS, and PIP5K, modulate key carbon metabolism-related metabolites, including D-glucose 6-phosphate and β-D-fructose 6-phosphate. Additionally, genes encoding FDPS and CCR were implicated in terpenoid and phenylalanine biosynthesis, which affected metabolite contents (e.g., farnesylcysteine and tyrosine). Furthermore, genes for GST and GLT1, along with eight DAMs, including L-γ-glutamylcysteine and L-ornithine, contributed to amino acid metabolism, while genes encoding NDPK and APRT were linked to purine metabolism, thereby affecting certain metabolites (e.g., inosine and 3′,5′-cyclic GMP). Overall, the yeast functional screening system used in this study effectively identified genes and metabolites influencing blueberry root drought tolerance, offering new insights into the associated molecular mechanisms. Full article
(This article belongs to the Special Issue Abiotic and Biotic Stress of the Crops and Horticultural Plants)
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<p>Blueberry plant growth changes following treatments with different PEG-6000 concentrations. (<b>A</b>) Control; (<b>B</b>) 5% PEG-6000 treatment for 48 h; (<b>C</b>) 10% PEG-6000 treatment for 48 h; (<b>D</b>) 15% PEG-6000 treatment for 48 h; (<b>E</b>) 20% PEG-6000 treatment for 48 h. Bar: 2 cm.</p>
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<p>Trends in the changes in the relative water content of ‘Emerald’ blueberry leaves after treatments with different PEG-6000 concentrations. * indicates a significant difference (<span class="html-italic">p</span> &lt; 0.05) relative to the control group.</p>
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<p>Yeast library was treated with different PEG-4000 concentrations (0, 50, 80, 100, and 120 mM) to select the appropriate simulated drought concentration.</p>
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<p>Top 10 GO terms assigned to drought tolerance-related genes.</p>
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<p>Enriched KEGG pathways among drought tolerance-related genes. The dot area represents the relative number of isolated genes in the pathway, whereas the dot color represents the Q value.</p>
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<p>Verification of drought-tolerant yeast clones. The number above is the number of positive yeast clones.</p>
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<p>Metabolite classifications and proportions.</p>
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<p>Overview of the identified DAMs. (<b>A</b>) Venn diagram of the results of the comparisons of three groups (i.e., A, B, and C); (<b>B</b>–<b>D</b>) Heat maps of DAMs between different groups.</p>
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<p>Association analysis of drought tolerance-related genes and DAMs in carbohydrate metabolism pathways. (<b>A</b>) Inositol phosphate metabolism, glycolysis/gluconeogenesis, and pentose phosphate pathway; (<b>B</b>) qRT-PCR results for four drought tolerance-related genes involved in carbohydrate metabolism; (<b>C</b>) Heat map of DAMs involved in carbohydrate metabolism. Colors reflect the regulation of metabolites under drought conditions (indicated in the scale bar). * and ** represented significant difference under <span class="html-italic">p</span> &lt; 0.05 and <span class="html-italic">p</span> &lt; 0.01, respectively.</p>
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<p>Association analysis of drought tolerance-related genes and DAMs in secondary metabolite biosynthesis pathways. (<b>A</b>) Terpenoid backbone biosynthesis pathway and phenylpropanoid biosynthesis pathway; (<b>B</b>) qRT-PCR results for two drought tolerance-related genes involved in secondary metabolite biosynthesis; (<b>C</b>) Heat map of DAMs involved in secondary metabolite biosynthesis. Colors reflect the regulation of metabolites under drought conditions (indicated in the scale bar). * and ** represented significant difference under <span class="html-italic">p</span> &lt; 0.05 and <span class="html-italic">p</span> &lt; 0.01, respectively.</p>
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<p>Association analysis of drought tolerance-related genes and DAMs in amino acid metabolism pathways. (<b>A</b>) Alanine, aspartate, and glutamate metabolism and glutathione metabolism; (<b>B</b>) qRT-PCR results for six drought tolerance-related genes involved in amino acid metabolism; (<b>C</b>) Heat map of DAMs involved in amino acid metabolism. Colors reflect the regulation of metabolites under drought conditions (indicated in the scale bar). * and ** represented significant difference under <span class="html-italic">p</span> &lt; 0.05 and <span class="html-italic">p</span> &lt; 0.01, respectively.</p>
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<p>Association analysis of drought tolerance-related genes and DAMs in a nucleotide metabolism pathway. (<b>A</b>) Purine metabolism; (<b>B</b>) qRT-PCR results for six drought tolerance-related genes involved in nucleotide metabolism; (<b>C</b>) Heat map of DAMs involved in nucleotide metabolism. Colors reflect the regulation of metabolites under drought conditions (indicated in the scale bar). * and ** represented significant difference under <span class="html-italic">p</span> &lt; 0.05 and <span class="html-italic">p</span> &lt; 0.01, respectively.</p>
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<p>A conceptual model of key genes and metabolites affecting blueberry root drought resistance. This model identifies key genes and metabolites involved in carbon metabolism, secondary metabolite biosynthesis, and amino acid and nucleotide metabolism. The squares represent the genes, while the ellipses represent the metabolites. Solid arrows indicate the direct regulation of metabolites by genes, whereas dotted arrows suggest metabolites that are presumed to ultimately have functional roles.</p>
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16 pages, 3457 KiB  
Article
Genome-Wide Identification and Expression Analysis of the G-Protein Gene Family in Barley Under Abiotic Stresses
by Ailing Han, Zhengyuan Xu, Zhenyu Cai, Yuling Zheng, Mingjiong Chen, Liyuan Wu and Qiufang Shen
Plants 2024, 13(24), 3521; https://doi.org/10.3390/plants13243521 - 17 Dec 2024
Viewed by 229
Abstract
Heterotrimeric G-proteins are fundamental signal transducers highly conserved in plant species, which play crucial roles in regulating plant growth, development, and responses to abiotic stresses. Identification of G-protein members and their expression patterns in plants are essential for improving crop resilience against environmental [...] Read more.
Heterotrimeric G-proteins are fundamental signal transducers highly conserved in plant species, which play crucial roles in regulating plant growth, development, and responses to abiotic stresses. Identification of G-protein members and their expression patterns in plants are essential for improving crop resilience against environmental stresses. Here, we identified eight heterotrimeric G-protein genes localized on four chromosomes within the barley genome by using comprehensive genome-wide analysis. Phylogenetic analysis classified these genes into four distinct subgroups with obvious evolutionary relationships. Further analysis on gene structure, protein motif, and structure indicated that G-proteins within each evolutionary branch exhibited similar exon-intron organization, conserved motif patterns, and structural features. Collinearity analysis showed no significant collinear relationships among those G-protein genes, indicating a unique evolutionary trajectory within barley. Moreover, cis-regulatory elements detected in the upstream sequences of these genes were involved in response to plant hormones and signaling molecules. Expression analyses revealed tissue-specific expression patterns and differential regulation in response to abiotic stresses. The expression patterns of G-protein genes were further validated using a quantitative real-time PCR (qRT-PCR) technique, indicating the reliability of transcriptomic data, as well as special responses to salt, drought, and waterlogging stresses. These findings may provide underlying mechanisms by which G-protein genes participate in salt tolerance of barley, and also highlight candidate genes for potential genetic engineering applications in improving crop resilience to salinity stress. Full article
(This article belongs to the Special Issue Cell Physiology and Stress Adaptation of Crops)
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<p>Phylogeny and gene ontology (GO) analysis of G-protein genes. (<b>A</b>) Number of G-protein family genes in diploid and polyploids of different species; (<b>A</b>) gene ontology (GO) annotation and GO enrichment analysis of G-protein genes; (<b>B</b>) phylogenetic tree of G-protein family in different crop species, including barley, <span class="html-italic">Arabidopsis</span>, rice, maize, soybean, sorghum, wheat, rapeseed, and millet.</p>
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<p>Gene structure, conserved motifs, domains, and chromosomal localization of G-protein family members in barley. (<b>A</b>) Analysis of gene structure (left), conserved motifs (middle), and domains (right) of G-protein family in barley; (<b>B</b>) chromosome location maps of G-protein genes in barley. The left side scale bar is for the physical length of chromo-some (Mb).</p>
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<p>Collinearity, selection pressure analysis, and tertiary structure prediction of heterotrimeric G-protein family in barley. (<b>A</b>) Collinearity diagram of barley G-protein with <span class="html-italic">Arabidopsis thaliana</span> and rice.Gray lines: all synteny blocks in the <span class="html-italic">H. vulgare</span> genome. Blue lines: duplicated gene pairs; (<b>B</b>) Ka/Ks ratios of duplicated pairs of G-protein in barley; (<b>C</b>) the 3D structure modeling of G-protein in barley; (<b>C</b>) The 3D structure modeling of G-protein in barley. The pymol software (3.1.3) was used to create the structural image.</p>
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<p>The homeostatic cis-elements of G-protein regulatory genes in barley.</p>
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<p>Functional and regulatory network of heterotrimeric G-protein genes in barley. (<b>A</b>) Protein–protein interaction (PPI) network of G-protein in barley; (<b>B</b>) predictive protein interaction analysis of G-protein in barley. Gray connecting lines represent the predicted protein interactions, with the color gradually increasing from dark (red) to light (yellow), indicating a gradual increase in the number of interacting genes.</p>
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<p>Expression patterns of heterotrimeric G-protein regulatory genes in different tissues and under abiotic stresses. (<b>A</b>) Expression heatmap of G-protein during barely development in different seedling tissues in the database. Note: EMB: Germinating embryos after 4 days; ROO1: roots from seedlings; LEA: shoots from seedlings; INF: developing inflorescences (1 cm); NOD: developing tillers, 3rd internode; CAR5: developing grain (5 days after pollination); CAR15: developing grain (15 days after pollination); ETI: etiolated seedling, dark cond (dark treatment for 10 days); LEM: inflorescences, lemma (42 days after pollination); LOD: inflorescences, lodicule (42 days after pollination); PAL: dissected inflorescences, palea (42 days after pollination); EPI: epidermal strips (28 days after pollination); RAC: inflorescences, rachis (35 days after pollination); ROO2: roots (28 days after pollination); SEN: senescing leaves (56 days after pollination); (<b>B</b>) expression heatmap of G-protein during barely development under salt, drought, and waterlogging conditions.</p>
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<p>Expression patterns of selected seven G-protein genes in barley under salt stress. Error bars indicate the standard deviations of at least three biological replicates, and different letters show significant differences using the LSD test at <span class="html-italic">p</span> &lt; 0.05.</p>
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18 pages, 4799 KiB  
Article
A Comprehensive Analysis In Silico of KCS Genes in Maize Revealed Their Potential Role in Response to Abiotic Stress
by Xinyi Chen, Aixia Zhang, Chenyan Liu, Muhammad Saeed, Junyi Li, Ying Wu, Yunhao Wu, Haijing Gu, Jinchao Yuan, Baohua Wang, Ping Li and Hui Fang
Plants 2024, 13(24), 3507; https://doi.org/10.3390/plants13243507 - 16 Dec 2024
Viewed by 320
Abstract
β-ketoacyl-CoA synthase (KCS) enzymes play a pivotal role in plants by catalyzing the first step of very long-chain fatty acid (VLCFA) biosynthesis. This process is crucial for plant development and stress responses. However, the understanding of KCS genes in maize remains limited. In [...] Read more.
