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Search Results (544)

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Keywords = Hordeum vulgare

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23 pages, 3420 KiB  
Article
Salicylic Acid Seed Priming: A Key Frontier in Conferring Salt Stress Tolerance in Barley Seed Germination and Seedling Growth
by Rim Ben Youssef, Nahida Jelali, Jose Ramón Acosta Motos, Chedly Abdelly and Alfonso Albacete
Agronomy 2025, 15(1), 154; https://doi.org/10.3390/agronomy15010154 - 10 Jan 2025
Viewed by 320
Abstract
The goal of the current study was to investigate the effects of seed priming with salicylic acid (SA) on seed germination parameters, seedling growth traits, nutritional element mobilization, and oxidative stress status in two barley species that were subjected to various salt treatments. [...] Read more.
The goal of the current study was to investigate the effects of seed priming with salicylic acid (SA) on seed germination parameters, seedling growth traits, nutritional element mobilization, and oxidative stress status in two barley species that were subjected to various salt treatments. The findings demonstrated that salinity reduced a number of germination parameters in unprimed seeds and impacted seedling growth by impeding both species’ necessary nutrient mobilization. Under this abiotic stress, a noticeable rise in malondialdehyde and electrolyte leakage was also noted. Interestingly, pretreating seeds with SA improved seed germination and seedling growth performance under either 100 mM or 200 mM NaCl treatments. In fact, SA improved the length and dry weight of stressed seedlings of both barley species in addition to increasing the germination rate and mean daily germination. Additionally, SA increased the content of calcium, iron, magnesium, and potassium while lowering the concentrations of sodium and malondialdehyde and electrolyte leakage. It is significant to note that, in comparison to Hordeum maritimum, the positive effects of this hormone were more noticeable in stressed Hordeum vulgare species. Full article
(This article belongs to the Section Plant-Crop Biology and Biochemistry)
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Figure 1

Figure 1
<p>Effect of SA seed priming and no pretreatment (Control: C) on the final germination rate (%) of two barley species, <span class="html-italic">Hordeum maritimum</span> (<b>A</b>) and <span class="html-italic">Hordeum vulgare</span> (<b>B</b>), grown under salinity stress. Values are means of five replicates ± standard error. Data with the same letter are not significantly different at <span class="html-italic">p</span> &lt; 0.05 (Duncan’s test).</p>
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<p>Effect of SA seed priming and no pretreatment (Control: C) on the Mean Daily Germination of two barley species, <span class="html-italic">Hordeum maritimum</span> (<b>A</b>) and <span class="html-italic">Hordeum vulgare</span> (<b>B</b>), grown under salinity stress. Values are means of three replicates ± standard error. Data with the same letter are not significantly different at <span class="html-italic">p</span> &lt; 0.05 (Duncan’s test).</p>
Full article ">Figure 2 Cont.
<p>Effect of SA seed priming and no pretreatment (Control: C) on the Mean Daily Germination of two barley species, <span class="html-italic">Hordeum maritimum</span> (<b>A</b>) and <span class="html-italic">Hordeum vulgare</span> (<b>B</b>), grown under salinity stress. Values are means of three replicates ± standard error. Data with the same letter are not significantly different at <span class="html-italic">p</span> &lt; 0.05 (Duncan’s test).</p>
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<p>Effect of SA seed priming and no pretreatment (Control: C) on the length of radicles (<b>A</b>,<b>B</b>) and coleoptiles (<b>C</b>,<b>D</b>) of two barley species, <span class="html-italic">Hordeum maritimum</span> and <span class="html-italic">Hordeum vulgare</span>, grown under salinity stress. Values are means of five replicates from three independent experiments ± standard error. Data with the same letter are not significantly different at <span class="html-italic">p</span> &lt; 0.05 (Duncan’s test).</p>
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<p>Effect of SA seed priming and no pretreatment (Control: C) on dry weight in radicles (<b>A</b>,<b>B</b>) and coleoptiles (<b>C</b>,<b>D</b>) of two barley species, <span class="html-italic">Hordeum maritimum</span> and <span class="html-italic">Hordeum vulgare</span>, grown under salinity stress. Values are means of five independent replicates ± standard error. Data with the same letter are not significantly different at <span class="html-italic">p</span> &lt; 0.05 (Duncan’s test).</p>
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<p>Effect of SA seed priming and no pretreatment (Control: C) on the Membrane Stability Index in radicles (<b>A</b>,<b>B</b>) and coleoptiles (<b>C</b>,<b>D</b>) of two barley species, <span class="html-italic">Hordeum maritimum</span> and <span class="html-italic">Hordeum vulgare</span>, grown under salinity stress. Values are means of five independent replicates ± standard error. Data with the same letter are not significantly different at <span class="html-italic">p</span> &lt; 0.05 (Duncan’s test).</p>
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<p>Effect of SA seed priming and no pretreatment (Control: C) on Malondialdehyde content in radicles (<b>A</b>,<b>B</b>) and coleoptiles (<b>C</b>,<b>D</b>) of two barley species, <span class="html-italic">Hordeum maritimum</span> and <span class="html-italic">Hordeum vulgare</span>, grown under salinity stress. Values are means of five independent replicates ± standard error. Data with the same letter are not significantly different at <span class="html-italic">p</span> &lt; 0.05 (Duncan’s test).</p>
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<p>A principal component analysis of coleoptiles for the different treatments (HV_0 mM NaCl, HV_100 mM NaCl, HV_200 mM NaCl, HV_SA_100 mM NaCl, HV_SA_200 mM NaCl, HM_0 mM NaCl, HM_100 mM NaCl, HM_200 mM NaCl, HM_SA_100 mM NaCl, and HM_SA 200 mM NaCl). Two principal components (PC1 and PC2) resulted in a model that explained 84.47% of the total variance.</p>
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<p>A principal component analysis of radicles for the different treatments (HV_0 mM NaCl, HV_100 mM NaCl, HV_200 mM NaCl, HV_SA_100 mM NaCl, HV_SA_200 mM NaCl, HM_0 mM NaCl, HM_100 mM NaCl, HM_200 mM NaCl, HM_SA_100 mM NaCl, and HM_SA 200 mM NaCl). Two principal components (PC1 and PC2) resulted in a model that explained 84.47% of the total variance.</p>
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23 pages, 3489 KiB  
Article
Optimizing Nitrogen Use Efficiency and Yield in Winter Barley: A Three-Year Study of Fertilization Systems in Southern Germany
by Martin Mittermayer, Franz-Xaver Maidl, Joseph Donauer, Stefan Kimmelmann, Johannes Liebl and Kurt-Jürgen Hülsbergen
Appl. Sci. 2025, 15(1), 391; https://doi.org/10.3390/app15010391 - 3 Jan 2025
Viewed by 445
Abstract
Various fertilization systems have been developed to optimize nitrogen (N) application, yet their effectiveness remains a topic of debate in both science and practice. This study evaluates the effects of 28 N fertilization treatments on yield, quality, nitrogen use efficiency (NUE), N surplus, [...] Read more.