β-ketoacyl-CoA synthase (KCS) enzymes play a pivotal role in plants by catalyzing the first step of very long-chain fatty acid (VLCFA) biosynthesis. This process is crucial for plant development and stress responses. However, the understanding of KCS genes in maize remains limited. In this study, we present a comprehensive analysis of ZmKCS genes, identifying 29 KCS genes that are unevenly distributed across nine maize chromosomes through bioinformatics approaches. These ZmKCS proteins varied in length and molecular weight, suggesting functional diversity. Phylogenetic analysis categorized 182 KCS proteins from seven species into six subgroups, with maize showing a closer evolutionary relationship to other monocots. Collinearity analysis revealed 102 gene pairs between maize and three other monocots, whereas only five gene pairs were identified between maize and three dicots, underscoring the evolutionary divergence of KCS genes between monocotyledonous and dicotyledonous plants. Structural analysis revealed that 20 out of 29 ZmKCS genes are intronless. Subcellular localization prediction and experimental validation suggest that most ZmKCS proteins are likely localized at the plasma membrane, with some also present in mitochondria and chloroplasts. Analysis of the cis-acting elements within the ZmKCS promoters suggested their potential involvement in abiotic stress responses. Notably, expression analysis under abiotic stresses highlighted ZmKCS17 as a potential key gene in the stress response of maize, which presented an over 10-fold decrease in expression under salt and drought stresses within 48 h. This study provides a fundamental understanding of ZmKCS genes, paving the way for further functional characterization and their potential application in maize breeding for enhanced stress tolerance. Full article
(This article belongs to the Special Issue Plant Fruit Development and Abiotic Stress)
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<p>Chromosomal distribution of maize <span class="html-italic">KCS</span> genes and their interchromosomal relationships. The innermost ring represents the syntenic blocks across the maize B73 genome, with grey lines indicating all such blocks. The colorful lines denote the collinear blocks of <span class="html-italic">ZmKCS</span> genes within the maize genome. The subsequent yellow and red rings correspond to gene density and the GC ratio, respectively, with each ring reflecting these genomic features at their respective locations. The outermost ring shows the physical positions of the <span class="html-italic">ZmKCS</span> genes within the maize genome.</p>
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<p>Phylogenetic analysis of <span class="html-italic">KCS</span> gene family across different species. (<b>A</b>) This section presents a phylogenetic tree of <span class="html-italic">KCS</span> gene family members from seven species, including maize and three monocots (sorghum, rice, <span class="html-italic">Brachypodium distachyon</span>), as well as three dicots (<span class="html-italic">Arabidopsis thaliana</span>, tomato, soybean). The tree was constructed using MEGA 7.0 software employing the maximum likelihood (ML) method. The tree is organized into six subgroups, each identified by a distinct background color. Monocot branches are highlighted in red, with maize <span class="html-italic">KCS</span> genes marked by green stars. The abbreviations used are as follows: EER, EES for sorghum; KQJ, KQK, PNT for <span class="html-italic">Brachypodium distachyon</span>; At for <span class="html-italic">Arabidopsis thaliana</span>; Zm for maize; Os for rice; solyc for tomato; and KRH for soybean. (<b>B</b>) This panel shows the results of a collinearity analysis of <span class="html-italic">KCS</span> genes between maize and three dicots: <span class="html-italic">Glycine max</span> (Gm), <span class="html-italic">Solanum lycopersicum</span> (Sl), and <span class="html-italic">Arabidopsis thaliana</span> (At). (<b>C</b>) This panel shows the collinearity analysis of <span class="html-italic">KCS</span> genes between maize and three monocots: <span class="html-italic">Sorghum bicolor</span> (Sb), <span class="html-italic">Oryza sativa</span> (Os), and <span class="html-italic">Brachypodium distachyon</span> (Bd). The background gray lines represent genome-wide collinear blocks, while the blue lines specifically highlight the collinearity of <span class="html-italic">KCS</span> genes, illustrating evolutionary connections and genomic conservation across these species.</p>
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<p>Gene structure and conserved motifs in <span class="html-italic">ZmKCS</span> genes. (<b>A</b>) Exon–intron structure of <span class="html-italic">ZmKCS</span> genes. (<b>B</b>) Motif analysis of ZmKCS proteins. Ten conserved motifs across 29 ZmKCS proteins, with each conserved motif represented by a unique color. Motif lengths are proportional to their representation in each protein.</p>
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<p>Subcellular localization of four of the <span class="html-italic">ZmKCS</span> genes in tobacco epidermal cells. The KCS-GFP fusion proteins are predominantly localized to the cell membrane and chloroplasts, as indicated by their fluorescence, indicating specific localization patterns.</p>
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<p>Salt stress response of <span class="html-italic">ZmKCS</span> genes. The expression levels of seven <span class="html-italic">ZmKCS</span> genes (<b>A</b>–<b>G</b>) were assessed via qRT-PCR. Maize seedlings were subjected to salt stress (150 mM NaCl), and leaf samples were collected at 0, 6, 12, 24, 36, and 48 h. The data are presented as the means ± standard errors (SEs) of three biological replicates. Statistically significant differences between the control (CK) and salt treatment groups are denoted by asterisks: * <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, **** <span class="html-italic">p</span> &lt; 0.0001 (determined by independent Student’s tests).</p>
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<p>Drought stress response of <span class="html-italic">ZmKCS</span> genes. The expression levels of seven <span class="html-italic">ZmKCS</span> genes (<b>A</b>–<b>G</b>) were validated via qRT-PCR. Seedings were subjected to drought (20% PEG6000), and leaves were sampled at 0, 6, 12, 24, 36 and 48 h. Data represent the means ± standard errors (SEs) of three biological replicates. Statistically significant differences between the control (CK) and treatment groups (PEG) are indicated by asterisks (* <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, **** <span class="html-italic">p</span> &lt; 0.0001; independent Student’s <span class="html-italic">t</span>-test).</p>
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13 pages, 2527 KiB  
Article
Exploring Drought Resistance Genes from the Roots of the Wheat Cultivar Yunhan1818
by Linyi Qiao, Lifang Chang, Mengxiang Kai, Xueqi Zhang, Tingting Kang, Lijuan Wu, Xiaojun Zhang, Xin Li, Jiajia Zhao, Zhiyong Zhao and Jun Zheng
Int. J. Mol. Sci. 2024, 25(24), 13458; https://doi.org/10.3390/ijms252413458 - 16 Dec 2024
Viewed by 295
Abstract
The root is an important organ by which plants directly sense variation in soil moisture. The discovery of drought stress-responsive genes in roots is very important for the improvement of drought tolerance in wheat varieties via molecular approaches. In this study, transcriptome sequencing [...] Read more.