Various fertilization systems have been developed to optimize nitrogen (N) application, yet their effectiveness remains a topic of debate in both science and practice. This study evaluates the effects of 28 N fertilization treatments on yield, quality, nitrogen use efficiency (NUE), N surplus, and economic optima in two winter barley (Hordeum vulgare L.) varieties—a multi-row and a two-row type—across a three-year field trial (2021–2023). Specifically, it compares the performance of fertilizer requirement calculations based on the German Fertilizer Application Ordinance (GFO), multispectral sensor-based fertilization systems, and fixed N input treatments. Under the trial conditions (highly productive fields without organic fertilization for decades), the GFO system consistently achieved high yields (>10 t ha−1) and NUE (up to 88%) for both barley varieties, often near economically optimal N rates and with minimal N surpluses. Sensor-based systems demonstrated promising potential for yield optimization and reducing N input; however, they did not result in significantly higher yields. Further research is needed to assess the performance of these fertilization systems under different conditions, such as sandy soils in regions with early-summer droughts or in systems involving organic fertilization. Full article
(This article belongs to the Special Issue Crop Yield and Nutrient Use Efficiency)
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<p>Relationships between N fertilization and yield and between N fertilization and N surplus for the year 2021 (Meridian). Yield is expressed as fresh matter, standardized to a dry matter content of 86%. The upper lines depict the relationship between N fertilization and yield, whereas the lower lines represent the relationship between N fertilization and N surplus.</p>
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<p>Relationships between N fertilization and yield and between N fertilization and N surplus for the year 2021 (Sandra). Yield is expressed as fresh matter, standardized to a dry matter content of 86%. The upper lines depict the relationship between N fertilization and yield, whereas the lower lines represent the relationship between N fertilization and N surplus.</p>
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<p>Relationships between N fertilization and yield and between N fertilization and N surplus for the year 2022 (Meridian). Yield is expressed as fresh matter, standardized to a dry matter content of 86%. The upper lines depict the relationship between N fertilization and yield, whereas the lower lines represent the relationship between N fertilization and N surplus.</p>
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<p>Relationships between N fertilization and yield and between N fertilization and N surplus for the year 2023 (Meridian). Yield is expressed as fresh matter, standardized to a dry matter content of 86%. The upper lines depict the relationship between N fertilization and yield, whereas the lower lines represent the relationship between N fertilization and N surplus.</p>
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<p>Design of the plot trial. The trials were consistent in each study year (2021–2023).</p>
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<p>Relationships between N fertilization, yield, and nitrogen surplus for the year 2022 (Sandra). Yield is given in fresh matter with a dry matter content of 86%. The lines above describe the relationship between N fertilization and yield, while the lines below illustrate the relationship between N fertilization and N surplus.</p>
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<p>Relationships between N fertilization, yield, and nitrogen surplus for the year 2023 (Sandra) Yield is given in fresh matter with a dry matter content of 86%. The lines above describe the relationship between N fertilization and yield, while the lines below illustrate the relationship between N fertilization and N surplus.</p>
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13 pages, 2920 KiB  
Article
How the Ectopic Expression of the Barley F-Box Gene HvFBX158 Enhances Drought Resistance in Arabidopsis thaliana
by Shuting Wen, Yicheng Chen, Xingzhe Yang, Guo Zhang, Lulu Jin, Xiaoqin Zhang, Yunxia Fang and Dawei Xue
Int. J. Mol. Sci. 2025, 26(1), 342; https://doi.org/10.3390/ijms26010342 - 2 Jan 2025
Viewed by 377
Abstract
In this study, the drought-responsive gene HvFBX158 from barley was transferred to Arabidopsis thaliana, and overexpression lines were obtained. The phenotypic characteristics of the transgenic plants, along with physiological indicators and transcription level changes of stress-related genes, were determined under drought treatment. [...] Read more.
In this study, the drought-responsive gene HvFBX158 from barley was transferred to Arabidopsis thaliana, and overexpression lines were obtained. The phenotypic characteristics of the transgenic plants, along with physiological indicators and transcription level changes of stress-related genes, were determined under drought treatment. Under drought stress, transgenic plants overexpressing HvFBX158 exhibited enhanced drought tolerance and longer root lengths compared to wild-type plants. Additionally, malondialdehyde and hydrogen peroxide contents were significantly lower in transgenic lines, while superoxide dismutase activity was elevated. Quantitative RT-PCR showed that the expression levels of drought and stress response genes, including AtP5CS, AtDREB2A, AtGSH1, AtHSP17.8, and AtSOD, were significantly upregulated. Transcriptome analysis further confirmed that HvFBX158 regulated multiple stress tolerance pathways. In summary, the overexpression of the HvFBX158 gene enhanced drought tolerance in Arabidopsis thaliana by regulating multiple stress response pathways. This study provides a practical basis for improving drought-resistant barley varieties and lays a foundation for subsequent research on F-box family genes for stress resistance in barley. Full article
(This article belongs to the Special Issue Molecular Mechanisms of Plant Abiotic Stress Tolerance: 2nd Edition)
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<p>Identification of transgenic <span class="html-italic">Arabidopsis</span> lines. (<b>A</b>) PCR identification of T<sub>3</sub> transgenic <span class="html-italic">Arabidopsis thaliana</span>, m: 2000 bp; 1: plasmid; 2: wild-type <span class="html-italic">Arabidopsis thaliana</span>; 3–7: transgenic <span class="html-italic">Arabidopsis</span>. (<b>B</b>) qRT-PCR detection of transgenic <span class="html-italic">Arabidopsis</span> was determined using the Student’s <span class="html-italic">t</span>-test, and the error bar represents the standard error (<span class="html-italic">n</span> ≥ 3, * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01). WT and OE are the abbreviations of wild type and overexpression line, respectively.</p>
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<p>The drought resistance of <span class="html-italic">HvFBX158</span> transgenic <span class="html-italic">Arabidopsis</span> under drought stress. (<b>A</b>) Roots of the wild-type and transgenic <span class="html-italic">Arabidopsis</span> seedlings under normal (0% PEG 6000) and drought stress (6% PEG 6000) conditions (<span class="html-italic">n</span> ≥ 12); Scale bar = 3 cm. (<b>B</b>) Comparison of root length data of wild-type and transgenic <span class="html-italic">Arabidopsis</span> seedlings under drought stress. Data were analyzed by the Student’s <span class="html-italic">t</span>-test, and the error bar represents standard errors (<span class="html-italic">n</span> ≥ 3, ** <span class="html-italic">p</span> &lt; 0.01). WT and OE are the abbreviations of wild type and overexpression line, respectively.</p>
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<p>Phenotypes of wild-type and OE lines at the rosette stage under drought stress. WT and OE are the abbreviations of wild type and overexpression line, respectively. Control: wild-type and OE lines cultivated under normal conditions for 30 days; Drought: wild-type and OE lines after 21 days of drought treatment; Recovery: wild-type and OE lines after 3 days of rehydration after drought treatment. Scale bar = 2 cm. (Culture conditions of control group is 20–22 °C, the light cycle is 16/8 h, and the relative humidity is 60–70%; the drought treatment group was not watered.)</p>
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<p>Physiological index data analysis of WT and OE lines under normal and drought treatment. (<b>A</b>) MDA content. (<b>B</b>) SOD enzyme activity. (<b>C</b>) H<sub>2</sub>O<sub>2</sub> content. The data were analyzed by the Student’s <span class="html-italic">t</span>-test, and the error bar indicates the standard error (n ≥ 3, ** <span class="html-italic">p</span> &lt; 0.01). WT and OE are the abbreviations of wild type and overexpression line, respectively. MDA, SOD, and H<sub>2</sub>O<sub>2</sub> are abbreviations of malondialdehyde, superoxide dismutase, and hydrogen peroxide, respectively.</p>
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<p>Expression analysis of stress-related genes under drought conditions. Relative expressions of <span class="html-italic">AtP5CS</span> (<b>A</b>), <span class="html-italic">AtDREB2A</span> (<b>B</b>), <span class="html-italic">AtHSP17.8</span> (<b>C</b>), <span class="html-italic">AtGSH1</span> (<b>D</b>), and <span class="html-italic">AtSOD</span> (<b>E</b>). The Student’s <span class="html-italic">t</span>-test was employed to ascertain significant differences, and the error bar indicated the standard error (<span class="html-italic">n</span> ≥ 3, * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01). WT and OE are the abbreviations of wild type and overexpression line, respectively.</p>
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<p>GO enrichment map of differentially expressed genes. (<b>A</b>) Cluster analysis of differentially expressed genes of WT and OE2 involved in GO entries in the dehydration response. (<b>B</b>) Bubble map of GO functional enrichment analysis of differentially expressed genes in WT vs. OE2. WT and OE are the abbreviations of wild type and overexpression line, respectively. BP, CC, and MF are abbreviations of biological processes, cellular components, and molecular functions, respectively. GO is the abbreviation of gene ontology.</p>
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20 pages, 12150 KiB  
Article
A Study on the Infrageneric Classification of Hordeum Using Multiple Methods: Based on Morphological Data
by Nayoung Ro, Pilmo Sung, Mesfin Haile, Hyemyeong Yoon, Dong-Su Yu, Ho-Cheol Ko, Gyu-Taek Cho, Hee-Jong Woo and Nam-Jin Chung
Agronomy 2025, 15(1), 60; https://doi.org/10.3390/agronomy15010060 - 29 Dec 2024
Viewed by 312
Abstract
The genus Hordeum (barley) represents an essential group within the Poaceae family, comprising diverse species with significant ecological and economic importance. This study aims to improve the infrageneric classification of Hordeum by integrating multiple analytical approaches based on morphological data. A comprehensive dataset [...] Read more.