The root is an important organ by which plants directly sense variation in soil moisture. The discovery of drought stress-responsive genes in roots is very important for the improvement of drought tolerance in wheat varieties via molecular approaches. In this study, transcriptome sequencing was conducted on the roots of drought-tolerant wheat cultivar YH1818 seedlings at 0, 2, and 7 days after treatment (DAT). Based on a weighted gene correlation network analysis of differentially expressed genes (DEGs), 14 coexpression modules were identified, of which five modules comprising 3107 DEGs were related to 2 or 7 DAT under drought stress conditions. A total of 223,357 single-nucleotide polymorphisms (SNPs) of these DEGs were retrieved from public databases. Using the R language package and GAPIT program, association analysis was performed between the 223,357 SNPs and the drought tolerance coefficient (DTC) values of six drought resistance-related traits in 114 wheat germplasms. The results revealed that 18 high-confidence SNPs of 10 DEGs, including TaPK, TaRFP, TaMCO, TaPOD, TaC3H-ZF, TaGRP, TaDHODH, TaPPDK, TaLectin, and TaARF7-A, were associated with drought tolerance. The RT–qPCR results confirmed that these genes were significantly upregulated by drought stress at 7 DAT. Among them, TaARF7-A contained three DTC-related SNPs, which presented two haplotypes in the tested wheat germplasms. YH1818 belongs to the Hap1 allele, which is involved in increased drought tolerance. This study revealed key modules and candidate genes for understanding the drought-stress response mechanism in wheat roots. Full article
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<p>DEGs under drought stress conditions in the roots of wheat cv. YH1818. (<b>a</b>) Number of DEGs after 20% PEG treatment and control. (<b>b</b>) Venn diagram of DEGs. C0, C2, C7: control (1/2 Hoagland’s culture mixture) at 0, 2, and 7 DAT; D0, D2, D7: drought stress (1/2 Hoagland’s culture mixture with 20% PEG-6000) at 0, 2, and 7 DAT, marked in red.</p>
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<p>WGCNA for DEGs responding to drought stress in wheat roots. (<b>a</b>) Cluster dendrogram and module colors of 8597 DEGs. (<b>b</b>) Heatmap of the relationships between the modules and drought treatment. Positive and negative correlations are represented in red and blue, respectively. Each cell lists the <span class="html-italic">p</span> value to indicate the significance of the correlation, and the cells with significant correlations are marked with yellow dashed boxes; * indicates <span class="html-italic">p</span> &lt; 0.05, ** indicates <span class="html-italic">p</span> &lt; 0.01, and **** indicates <span class="html-italic">p</span> &lt; 0.0001 according to the Pearson test. C2, C7: control at 2 and 7 DAT; D2, D7: drought stress at 2 and 7 DAT; CD0: control or drought stress at 0 DAT.</p>
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<p>SNP loci of DEGs in drought-responsive modules. (<b>a</b>) Distribution of SNPs on wheat chromosomes. (<b>b</b>) The number of SNPs on each chromosome.</p>
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<p>Manhattan plot of association analysis between SNPs of drought stress-response module genes and drought-tolerant phenotypes of 114 wheat germplasms. The threshold is set to −log<sub>10</sub> <span class="html-italic">p</span> &gt; 7. DTC: drought tolerance coefficient; PH: plant height; RL: root length; RN: root number; SFW: shoot fresh weight; RFW: root fresh weight; R/SFW: the ratio of RFW/SFW.</p>
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<p>Response of candidate genes to drought stress in the roots of wheat cv. YH1818. (<b>a</b>) Cluster diagram of the transcription data of 10 DEGs after PEG treatment. FPKM values were normalized by z-score to present different degrees of upregulation, with small upregulation changes appearing in blue and large upregulation changes appearing in red. (<b>b</b>) RT–qPCR results of 10 DEGs after drought stress. D: drought treatment; C: control. The bars indicate the standard error. * indicates <span class="html-italic">p</span> &lt; 0.05, ** indicates <span class="html-italic">p</span> &lt; 0.01, *** indicates <span class="html-italic">p</span> &lt; 0.001, and **** indicates <span class="html-italic">p</span> &lt; 0.0001 according to the <span class="html-italic">t</span> test.</p>
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<p>Two haplotypes of <span class="html-italic">TaARF7-A</span>. (<b>a</b>) Location of three SNPs 2A71380, 2A73196, and 2A74450 in the gene. Gray boxes indicate exons. (<b>b</b>,<b>c</b>) Phenotypic differences in DTC-RFW and DTC-R/SFW between Hap1 and Hap2. ** indicates <span class="html-italic">p</span> &lt; 0.01 and *** indicates <span class="html-italic">p</span> &lt; 0.001 according to a <span class="html-italic">t</span> test. (<b>d</b>) Distribution frequency of the two haplotypes in 114 wheat germplasms, including 63 landraces and 51 cultivars.</p>
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18 pages, 5981 KiB  
Article
Identification, Phylogeny, and Expression Profiling of Pineapple Heat Shock Proteins (HSP70) Under Various Abiotic Stresses
by Rui Xu, Fangjun Wei, Yanzhao Chen, Faiza Shafique Khan, Yongzan Wei and Hongna Zhang
Int. J. Mol. Sci. 2024, 25(24), 13407; https://doi.org/10.3390/ijms252413407 - 14 Dec 2024
Viewed by 255
Abstract
Pineapple (Ananas comosus (L.) Merr.) is an economically significant and delicious tropical fruit. Pineapple commercial production faces severe decline due to abiotic stresses, which affect the development and quality of pineapple fruit. Heat shock protein 70 (HSP70) plays an essential role in abiotic [...] Read more.
Pineapple (Ananas comosus (L.) Merr.) is an economically significant and delicious tropical fruit. Pineapple commercial production faces severe decline due to abiotic stresses, which affect the development and quality of pineapple fruit. Heat shock protein 70 (HSP70) plays an essential role in abiotic stress tolerance. However, the pineapple HSP70 family identification and expression analysis in response to abiotic stresses has not been studied. To explore the functional role of AcHSP70, different abiotic stress treatments were applied to pineapple cultivar “Bali” seedlings. A total of 21 AcHSP70 members were identified in the pineapple genome. The identified genes were classified into four subfamilies (I–IV) using phylogenetic analysis. The AcHSP70 family is expressed under different stress conditions. Quantitative real time polymerase chain reaction (qRT-PCR) revealed the expression pattern of the AcHSP70 family under cold, drought, salt, and heat stress. The expression level of genes such as AcHSP70-2 increased under heat, cold, and drought stress, while the expression level of genes such as AcHSP70-3 decreased under salt stress. Furthermore, the expression profile of AcHSP70s in different tissues and development stages was analyzed using transcriptome analysis. The HSP70 genes exhibited unique expression patterns in pineapple tissue at different developmental stages. The study therefore provides a list of HSP70 genes with substantial roles in abiotic stress response and valuable information for understanding AcHSP70 functional characteristics during abiotic stress tolerance in pineapple. Full article
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<p>Chromosome distribution of AcHSP70 genes in pineapple. The <span class="html-italic">AcHSP70s</span> were located on Chr 2, 3, 4, 7, 8, 13, 14, 16, 17, 19, 20, 21, 22, and 25. Chr: chromosome. The ruler located on the left side represents the chromosome length and is shown in megabase (Mb).</p>
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<p>The phylogenetic analysis of AcHSP70 proteins with <span class="html-italic">Arabidopsis</span>, cucumber (<span class="html-italic">Cucumis sativus</span> L.), rice (<span class="html-italic">Oryza sativa</span> L.), and maize (<span class="html-italic">Zea mays</span> L.). The phylogenetic tree was made by using MEGA 11.0 software with the neighbor-joining (NJ) method, and the bootstrap replications were set to 1000 times. Different colors represent four groups (I–IV), and stars represent <span class="html-italic">AcHSP70s</span>.</p>
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<p>Gene structures and conserved motifs of <span class="html-italic">AcHSP70s</span>. (<b>A</b>) Different colors represent the four groups of <span class="html-italic">AcHSP70</span> genes (I–IV). (<b>B</b>) The motifs of AcHSP70 proteins are shown as colored boxes. (<b>C</b>) Gene structures of <span class="html-italic">AcHSP70</span> genes. The yellow blocks represent the coding sequence (CDS), the green blocks represent the untranslated region (UTR), and the black lines represent introns.</p>
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<p>Three-dimensional structural analysis of <span class="html-italic">AcHSP70s</span>.</p>
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<p>The <span class="html-italic">cis</span>-acting elements in promoters of <span class="html-italic">AcHSP70</span> genes. The amounts of <span class="html-italic">cis</span>-elements in <span class="html-italic">AcHSP70s</span> promoter regions were displayed in different colors and numbers in the grid.</p>
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<p>Intraspecies synteny analysis of <span class="html-italic">AcHSP70</span> genes. The black curve represents duplication events between <span class="html-italic">AcHSP70</span> genes. Chr 1–25: Chromosome 1–25.</p>
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<p>Collinearity of <span class="html-italic">HSP70</span> genes in pineapple. The gray line represents the collinearity of all the genes in the pineapple, and the red line represents the collinearity of the <span class="html-italic">AcHSP70</span> genes.</p>
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<p>Expression profiles of <span class="html-italic">AcHSP70</span> family members in pineapple leaves with and without spines. Transcriptomic data (Le_1: Leaf apices; Le_2: Leaf base; Ro: Root; Fl: Flower; Fr: fruit) were analyzed using Log2(FPKM) values. The color scale on the right represents the relative expression level, from high (orange) to low (blue).</p>
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<p>Expression levels of <span class="html-italic">AcHSP70</span> genes under 0 h control (CK), 4 h, 12 h, 24 h, and 72 h of heat stress treatment. Data are expressed as means ± SD (<span class="html-italic">n</span> = <span class="html-italic">3</span>). Different letters indicate significant differences between groups (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Expression levels of <span class="html-italic">AcHSP70</span> genes under 0 h control (CK), 4 h, 12 h, 24 h, and 72 h of cold stress treatment. Data are expressed as means ± SD (<span class="html-italic">n</span> = <span class="html-italic">3</span>). Different letters indicate significant differences between groups (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Expression levels of <span class="html-italic">AcHSP70</span> genes under 0 h control (CK), 4 h, 12 h, 24 h, and 72 h of drought treatment. Data are expressed as means ± SD (<span class="html-italic">n</span> = <span class="html-italic">3</span>). Different letters indicate significant differences between groups (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Expression levels of <span class="html-italic">AcHSP70</span> genes under 0 h control (CK), 4 h, 12 h, 24 h, and 72 h of salt stress treatment. Data are expressed as means ± SD (<span class="html-italic">n</span> = <span class="html-italic">3</span>). Different letters indicate significant differences between groups (<span class="html-italic">p</span> &lt; 0.05).</p>
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15 pages, 3664 KiB  
Article
Poly-Glutamic Acid Regulates Physiological Characteristics, Plant Growth, and the Accumulation of the Main Medical Ingredients in the Root of Salvia miltiorrhiza Under Water Shortage
by Changjuan Shan and Yibo Zhang
Agronomy 2024, 14(12), 2977; https://doi.org/10.3390/agronomy14122977 - 13 Dec 2024
Viewed by 319
Abstract
To supply information concerning the application of poly-glutamic acid (PGA) in the drought-resistant cultivation of red sage (Salvia miltiorrhiza), we investigated the role of PGA in regulating the physiological characteristics, plant growth, and the accumulation of the main medical components in [...] Read more.