The genus Hordeum (barley) represents an essential group within the Poaceae family, comprising diverse species with significant ecological and economic importance. This study aims to improve the infrageneric classification of Hordeum by integrating multiple analytical approaches based on morphological data. A comprehensive dataset of key morphological traits was compiled from a wide range of Hordeum accessions, including representatives from all major taxonomic groups within the genus. Understanding and classifying the evolutionary traits of barley species, particularly in terms of environmental adaptation, pest resistance, and productivity improvement, is essential. DNA-based classification methods allow precise molecular-level analysis but are resource-intensive, especially when large-scale processing is required. This study addresses these limitations by employing an integrative approach combining hierarchical clustering, Principal Component Analysis–Linear Discriminant Analysis (PCA-LDA), and Random Forest (RF) to analyze the compiled morphological datasets. Morphological clustering via hierarchical analysis revealed clear taxonomic distinctions, achieving 86.0% accuracy at the subgenus level and 83.1% at the section level. PCA-LDA further refined classification by identifying key traits such as seed width, area, and 100-seed weight as primary contributors, achieving perfect accuracy for the Hordeum section and high accuracy for species like Hordeum vulgare and Hordeum spontaneum. RF analysis enhanced classification performance, achieving 100% accuracy at the section level and high accuracy for species with sufficient data. This approach offers a new framework for classifying diverse barley species and contributes significantly to data-driven decision-making in breeding and conservation efforts, supporting a deeper understanding of barley’s adaptive evolution in response to environmental changes. Full article
(This article belongs to the Special Issue Genetics and Breeding of Field Crops in the 21st Century)
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<p>Comparison of 17 agronomic traits among different sections: <span class="html-italic">Hordeum</span>, <span class="html-italic">Stenostachys</span>, <span class="html-italic">Trichostachys</span>, <span class="html-italic">Marina</span>, and <span class="html-italic">Nodosa</span>. Box plots represent the distribution of 17 agronomic traits for each section. Statistical significance was determined using Wilcoxon rank sum tests for pairwise comparisons between sections. Significance levels are indicated by asterisks: **** <span class="html-italic">p</span> &lt; 0.0001, *** <span class="html-italic">p</span> &lt; 0.001, ** <span class="html-italic">p</span> &lt; 0.01, * <span class="html-italic">p</span> &lt; 0.05, and “ns” denotes no significant difference. The results reveal significant differences in days to flowering across several sections, with <span class="html-italic">Hordeum</span> and <span class="html-italic">Stenostachys</span> showing particularly notable differences from other sections. (<b>A</b>) Days to flowering, (<b>B</b>) Days to maturity, (<b>C</b>) Culm length (cm), (<b>D</b>) Number of triple-spikelets, (<b>E</b>) Central-spikelet awn length (cm), (<b>F</b>) Central-spikelet glume length (cm), (<b>G</b>) Lateral-spikelet awn length (cm), (<b>H</b>) Lateral-spikelet glume length (cm), (<b>I</b>) Spike length (cm), (<b>J</b>) Flag-leaves area (cm<sup>2</sup>), (<b>K</b>) Flag-leaves length (cm), (<b>L</b>) Flag-leaves width (cm), (<b>M</b>) 100-seed weight (g), (<b>N</b>) Seed area (mm<sup>2</sup>), (<b>O</b>) Seed length (mm), (<b>P</b>) Seed width (mm), (<b>Q</b>) Seed roundness.</p>