To supply information concerning the application of poly-glutamic acid (PGA) in the drought-resistant cultivation of red sage (Salvia miltiorrhiza), we investigated the role of PGA in regulating the physiological characteristics, plant growth, and the accumulation of the main medical components in the root under water shortage. The findings showed that different levels of water shortage (WS) all suppressed the photosynthetic function by reducing the net photosynthetic rate (Pn), Soil and plant analyzer development (SPAD) value, maximum photochemical efficiency of PSII (Fv/Fm), photochemical quenching (qP), and actual photochemical efficiency of PSII (Y(II)), as well as increasing non-photochemical quenching (qN). Compared with WS, PGA plus WS enhanced the photosynthetic function by reducing qN and increasing the other indicators above. For water metabolism, WS increased stomatal limit value (Ls) and water use efficiency (WUE), but decreased transpiration rate (Tr) and stomatal conductance (Gs). Compared with WS, PGA plus WS decreased Ls and increased Tr, Gs, and WUE. Meanwhile, WS enhanced the antioxidant capacity by increasing superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) activities. However, WS increased malondialdehyde (MDA) content. Compared with WS, PGA plus WS enhanced the above antioxidant enzymes. In this way, PGA reduced MDA content and improved the antioxidant capacity under WS. In addition, WS decreased the shoot and root biomass, but increased the root/shoot ratio. Compared with WS, PGA plus WS further increased the root/shoot ratio and shoot and root biomass. For medical ingredients, WS decreased the yield of rosmarinic acid (RosA) and salvianolic acid B (SalB), but increased the yield of dihydrotanshinone (DHT), cryptotanshinone (CTS), tanshinone I (Tan I), and tanshinone ⅡA (Tan ⅡA). Compared with WS, PGA plus WS increased the yield of these medical ingredients. Our findings clearly suggested that PGA application was an effective method to enhance sage drought tolerance and the yield of the main medical ingredients in sage root. This provides useful information for its application in sage production under WS. Full article
(This article belongs to the Section Water Use and Irrigation)
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<p>Effects of PGA on SPAD value (<b>A</b>) and Pn (<b>B</b>) under WS. Different letters represent significant differences between treatments at <span class="html-italic">p</span> &lt; 0.05 as determined by DMRT. The plants were treated as below. Control, 60% field water capacity; 50%FC, 50% field water capacity; 50%FC + PGA-1, 50% field water capacity + 0.22 g/L PGA; 50%FC + PGA-2, 50% field water capacity + 0.44 g/L PGA; 50%FC + PGA-3, 50% field water capacity + 0.88 g/L PGA; 40%FC, 40% field water capacity; 40%FC + PGA-1, 40% field water capacity + 0.22 g/L PGA; 40%FC + PGA-2, 40% field water capacity + 0.44 g/L PGA; 40%FC + PGA-3, 40% field water capacity + 0.88 g/L PGA.</p>
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<p>Effects of PGA on gas exchange parameters Tr (<b>A</b>), Gs (<b>B</b>), Ls (<b>C</b>), and WUE (<b>D</b>) under WS. Different letters represent significant differences between treatments at <span class="html-italic">p</span> &lt; 0.05 as determined by DMRT. The plants were treated as below. Control, 60% field water capacity; 50%FC, 50% field water capacity; 50%FC + PGA-1, 50% field water capacity + 0.22 g/L PGA; 50%FC + PGA-2, 50% field water capacity + 0.44 g/L PGA; 50%FC + PGA-3, 50% field water capacity + 0.88 g/L PGA; 40%FC, 40% field water capacity; 40%FC + PGA-1, 40% field water capacity + 0.22 g/L PGA; 40%FC + PGA-2, 40% field water capacity + 0.44 g/L PGA; 40%FC + PGA-3, 40% field water capacity + 0.88 g/L PGA.</p>
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<p>Effects of PGA on Y(Ⅱ) (<b>A</b>), F<sub>v</sub>/F<sub>m</sub> (<b>B</b>), q<sub>N</sub> (<b>C</b>), and q<sub>P</sub> (<b>D</b>) under WS. Different letters represent significant differences between treatments at <span class="html-italic">p</span> &lt; 0.05 as determined by DMRT. The plants were treated as below. Control, 60% field water capacity; 50%FC, 50% field water capacity; 50%FC + PGA-1, 50% field water capacity + 0.22 g/L PGA; 50%FC + PGA-2, 50% field water capacity + 0.44 g/L PGA; 50%FC + PGA-3, 50% field water capacity + 0.88 g/L PGA; 40%FC, 40% field water capacity; 40%FC + PGA-1, 40% field water capacity + 0.22 g/L PGA; 40%FC + PGA-2, 40% field water capacity + 0.44 g/L PGA; 40%FC + PGA-3, 40% field water capacity + 0.88 g/L PGA.</p>
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<p>Effects of PGA on SOD (<b>A</b>), POD (<b>B</b>), and CAT (<b>C</b>) activities, and MDA content (<b>D</b>) under WS. Different letters represent significant differences between treatments at <span class="html-italic">p</span> &lt; 0.05 as determined by DMRT. The plants were treated as below. Control, 60% field water capacity; 50%FC, 50% field water capacity; 50%FC + PGA-1, 50% field water capacity + 0.22 g/L PGA; 50%FC + PGA-2, 50% field water capacity + 0.44 g/L PGA; 50%FC + PGA-3, 50% field water capacity + 0.88 g/L PGA; 40%FC, 40% field water capacity; 40%FC + PGA-1, 40% field water capacity + 0.22 g/L PGA; 40%FC + PGA-2, 40% field water capacity + 0.44 g/L PGA; 40%FC + PGA-3, 40% field water capacity + 0.88 g/L PGA.</p>
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<p>Effects of PGA on shoot biomass (<b>A</b>) and root biomass (<b>B</b>), root/shoot ratio (<b>C</b>), and root volume (<b>D</b>) under WS. Different letters represent significant differences between treatments at <span class="html-italic">p</span> &lt; 0.05 as determined by DMRT. The plants were treated as below. Control, 60% field water capacity; 50%FC, 50% field water capacity; 50%FC + PGA-1, 50% field water capacity + 0.22 g/L PGA; 50%FC + PGA-2, 50% field water capacity + 0.44 g/L PGA; 50%FC + PGA-3, 50% field water capacity + 0.88 g/L PGA; 40%FC, 40% field water capacity; 40%FC + PGA-1, 40% field water capacity + 0.22 g/L PGA; 40%FC + PGA-2, 40% field water capacity + 0.44 g/L PGA; 40%FC + PGA-3, 40% field water capacity + 0.88 g/L PGA.</p>
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<p>Effects of PGA on the yield of main water-soluble medical ingredients (<b>A</b>) and main fat-soluble medical ingredients (<b>B</b>) in root under WS. Different letters represent significant differences between treatments at <span class="html-italic">p</span> &lt; 0.05 as determined by DMRT. The plants were treated as below. Control, 60% field water capacity; 50%FC, 50% field water capacity; 50%FC + PGA-1, 50% field water capacity + 0.22 g/L PGA; 50%FC + PGA-2, 50% field water capacity + 0.44 g/L PGA; 50%FC + PGA-3, 50% field water capacity + 0.88 g/L PGA; 40%FC, 40% field water capacity; 40%FC + PGA-1, 40% field water capacity + 0.22 g/L PGA; 40%FC + PGA-2, 40% field water capacity + 0.44 g/L PGA; 40%FC + PGA-3, 40% field water capacity + 0.88 g/L PGA.</p>
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<p>Pearson correlation analysis between parameters related to plant growth and medical ingredient yield and parameters related to physiological characteristics, measured in June and August. The abbreviations in this figure are as follows: SPAD = soil and plant analyzer development; Pn = net photosynthetic rate; Tr = transpiration rate; Gs = stomatal conductance; Ls = stomatal limit value; WUE = water use efficiency; Y(Ⅱ) = actual photochemical efficiency of PSII; F<sub>v</sub>/F<sub>m</sub> = maximum photochemical efficiency of PSII; q<sub>N</sub> = non-photochemical quenching; q<sub>P</sub> = photochemical quenching; SOD = superoxide dismutase; POD = peroxidase; CAT = catalase; MDA = malondialdehyde; RosA = rosmarinic acid; SalB = salvianolic acid B; DHT = dihydrotanshinone; CTS = cryptotanshinone; Tan I = tanshinone I and Tan ⅡA = tanshinone ⅡA.</p>
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7 pages, 1355 KiB  
Communication
Barley Seed Germination and Seedling Growth Responses to Polyethylene Glycol (PEG)-Induced Drought Stress
by Matthew Davidson-Willis, Guoqi Wen, Bahram Samanfar and Raja Khanal
Int. J. Plant Biol. 2024, 15(4), 1353-1359; https://doi.org/10.3390/ijpb15040093 - 13 Dec 2024
Viewed by 330
Abstract
Drought is becoming more prevalent and negatively affects the growth and development of barley. To explore the genetic variation in barley under drought stress, ten breeding genotypes were tested using polyethylene glycol-6000 to simulate drought conditions. We observed that drought stress significantly affected [...] Read more.