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<p>Dendrogram of the hierarchical clustering of species based on Euclidean distance and McQuitty’s (WPGMA) clustering method. The analysis grouped the species into two main classes (Class1, Class2), further divided into nine subclasses (Subclass 1–9), as indicated within the dendrogram. Each branch represents a unique taxonomic group based on the morphological traits analyzed, with clustering patterns reflecting similarity levels among the species. Class1 primarily consists of 60.5% accessions from the <span class="html-italic">Hordeastrum</span> subgenus, while Class2 is entirely composed of accessions from the <span class="html-italic">Hordeum</span> subgenus. The five sections (<span class="html-italic">Hordeum</span>, <span class="html-italic">Stenostachys</span>, <span class="html-italic">Trichostachys</span>, <span class="html-italic">Marina</span>, and <span class="html-italic">Nodosa</span>) are color-coded for clear visualization of sectional affiliations.</p>
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<p>Linear Discriminant Analysis (LDA) classification results based on the top 10 traits selected from PCA (<b>A</b>,<b>B</b>). (<b>A</b>) The left scatter plot shows the classification of <span class="html-italic">Hordeum</span> sections using two LDA components, illustrating clear separation among the five sections: <span class="html-italic">Hordeum</span>, <span class="html-italic">Stenostachys</span>, <span class="html-italic">Trichostachys</span>, <span class="html-italic">Marina</span>, and <span class="html-italic">Nodosa</span>. (<b>B</b>) The right scatter plot presents species-level classification, where individual <span class="html-italic">Hordeum</span> species are distinctly separated based on the same two LDA components. These visualizations demonstrate the effectiveness of the selected traits in distinguishing both sections and species within the <span class="html-italic">Hordeum</span> genus, with high clustering consistency among groups.</p>
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<p>Confusion matrices for LDA classification of <span class="html-italic">Hordeum</span> sections and species based on PCA-selected traits (<b>A</b>,<b>B</b>). The analysis was conducted using 70% of the data as training data and 30% as testing data. (<b>A</b>) The left matrix shows the section-level classification results, where most sections (<span class="html-italic">Hordeum</span>, <span class="html-italic">Marina</span>, <span class="html-italic">Nodosa</span>, <span class="html-italic">Stenostachys</span>, and <span class="html-italic">Trichostachys</span>) exhibit high accuracy, with only minor misclassifications observed among similar sections. (<b>B</b>) The right matrix illustrates the species-level classification, with distinct separation among <span class="html-italic">Hordeum</span> species, though some species show slight misclassification, due to close morphological similarities. These confusion matrices provide a visual representation of the classification performance, highlighting the effectiveness of PCA-LDA in distinguishing sections and species within the <span class="html-italic">Hordeum</span> genus.</p>
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<p>Trait importance for section and species classification using PCA-LDA (<b>A</b>,<b>B</b>). (<b>A</b>) The feature importance for section classification, (<b>B</b>) the feature importance for species classification. Traits include days to flowering, days to maturity, culm length, spike length, number of triple-spikelets, lateral-spikelet awn length, central-spikelet awn length, flag-leaf area, flag-leaf length, flag-leaf width, 100-seed weight, seed area, seed length, and seed width.</p>
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<p>Confusion matrices for section and species classification using RF analysis based on 17 traits (<b>A</b>,<b>B</b>). The analysis was conducted with 70% of the data as training data and 30% as testing data. (<b>A</b>) The left confusion matrix shows the section-level classification results, demonstrating high accuracy in distinguishing sections such as <span class="html-italic">Hordeum</span>, <span class="html-italic">Marina</span>, <span class="html-italic">Nodosa</span>, <span class="html-italic">Stenostachys</span>, and <span class="html-italic">Trichostachys</span>. (<b>B</b>) The right confusion matrix presents species-level classification results, where most <span class="html-italic">Hordeum</span> species were accurately classified, although slight misclassifications occurred for some species. These confusion matrices visually illustrate the effectiveness of RF in classifying both sections and species within the <span class="html-italic">Hordeum</span> genus, based on the selected 17 traits.</p>
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<p>Visualization of decision tree from random forest model (<b>A</b>,<b>B</b>). This figure shows a simplified visualization of a single decision tree derived from the RF model, with a limited depth. Each node represents a feature-based data split, and includes the Gini impurity value, along with the number of samples classified into each class. From top to bottom, the figure illustrates the progressive division of data into increasingly specific classes. (<b>A</b>) represents the Section classification results, distinguishing the data at a higher hierarchical level to identify different sections. (<b>B</b>) shows the Species classification results, focusing on finer distinctions to classify species within each section.</p>
Full article ">Figure 7 Cont.
<p>Visualization of decision tree from random forest model (<b>A</b>,<b>B</b>). This figure shows a simplified visualization of a single decision tree derived from the RF model, with a limited depth. Each node represents a feature-based data split, and includes the Gini impurity value, along with the number of samples classified into each class. From top to bottom, the figure illustrates the progressive division of data into increasingly specific classes. (<b>A</b>) represents the Section classification results, distinguishing the data at a higher hierarchical level to identify different sections. (<b>B</b>) shows the Species classification results, focusing on finer distinctions to classify species within each section.</p>
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<p>Trait importance for section and species classification using RF analysis based on 17 traits (<b>A</b>,<b>B</b>). (<b>A</b>) The feature importance for section classification, where seed length, culm length, seed area, and spike length are among the traits with the highest importance in distinguishing <span class="html-italic">Hordeum</span> sections. (<b>B</b>) The feature importance for species classification, with lateral-spikelet awn length, seed width, and seed area showing the highest contributions in differentiating <span class="html-italic">Hordeum</span> species. These graphs highlight the key traits that play a significant role in classifying both sections and species within the <span class="html-italic">Hordeum</span> genus, demonstrating the effectiveness of RF in identifying essential morphological traits for accurate classification.</p>
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16 pages, 4957 KiB  
Article
Growth-Promoting Effects of Grass Root-Derived Fungi Cadophora fastigiata, Paraphoma fimeti and Plectosphaerella cucumerina on Spring Barley (Hordeum vulgare) and Italian Ryegrass (Lolium multiflorum)
by Izolda Pašakinskienė, Violeta Stakelienė, Saulė Matijošiūtė, Justas Martūnas, Marius Rimkevičius, Jurga Būdienė, Algis Aučina and Audrius Skridaila
Microorganisms 2025, 13(1), 25; https://doi.org/10.3390/microorganisms13010025 - 26 Dec 2024
Viewed by 606
Abstract
Many endophytic fungi are approved as plant growth stimulants, and several commercial biostimulants have already been introduced in agricultural practice. However, there are still many species of fungi whose plant growth-promoting properties have been understudied or not studied at all. We examined the [...] Read more.