Drought is becoming more prevalent and negatively affects the growth and development of barley. To explore the genetic variation in barley under drought stress, ten breeding genotypes were tested using polyethylene glycol-6000 to simulate drought conditions. We observed that drought stress significantly affected germination-related traits, depending on the specific genotypes. Some parameters, such as root length, reduced by up to 85% under drought conditions compared to the control. Overall, considering the barley growth performance, the drought tolerance index was an ideal criterion for selecting drought-tolerant genotypes, as it well characterized the gradient responses of barley genotypes to drought stress. Based on this indicator, genotype OB1878-ON-50 is recommended as a significant germplasm resource for low-precipitation regions. Full article
(This article belongs to the Section Plant Reproduction)
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<p>Mean and standard error of germination percent (%) in no stress and PEG-induced drought stress condition.</p>
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<p>Mean and standard error of root length (mm) in no stress and PEG-induced drought stress condition.</p>
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<p>Mean and standard error of shoot length (mm) in no stress and PEG-induced drought stress condition.</p>
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<p>Mean and standard error of number of roots in no stress and PEG-induced drought stress condition.</p>
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21 pages, 11208 KiB  
Article
Genome-Wide Identification, Functional Characterization, and Stress-Responsive Expression Profiling of Subtilase (SBT) Gene Family in Peanut (Arachis hypogaea L.)
by Shipeng Li, Huiwen Fu, Yasir Sharif, Sheidu Abdullaziz, Lihui Wang, Yongli Zhang and Yuhui Zhuang
Int. J. Mol. Sci. 2024, 25(24), 13361; https://doi.org/10.3390/ijms252413361 - 13 Dec 2024
Viewed by 466
Abstract
Subtilases (SBTs), known as serine proteases or phytoproteases in plants, are crucial enzymes involved in plant development, growth, and signaling pathways. Despite their recognized importance in other plant species, information regarding their functional roles in cultivated peanut (Arachis hypogea L.) remains sparse. [...] Read more.
Subtilases (SBTs), known as serine proteases or phytoproteases in plants, are crucial enzymes involved in plant development, growth, and signaling pathways. Despite their recognized importance in other plant species, information regarding their functional roles in cultivated peanut (Arachis hypogea L.) remains sparse. We identified 122 AhSBT genes in the STQ peanut genome, classifying them into six subgroups based on phylogenetic analysis. Detailed structural and motif analyses revealed the presence of conserved domains, highlighting the evolutionary conservation of AhSBTs. The collinearity results indicate that the A. hypogea SBT gene family has 17, 5, and 1 homologous gene pairs with Glycine max, Arabidopsis thaliana, and Zea mays, respectively. Furthermore, the prediction of cis-elements in promoters indicates that they are mainly associated with hormones and abiotic stress. GO and KEGG analyses showed that many AhSBTs are important in stress response. Based on transcriptome datasets, some genes, such as AhSBT2, AhSBT18, AhSBT19, AhSBT60, AhSBT102, AhSBT5, AhSBT111, and AhSBT113, showed remarkably higher expression in diverse tissues/organs, i.e., embryo, root, and leaf, potentially implicating them in seed development. Likewise, only a few genes, including AhSBT1, AhSBT39, AhSBT53, AhSBT92, and AhSBT115, were upregulated under abiotic stress (drought and cold) and phytohormone (ethylene, abscisic acid, paclobutrazol, brassinolide, and salicylic acid) treatments. Upon inoculation with Ralstonia solanacearum, the expression levels of AhSBT39, AhSBT50, AhSBT92, and AhSBT115 were upregulated in disease-resistant and downregulated in disease-susceptible varieties. qRT-PCR-based expression profiling presented the parallel expression trends as generated from transcriptome datasets. The comprehensive dataset generated in the study provides valuable insights into understanding the functional roles of AhSBTs, paving the way for potential applications in crop improvement. These findings deepen our understanding of peanut molecular biology and offer new strategies for enhancing stress tolerance and other agronomically important traits. Full article
(This article belongs to the Special Issue Plant Responses to Abiotic and Biotic Stresses)
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<p>Chromosomal mapping of <span class="html-italic">AhSBTs</span> on <span class="html-italic">A. hypogea</span> genome. Map distribution of 20 chromosomes (grey bars). Representative chromosome numbers are shown on the left (black), and gene names are on the right side (red).</p>
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<p>Phylogenetic analysis of <span class="html-italic">AhSBTs</span>. (<b>A</b>) The phylogenetic tree shows <span class="html-italic">AhSBTs</span> were classified into six groups. Shades of colors represent different branches. (<b>B</b>) Number of SBT proteins of <span class="html-italic">A. hypogea</span>, <span class="html-italic">Z. mays</span>, <span class="html-italic">G. max</span>, and <span class="html-italic">A. thaliana</span> in each group of the phylogenetic tree.</p>
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<p>Gene structures and conserved domains of peanut. (<b>A</b>) <span class="html-italic">AhSBTs</span> evolutionary connection. The conserved domains of <span class="html-italic">AhSBTs</span> were shown by various colors in the right column. (<b>B</b>) <span class="html-italic">AhSBTs</span> structures, green, yellow, and black lines represent UTR regions, exons, and introns, respectively. The bar displays the length of <span class="html-italic">AhSBTs</span>.</p>
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<p>Examination of the <span class="html-italic">AhSBTs</span> promoters’ regions <span class="html-italic">cis</span>-elements. Similar colors are used to symbolize <span class="html-italic">cis</span>-elements that share a functional similarity.</p>
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<p><span class="html-italic">Cis</span>-element in the promoter regions of <span class="html-italic">AhSBTs</span>. (<b>A</b>,<b>C</b>,<b>E</b>) The total number of elements in <span class="html-italic">AhSBT</span> promoters associated with abiotic stress, phytohormones, and growth and development categories, respectively. (<b>B</b>,<b>D</b>,<b>F</b>) Pie charts display the percentage (%) ratio of the various <span class="html-italic">cis</span>-elements from each category, such as (<b>B</b>) abiotic stress sensitive, (<b>D</b>) phytohormones responsive, and (<b>F</b>) plant growth and development responsive.</p>
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<p>Distribution of <span class="html-italic">AhSBTs</span> on chromosomes and gene duplications. The gene density spread on the respective chromosomes is indicated by the outermost circle. Using colored lines, <span class="html-italic">AhSBTs</span> inside segmental duplications are connected. Tandem duplications are indicated with different colors. Orange lines show collinearity links among <span class="html-italic">AhSBTs</span>.</p>
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<p>Multiple collinearity assessments of <span class="html-italic">SBT</span> genes among <span class="html-italic">A. thaliana</span> (At), <span class="html-italic">G. max</span> (Gm), <span class="html-italic">A. hypogea</span> (Ah), and <span class="html-italic">Z. mays</span> (Zm). The red lines indicate the syntenic <span class="html-italic">SBT</span> orthologs, whereas the grey lines in the background designate the collinear blocks within peanut and the other three genomes.</p>
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<p>Gene ontology (GO) and KEGG enrichment analysis of AhSBT proteins. (<b>A</b>) The extremely rich GO terms in AhSBTs from the MF, CC, and BP categories. (<b>B</b>) Highly enriched KEGG pathways in AhSBT proteins.</p>
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<p>miRNAs targeting <span class="html-italic">AhSBTs</span>. The pictorial representation showing the physical target positions of miRNAs (ahy-miR3514-5p and ahy-miR3513-5p) on the <span class="html-italic">AhSBT69</span> and the physical target positions of miRNAs (ahy-miR156a and ahy-miR3511-5p) on <span class="html-italic">AhSBT115</span>. The black bar shows the chromosome; the red bar shows the gene position on the chromosome. The position of miRNAs on the gene sequence is indicated by the thick blue bar.</p>
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<p><span class="html-italic">AhSBTs</span> expression analysis in different tissues, hormones, and stress conditions. (<b>A</b>) The heatmap displays the expression levels of <span class="html-italic">AhSBTs</span> in 13 peanut tissues. (<b>B</b>) The expression levels of <span class="html-italic">AhSBTs</span> following treatment with five hormones. (<b>C</b>) The expression levels of <span class="html-italic">AhSBTs</span> under drought and low-temperature conditions. (<b>D</b>) Expression levels under <span class="html-italic">R. solanacearum</span> treatment in highly sensitive and highly resilient varieties to bacterial wilt (R/S-T: Treated resistant and susceptible varieties, R/S-C: Control for resistant/susceptible varieties).</p>
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<p>Quantitative expression of <span class="html-italic">AhSBTs.</span> The expression levels of representative <span class="html-italic">AhSBTs</span> under low temperature (<b>A</b>), ABA hormone treatment (<b>B</b>), and under <span class="html-italic">R. solanacearum</span> infection in varieties with high susceptibility and high resistance to bacterial wilt (<b>C</b>) (**: <span class="html-italic">p</span> &lt; 0.01, *: <span class="html-italic">p</span> &lt; 0.05).</p>
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17 pages, 3424 KiB  
Article
Role of the Foliar Endophyte Colletotrichum in the Resistance of Invasive Ageratina adenophora to Disease and Abiotic Stress
by Ailing Yang, Yuxuan Li, Zhaoying Zeng and Hanbo Zhang
Microorganisms 2024, 12(12), 2565; https://doi.org/10.3390/microorganisms12122565 - 12 Dec 2024
Viewed by 335
Abstract
Plant-associated fungi often drive plant invasion success by increasing host growth, disease resistance, and tolerance to environmental stress. A high abundance of Colletotrichum asymptomatically accumulated in the leaves of Ageratina adenophora. In this study, we aimed to clarify whether three genetically distinct [...] Read more.
Plant-associated fungi often drive plant invasion success by increasing host growth, disease resistance, and tolerance to environmental stress. A high abundance of Colletotrichum asymptomatically accumulated in the leaves of Ageratina adenophora. In this study, we aimed to clarify whether three genetically distinct endophytic Colletotrichum isolates (AX39, AX115, and AX198) activate invasive plant defenses against disease and environmental stress. We observed that, in the absence of pathogen attack and environmental stress, the foliar endophyte Colletotrichum reduced photosynthesis-related physiological indicators (i.e., chlorophyll content and soluble sugar content), increased resistance-related indicators (i.e., total phenolic (TP) and peroxidase (POD) activity), and decreased the biomass of A. adenophora. However, endophytic Colletotrichum strains exhibit positive effects on resistance to certain foliar pathogen attacks. Strains AX39 and AX115 promoted but AX198 attenuated the pathogenic effects of pathogen strains G56 and Y122 (members of Mesophoma ageratinae). In contrast, AX39 and AX115 weakened, but AX198 had no effect on, the pathogenic effect of the pathogen strain S188 (Mesophoma speciosa; Didymellaceae family). We also found that endophytes increase the biomass of A. adenophora under drought or nutrient stress. Strain AX198 significantly increased stem length and chlorophyll content under drought stress. Strain AX198 significantly increased the aboveground dry weight, AX115 increased the stem length, and AX39 significantly increased the chlorophyll content under nutrient stress. Our results revealed that there are certain positive effects of foliar Colletotrichum endophytes on A. adenophora in response to biotic and abiotic stresses, which may be beneficial for its invasion. Full article
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<p>Physiological indices of <span class="html-italic">A. adenophora</span> inoculated with endophytic <span class="html-italic">Colletotrichum</span> strains. (<b>a</b>) Chlorophyll content, (<b>b</b>) soluble sugar content, (<b>c</b>) total phenol content, and (<b>d</b>) peroxidase activity. Dots with different colors represent the raw data of each sample inoculated with the <span class="html-italic">Colletotrichum</span> AX39, AX115, and AX198 strains. The RI represents the response index, where the negative RI in panels (<b>a</b>,<b>b</b>) indicates reduced chlorophyll content and soluble sugar content in the treatment with <span class="html-italic">Colletotrichum</span> spp. infection compared with the control without <span class="html-italic">Colletotrichum</span> spp. infection. The positive RIs in panels (<b>c</b>,<b>d</b>) indicate increased total phenol content and peroxidase POD activity in the treatment with <span class="html-italic">Colletotrichum</span> spp. infection compared with the control without <span class="html-italic">Colletotrichum</span> spp. infection. Nonparametric Mann–Whitney U tests or independent sample T tests were used to identify the differences between each treatment group and the control group (* &lt;0.05, *** &lt;0.001). Post hoc comparisons were performed via Duncan’s test for equal variance and Dunnett’s test for unequal variance (T3 test) to determine whether the difference in the RI was significant among the treatments inoculated with AX39, AX115, and AX198, with different letters indicating significant differences. The error bars are the standard errors.</p>
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<p>Biomass of <span class="html-italic">A. adenophora</span> inoculated with endophytic <span class="html-italic">Colletotrichum</span> strains. (<b>a</b>) Aboveground parts, (<b>b</b>) underground parts, (<b>c</b>) branch number, (<b>d</b>) stem length, (<b>e</b>) root length, and (<b>f</b>) root–to-shoot ratio. Dots with different colors represent the raw data of each sample inoculated with the <span class="html-italic">Colletotrichum</span> AX39, AX115, and AX198 strains. A negative RI indicates a reduced biomass of <span class="html-italic">A. adenophora</span> in the experimental treatment with <span class="html-italic">Colletotrichum</span> spp. infection compared with that in the control without <span class="html-italic">Colletotrichum</span> spp. infection. Nonparametric Mann–Whitney U tests or independent sample T tests were used to identify the differences between each treatment group and the control group (* &lt;0.05, ** &lt;0.01, *** &lt;0.001). Post hoc comparisons were performed via Duncan’s test for equal variance and Dunnett’s test for unequal variance (T3 test) to determine whether the difference in the RI was significant among the treatments inoculated with AX39, AX115, and AX198, with different letters indicating significant differences. The error bars are the standard errors.</p>
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<p>LMA (dry weight per unit area) of <span class="html-italic">A. adenophora</span> inoculated with endophytic <span class="html-italic">Colletotrichum</span> strains. (<b>a</b>) The second pair of leaves, (<b>b</b>) the fifth pair of leaves. Dots with different colors represent the raw data of each sample inoculated with the <span class="html-italic">Colletotrichum</span> AX39, AX115, and AX198 strains. A negative RI indicates a reduced LMA of <span class="html-italic">A. adenophora</span> in the experimental treatment with <span class="html-italic">Colletotrichum</span> spp. infection compared with that in the control without <span class="html-italic">Colletotrichum</span> spp. infection. Nonparametric Mann–Whitney U tests or independent sample T tests were used to identify the differences between each treatment group and the control group (* &lt;0.05). Post hoc comparisons were performed via Duncan’s test for equal variance and Dunnett’s test for unequal variance (T3 test) to determine whether the difference in the RI was significant among the different treatments inoculated with AX39, AX115, and AX198, with different letters indicating significant differences. The error bars are the standard errors.</p>
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<p>The asymptomatic leaves of <span class="html-italic">A. adenophora</span> plants inoculated with a <span class="html-italic">Colletotrichum</span> spore mixture (<b>a</b>) and wounded and inoculated with agar discs of <span class="html-italic">Colletotrichum</span> (<b>b</b>). “CK” represents the control group without <span class="html-italic">Colletotrichum</span> inoculation.</p>
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<p>Pathogenicity effects of inoculating endophyte <span class="html-italic">Colletotrichum</span> strains on <span class="html-italic">A. adenophora</span> after challenge with the (<b>a</b>) pathogen G56, (<b>b</b>) pathogen Y122, and (<b>c</b>) pathogen S188. Dots with different colors represent the raw data of each sample inoculated with <span class="html-italic">the Colletotrichum</span> AX39, AX115, and AX198 strains. The specific leaf spot area and morphology are shown in (<b>d</b>); scale bar = 10 mm, and “CK” represents the control group without <span class="html-italic">Colletotrichum</span> inoculation. Nonparametric Mann–Whitney U tests or independent sample T tests were used to identify the differences between each treatment group and the control group (* &lt;0.05, *** &lt;0.001). Post hoc comparisons were performed via Duncan’s test for equal variance and Dunnett’s test for unequal variance (T3 test) to determine whether the difference in the RI was significant among the different treatments inoculated with AX39, AX115, and AX198, with different letters indicating significant differences. The error bars are the standard errors.</p>
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<p>Biomass and chlorophyll content of <span class="html-italic">A. adenophora</span> inoculated with endophyte <span class="html-italic">Colletotrichum</span> strains under normal conditions and drought stress. (<b>a</b>) Aboveground parts, (<b>b</b>) underground parts, (<b>c</b>) root/shoot ratio, (<b>d</b>) stem length, (<b>e</b>) root length, (<b>f</b>) branch number, and (<b>g</b>) chlorophyll content. A positive RI indicates an increased biomass of <span class="html-italic">A. adenophora</span> in the drought stress (−W) treatment with <span class="html-italic">Colletotrichum</span> strain (AX39, AX115, or AX198) inoculation compared with that without <span class="html-italic">Colletotrichum</span> inoculation. The formula is as follows: (treatment_Wcontrol_W)/control_W. Nonparametric Mann–Whitney U tests or independent sample T tests were used to identify the differences between each drought stress treatment group and the normal treatment group (** &lt;0.01). Post hoc comparisons were performed via Duncan’s test for equal variance and Dunnett’s test for unequal variance (T3 test) to show that the differences in the RIs were significant among the treatments inoculated with strains AX39, AX115, and AX198, with different letters indicating significant differences. The error bars are the standard errors.</p>
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<p>Growth effects of <span class="html-italic">A. adenophora</span> inoculated with endophyte <span class="html-italic">Colletotrichum</span> strains under nutrient stress and drought stress. Individuals of <span class="html-italic">A. adenophora</span> were inoculated with AX39, AX115, or AX198 and grown for one month in a plant growth chamber under nutrient stress (−N) and drought (−W).</p>
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<p>Biomass and chlorophyll content of <span class="html-italic">A. adenophora</span> plants inoculated with endophytic <span class="html-italic">Colletotrichum</span> strains under normal conditions and nutrient stress conditions. (<b>a</b>) Aboveground parts, (<b>b</b>) underground parts, (<b>c</b>) root/shoot ratio, (<b>d</b>) stem length, (<b>e</b>) root length, (<b>f</b>) branch number, and (<b>g</b>) chlorophyll content. A positive RI indicates an increased biomass of <span class="html-italic">A. adenophora</span> in the nutrient stress (−N) treatment with <span class="html-italic">Colletotrichum</span> strain (AX39, AX115, or AX198) inoculation compared with that without <span class="html-italic">Colletotrichum</span> inoculation. The formula is as follows: (treatment_Ncontrol_N)/control_N. Nonparametric Mann–Whitney U tests or independent sample T tests were used to identify the differences between each nutrient stress treatment and the normal treatment (* &lt;0.05,*** &lt;0.001). Post hoc comparisons were performed via Duncan’s test for equal variance and Dunnett’s test for unequal variance (T3 test) to show that the differences in the RIs were significant among the treatments inoculated with strains AX39, AX115, and AX198, with different letters indicating significant differences. The error bars are the standard errors.</p>
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20 pages, 2139 KiB  
Article
Beneficial Microorganisms: Sulfur-Oxidizing Bacteria Modulate Salt and Drought Stress Responses in the Halophyte Plantago coronopus L.
by Aleksandra Koźmińska, Mohamad Al Hassan, Wiktor Halecki, Cezary Kruszyna and Ewa Hanus-Fajerska
Sustainability 2024, 16(24), 10866; https://doi.org/10.3390/su162410866 - 11 Dec 2024
Viewed by 544
Abstract
Land degradation due to salinity and prolonged drought poses significant global challenges by reducing crop yields, depleting resources, and disrupting ecosystems. Halophytes, equipped with adaptive traits for drought and soil salinity, and their associations with halotolerant microbes, offer promising solutions for restoring degraded [...] Read more.
Land degradation due to salinity and prolonged drought poses significant global challenges by reducing crop yields, depleting resources, and disrupting ecosystems. Halophytes, equipped with adaptive traits for drought and soil salinity, and their associations with halotolerant microbes, offer promising solutions for restoring degraded areas sustainably. This study evaluated the effects of halophilic sulfur-oxidizing bacteria (SOB), specifically Halothiobacillus halophilus, on the physiological and biochemical responses of the halophyte Plantago coronopus L. under drought and salt stress. We analyzed the accumulation of ions (Na, Cl, K) and sulfur (S), along with growth parameters, glutathione levels, photosynthetic pigments, proline, and phenolic compounds. Drought significantly reduced water content (nearly 10-fold in plants without SOB and 4-fold in those with SOB). The leaf growth tolerance index improved by 70% in control plants and 30% in moderately salt-stressed plants (300 mM NaCl) after SOB application. SOB increased sulfur content in all treatments except at high salinity (600 mM NaCl), reduced toxic sodium and chloride ion accumulation, and enhanced potassium levels under drought and moderate salinity. Proline, total phenolic, and malondialdehyde (MDA) levels were highest in drought-stressed plants, regardless of SOB inoculation. SOB inoculation increased GSH levels in both control and 300 mM NaCl-treated plants, while GSSG levels remained constant. These findings highlight the potential of SOB as beneficial microorganisms to enhance sulfur availability and improve P. coronopus tolerance to moderate salt stress. Full article
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<p>Elemental contents: sodium (<b>A</b>); potassium (<b>B</b>); chloride (<b>C</b>); K/Na ratio (<b>D</b>); sulfur (<b>E</b>); in <span class="html-italic">P. coronopus</span> subjected to drought and sodium chloride (300 and 600 mM NaCl) and sulfur-oxidizing bacteria. SOB—<span class="html-italic">Halothiobacillus halophilus</span>-inoculated substrate, non-SOB-non-inoculated substrate. Different lowercase letters indicate significant differences between plants cultivated on non-inoculated substrate by SOB within different stress treatments. Different capital letters indicate statistically significant differences between plants cultivated on substrate inoculated by SOB within different stress treatments. * Indicates statistically significant differences between inoculated and non-inoculated plants within the same stress treatment, according to Tukey’s test (α = 0.05), ±SE, n = 5.</p>
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<p>PCA for K/Na, S, K, Cl, and Na; KMO = 0.62; <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Photosynthetic pigments: chlorophyll a (chl.a), chlorophyll b (chl.b), and carotenoids (car.) in <span class="html-italic">P. coronopus</span> subjected to drought, sodium chloride (300 and 600 mM NaCl) and sulfur-oxidizing bacteria.</p>
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<p>PCA for GSH, GSSG, and GSH/GSSG. KMO = 0.34; <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>(<b>A</b>). Box plot of proline (Pro), across experimental treatments. (<b>B</b>). Box plot of TPC across experimental treatments. (<b>C</b>). Box plot of MDA across experimental treatments. (<b>D</b>). Box plot of DPPH across experimental treatments.</p>
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<p>(<b>A</b>). Box plot of proline (Pro), across experimental treatments. (<b>B</b>). Box plot of TPC across experimental treatments. (<b>C</b>). Box plot of MDA across experimental treatments. (<b>D</b>). Box plot of DPPH across experimental treatments.</p>
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17 pages, 2826 KiB  
Article
Effects of Drought Stress at the Booting Stage on Leaf Physiological Characteristics and Yield of Rice
by Xiaolong Yang, Xiuxiu Wang, Yang Li, Lantian Yang, Long Hu, Yuling Han and Benfu Wang
Plants 2024, 13(24), 3464; https://doi.org/10.3390/plants13243464 - 11 Dec 2024
Viewed by 363
Abstract
Drought stress is a major environmental constraint that limits rice (Oryza sativa L.) production worldwide. In this study, we investigated the effects of drought stress at the booting stage on rice leaf physiological characteristics and yield. The results showed that drought stress [...] Read more.
Drought stress is a major environmental constraint that limits rice (Oryza sativa L.) production worldwide. In this study, we investigated the effects of drought stress at the booting stage on rice leaf physiological characteristics and yield. The results showed that drought stress would lead to a significant decrease in chlorophyll content and photosynthesis in rice leaves, which would affect rice yield. Three different rice varieties were used in this study, namely Hanyou73 (HY73), Huanghuazhan (HHZ), and IRAT109. Under drought stress, the chlorophyll content of all cultivars decreased significantly: 11.1% and 32.2% decreases in chlorophyll a and chlorophyll b in HHZ cultivars, 14.1% and 28.5% decreases in IRAT109 cultivars, and 22.9% and 18.6% decreases in HY73 cultivars, respectively. In addition, drought stress also led to a significant decrease in leaf water potential, a significant increase in antioxidant enzyme activity, and an increase in malondialdehyde (MDA) content, suggesting that rice activated a defense mechanism to cope with drought-induced oxidative stress. This study also found that drought stress significantly reduced the net photosynthetic rate and stomatal conductance of rice, which, in turn, affected the yield of rice. Under drought stress, the yield of the HHZ cultivars decreased most significantly, reaching 30.2%, while the yields of IRAT109 and HY73 cultivars decreased by 13.0% and 18.2%, respectively. The analysis of yield composition showed that the number of grains per panicle, seed-setting rate, and 1000-grain weight were the key factors affecting yield formation. A correlation analysis showed that there was a significant positive correlation between yield and net photosynthetic rate, stomatal conductance, chla/chlb ratio, Rubisco activity, and Fv/Fm, but there was a negative correlation with MDA and non-photochemical quenching (NPQ). In summary, the effects of drought stress on rice yield are multifaceted, involving changes in multiple agronomic traits. The results highlight the importance of selecting and nurturing rice varieties with a high drought tolerance, which should have efficient antioxidant systems and high photosynthetic efficiency. Future research should focus on the genetic mechanisms of these physiological responses in order to develop molecular markers to assist in the breeding of drought-tolerant rice varieties. Full article
(This article belongs to the Special Issue Cell Physiology and Stress Adaptation of Crops)
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<p>Dynamic change in leaf SPAD values after drought stress treatment (day 1 to day 18). The bars represent the standard error (SE), n = 3.</p>
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<p>Effects of drought stress on leaf water potential (LWP) on day 9 after drought stress treatment. Different letters indicate significant differences between the treatments using Tukey’s test (<span class="html-italic">p</span> &lt; 0.05). Data represent means of n = 3 measurements ± standard deviation.</p>
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<p>Effects of drought stress on Rubisco, FBPase, and SPSase activities on day 9 after drought stress treatment: (<b>A</b>) ribulose bisphosphate carboxylase; (<b>B</b>) fructose-1,6-diphosphatase; and (<b>C</b>) sucrose phosphate synthase. Different letters indicate significant differences between the treatments using Tukey’s test (<span class="html-italic">p</span> &lt; 0.05). Data represent means of n = 3 measurements ± standard deviation.</p>
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<p>Effects of drought stress on Fv/Fm, q<sup>p</sup>, NPQ, and distribution of light energy PR, EX, and AD on day 9 after drought stress treatment. Different letters indicate significant differences between the treatments using Tukey’s test (<span class="html-italic">p</span> &lt; 0.05). (<b>A</b>) Maximum photochemical values of PSII in the dark; (<b>B</b>) photochemical quenching; (<b>C</b>) non-photochemical quenching; (<b>D</b>) photochemical reaction; (<b>E</b>) non-photochemical reaction dissipation; and (<b>F</b>) antenna heat dissipation. Data represent the means of n = 3 measurements ± standard deviation.</p>
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<p>Effects of drought stress on net photosynthetic rate and stomatal conductance (G<sub>s</sub>) on day 9 after drought stress treatment: (<b>A</b>) net photosynthetic rate and (<b>B</b>) stomatal conductance. Different letters indicate significant differences between the treatments using Tukey’s test (<span class="html-italic">p</span> &lt; 0.05). Data represent the means of n = 3 measurements ± standard deviation.</p>
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<p>Effects of drought stress on photosynthetic light response curve on day 9 after drought stress treatment.</p>
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<p>Principal component analysis of all three cultivars under drought stress at the booting stage. HHZ-CK: flooding irrigation of Huanghuazhan; IRAT109-CK: flooding irrigation of IRAT109; HY73-CK: flooding irrigation of Hanyou73; HHZ-DS: drought stress of Huanghuazhan; IRAT109-DS: drought stress of IRAT109; and HY73-DS: drought stress of Hanyou73.</p>
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<p>Correlation analysis among 11 physiological indexes of all three cultivars under drought stress at the booting stage. Pn: net photosynthetic rate; Gs: stomatal conductance; LWP: leaf water potential; Chla/Chlb: ratio of chlorophyll a to chlorophyll b; SPAD: soil–plant analysis development value; MDA: malondialdehyde; Rubisco: ribulose bisphosphate carboxylase; Fv/Fm: maximum photochemical value of PSII in the dark; qP: photochemical quenching; and NPQ: non-photochemical quenching. * <span class="html-italic">p</span> &lt; 0.05.</p>
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16 pages, 10863 KiB  
Article
Genome-Wide Identification of Epidermal Pattern Factor (EPF) Gene Family in Potato and Functional Characterization of StEPF4 in Regulating Drought Stress
by Run Qiao, Jiangwei Yang, Yurong Deng, Xiaoqin Duan, Xinxia Li, Fengjiao Zhu, Mei Liu, Jiani Mou, Ning Zhang and Huaijun Si
Agronomy 2024, 14(12), 2948; https://doi.org/10.3390/agronomy14122948 - 11 Dec 2024
Viewed by 410
Abstract
Plants require adequate water for growth, development, and reproduction. Peptides play a key role in plant growth and development and act in a similar manner to plant hormones. However, only a few peptides have been identified to play a role in abiotic stress [...] Read more.
Plants require adequate water for growth, development, and reproduction. Peptides play a key role in plant growth and development and act in a similar manner to plant hormones. However, only a few peptides have been identified to play a role in abiotic stress tolerance in potato. In this study, we identified fourteen members of the epidermal patterning factor (EPF) family in potato, which were designated as StEPF1-14 according to their chromosomal locations. We also conducted a comprehensive analysis of their chromosomal distribution, gene structures, physicochemical properties, phylogenetic relationships, and tissue-specific expression patterns. RT-qPCR analysis revealed that the StEPF4 gene is significantly induced by drought stress, suggesting its potential role as a negative regulator in the plant’s response to drought. Furthermore, multiple cis-regulatory elements associated with drought-responsive regulation were identified within the promoter region of the StEPF genes. Here, we isolated an EPF secreted Cys-rich small peptide StEPF4 from ‘Atlantic’ and explored its mechanism in plant response to drought stress. We found that StEPF4 was greatly induced by dehydration treatment in potato. To investigate its potential biological functions, StEPF4 was knocked down in potato. The StEPF4 knocked down lines (KdStEPF4) significantly decreased stomatal density, resulting in a decrease in the transpiration rate. KdStEPF4 lines maintained a higher photosynthetic rate and lowered the water loss rate of leaves compared with the control, resulting in increased drought resistance. Taken together, this study provides detailed information about StEPFs, and our findings also show that StEPF4 plays an essential role in regulating drought resistance by reducing stomatal density in potato. Full article
(This article belongs to the Special Issue Resistance-Related Gene Mining and Genetic Improvement in Crops)
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<p>Chromosome localization analysis of <span class="html-italic">StEPFs</span>. The different colors represent the density information of the genes, red represents higher gene density, and blue is the opposite.</p>
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<p>Analysis of the gene structure of <span class="html-italic">StEPFs</span>. The blue and yellow boxes represented UTR and CDS, and the black lines represented introns.</p>
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<p>Multiplex sequence comparison analysis of potato StEPF protein.</p>
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<p>Phylogenetic tree of EPF proteins from <span class="html-italic">Solanum tuberosum</span> L., <span class="html-italic">Solanum lycopersicum</span>, <span class="html-italic">Arabidopsis thaliana</span>, and <span class="html-italic">Oryza sativa</span>. Each color represents one group. The red stars represent the highlighting of the <span class="html-italic">StEPFs</span>.</p>
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<p>Ten conserved motifs of the potato StEPF family. One color box corresponding to one motif.</p>
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<p>The cis-elements in the promoters of potato EPF genes. PlantCare online website was used to analyze and present the results with TBtools. The 2000 bp upstream of the <span class="html-italic">StEPFs</span> was used to perform cis-elements analysis.</p>
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<p>Relative expression of StEPF genes under drought. CK represent no treatment, D12 represent the droughted treatment on the twelfth day. Values are presented as the mean ± SE. All asterisks denote significant differences: **, <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Expression levels of <span class="html-italic">StEPFs</span> in different tissues of potato.</p>
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<p>Expression analysis of <span class="html-italic">StEPF4</span> in wild-type (WT) and transgenic strains. <span class="html-italic">StEf1α</span> (elongation factor 1α) was used as an internal control. (<b>A</b>) Comparison of <span class="html-italic">StEPF4</span> expression levels between overexpression lines and WT controls. (<b>B</b>) Comparison of <span class="html-italic">StEPF4</span> expression levels between knocked down lines and WT controls. Values are presented as the mean ± SE. All asterisks denote significant differences: *, <span class="html-italic">p</span> &lt; 0.05; **, <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Knock down of StEPF4 confers drought tolerance under short-term drought conditions. (<b>A</b>) Phenotypes differences under short-term drought stress. (<b>B</b>) Quantitative measurement of leaf RWC. (<b>C</b>) MDA content analysis. (<b>D</b>) Pro content analysis. (<b>E</b>) SOD activity. (<b>F</b>) POD activity. (<b>G</b>) CAT activity. Values are presented as the mean ± SE. <span class="html-italic">n</span> = 3; Different lowercase letters indicate significant difference.</p>
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<p><span class="html-italic">StEPF4</span>-modulated stomatal density of potato leaves. (<b>A</b>) Scanning electron micrograph of the leaf of WT and StEPF4 transgenic lines. (<b>B</b>) Stomatal density. (<b>C</b>) Leaf water loss rate. Values are presented as the mean ± SE. <span class="html-italic">n</span> = 3; Different lowercase letters indicate significant difference.</p>
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16 pages, 3345 KiB  
Article
Genome-Wide Identification of the Trihelix Transcription Factor Family and Functional Analysis of ZmTHX15 in Maize
by Yanyong Cao, Zeqiang Cheng, Xinyan Sun, Meichen Zhu, Ling Yue, Hui Liu, Xiaolin Wu, Jinghua Zhang and Canxing Duan
Int. J. Mol. Sci. 2024, 25(24), 13257; https://doi.org/10.3390/ijms252413257 - 10 Dec 2024
Viewed by 431
Abstract
The trihelix transcription factor, which is a plant-specific family, play a critical role in plant growth and development and stress responses. Drought is the main limiting factor affecting yield of maize (Zea mays). However, the identification and characterization of this gene [...] Read more.
The trihelix transcription factor, which is a plant-specific family, play a critical role in plant growth and development and stress responses. Drought is the main limiting factor affecting yield of maize (Zea mays). However, the identification and characterization of this gene family in maize and its biological functions in response to drought stress have not been reported. Here, 46 Zea mays trihelix genes (ZmTHXs) were identified in the genome. Phylogenetic analysis of the ZmTHXs revealed that the genes were clustered into five subfamilies: GT-1, GT-2, GTγ, SH4, and SIP1. Chromosomal localization analysis showed that the 46 ZmTHXs were unevenly distributed across 10 chromosomes in maize. Cis-acting elements related to abiotic stress in ZmTHXs were found. Most ZmTHXs genes showed significant changes in expression levels under drought treatment. In addition, ZmTHX15-overexpressing Arabidopsis exhibited stronger drought tolerance with less secondary oxidative damage and higher photosynthetic rate. These findings could serve as a basis for future studies on the roles of ZmTHXs and the potential genetic markers for breeding stress-resistant and high-yielding maize varieties. Full article
(This article belongs to the Special Issue Molecular Research in Plant Adaptation to Abiotic Stress)
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<p>Analysis of the evolutionary relationship of THXs in maize, rice, and <span class="html-italic">Arabidopsis</span>.</p>
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<p>The location of <span class="html-italic">ZmTHXs</span> on chromosomes. (<b>A</b>) Distribution of <span class="html-italic">ZmTHXs</span> on chromosomes; (<b>B</b>) The number of <span class="html-italic">ZmTHXs</span> on each chromosome.</p>
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<p><span class="html-italic">ZmTHXs</span> collinearity analysis. Heat maps and histograms along the rectangles represent gene densities on chromosomes. Gray lines indicate syntenic blocks in the poplar genome, and red lines between chromosomes indicate gene pairs with segmental duplications.</p>
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<p>Analysis of cis-acting regulatory elements in <span class="html-italic">ZmTHXs</span>. The key cis-acting regulatory elements are distributed in the 2000 bp region upstream of the <span class="html-italic">ZmTHXs</span>, and different elements are shown in different colors.</p>
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<p>Heat map of differential expression of <span class="html-italic">ZmTHXs</span>. Drought treatment time is 6 h. Different-colored rectangles on the right side of the evolutionary tree represent genes of different subfamilies.</p>
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<p>Phenotype of transgenic Arabidopsis. (<b>A</b>) The phenotypes of WT and <span class="html-italic">ZmTHX15</span>-overexpressing transgenic Arabidopsis line 1 and line 2 (OE-1, OE-2) treated with/without 100 mmol/L mannitol after 6 days of germination; (<b>B</b>) The root-length statistics of WT, OE-1, and OE-2 treated with/without 100 mmol/L mannitol after 6 days of germination. Data represent the mean ± SD of three biological repeats, and asterisks indicate significant differences among groups (<span class="html-italic">p</span> &lt; 0.05). Bar, 1 cm.</p>
Full article ">Figure 7
<p><span class="html-italic">ZmTHX15</span>-overexpressing <span class="html-italic">Arabidopsis</span> is more drought tolerant than WT. (<b>A</b>) Phenotype of WT, OE-1, and OE-2 plants transplanted for 10 days under normal watering; (<b>B</b>) Phenotype of WT, OE-1, and OE-2 plants after 10 days of drought treatment; (<b>C</b>) Plant survival rate after drought treatment; (<b>D</b>) Ascorbate peroxidase activity; (<b>E</b>) glutathione reductase activity; (<b>F</b>) ascorbic acid content; (<b>G</b>) malondialdehyde content; (<b>H</b>) chlorophyll fluorescence index; (<b>I</b>) chlorophyll content; (<b>J</b>) intercellular CO<sub>2</sub> concentration; (<b>K</b>) net photosynthetic rate. The data represent the mean ± SD of three biological repeats, and the letters represent significant differences among groups (<span class="html-italic">p</span> &lt; 0.05). Bar, 1 cm.</p>
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