Many endophytic fungi are approved as plant growth stimulants, and several commercial biostimulants have already been introduced in agricultural practice. However, there are still many species of fungi whose plant growth-promoting properties have been understudied or not studied at all. We examined the growth-promoting effect in spring barley (Hordeum vulgare) and Italian ryegrass (Lolium multiflorum) induced by three endophytic fungi previously obtained from the roots of Festuca/Lolium grasses. Surface-sterilized seeds were inoculated with a spore suspension of Cadophora fastigiata (isolate BSG003), Paraphoma fimeti (BSG010), Plectosphaerella cucumerina (BSG006), and their spore mixture. Before harvesting, the inoculated plants were grown in a greenhouse, with the barley being in multi-cavity trays for 30 days and ryegrass being placed in an original cylindric element system for 63 days. All three newly tested fungi had a positive effect on the growth of the barley and ryegrass plants, with the most pronounced impact observed in their root size. The fungal inoculations increased the dry shoot biomass between 11% and 26% in Italian ryegrass, but no such impact was observed in barley. The highest root increment was observed in barley. Herein, P. cucumerina and C. fastigiata inoculations were superior to other treatments, showing an increase in root dry weight of 50% compared to 20%, respectively. All fungal inoculations significantly promoted root growth in Italian ryegrass, resulting in a 20–30% increase in dry weight compared to non-inoculated plants. Moreover, a strong stimulatory effect of the fungi-emitted VOCs on the root development was observed in plate-in-plate arrays. In the presence of C. fastigiata and P. cucumerina cultures, the number of roots and root hairs in barley seedlings doubled compared to control plants. Thus, in our study, we demonstrated the potential of the grass root-derived endophytes C. fastigiata, P. fimeti, and P. cucumerina as growth promoters for spring barley and Italian ryegrass. These studies can be extended to other major crops and grasses by evaluating different fungal isolates. Full article
(This article belongs to the Special Issue Rhizosphere Bacteria and Fungi That Promote Plant Growth)
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<p>Spring barley plants affected by fungal inoculations after 30 days of growth in multi-cavity trays. (<b>A</b>) Control plants, no fungal inoculation; (<b>B</b>–<b>E</b>) inoculations as follows: <span class="html-italic">Cadophora fastigiata</span> BSG003 (<b>B</b>), <span class="html-italic">Paraphoma fimeti</span> BSG010 (<b>C</b>); <span class="html-italic">Plectosphaerella cucumerina</span> BSG006 (<b>D</b>); the three fungi mix (<b>E</b>); (<b>F</b>,<b>G</b>) representative views of the plants from <span class="html-italic">P. cucumerina</span> treatment in a multi-cavity tray (<b>G</b>), and a root display from the bottom (<b>F</b>). Scale bar 3 cm.</p>
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<p>Spring barley growth parameters affected by fungal inoculations after 30 days of growth in multi-cavity trays. (<b>A</b>) green shoot biomass; (<b>B</b>) dry root biomass. Different letters (a, b, c and d) above the bars indicate significant differences between the treatments (<span class="html-italic">p</span> ≤ 0.05) based on Tukey’s HSD post hoc test.</p>
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<p>A view of the experimental panel of Italian ryegrass plants affected by fungal inoculations after 63 days of growth in the cylinder elements system. (<b>A</b>) A representative view of five blocks of plants from <span class="html-italic">Cadophora fastigiata</span> BSG003, <span class="html-italic">Paraphoma fimeti</span> BSG010, <span class="html-italic">Plectosphaerella cucumerina</span> BSG006, the three fungi mix inoculations and the control; (<b>B</b>–<b>D</b>) representative views of the plants from <span class="html-italic">C. fastigiata</span> treatment: roots in the soil at the moment of the opening of geotextile bags (<b>B</b>), control plant roots measured (<b>C</b>) next to the plant roots from <span class="html-italic">C. fastigiata</span> inoculation (<b>D</b>).</p>
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<p>Italian ryegrass growth parameters affected by fungal inoculations after 63 days of growth in the cylinder elements system. (<b>A</b>) Shoot and root height; (<b>B</b>) shoot green and dry biomass; (<b>C</b>) shoot number; (<b>D</b>) dry root biomass. Different letters (a, b, c and d) above the bars indicate significant differences between the treatments (<span class="html-italic">p</span> ≤ 0.05) based on Tukey’s HSD post hoc test. In (<b>A</b>), regular font for root length, bold—for shoot height; in (<b>B</b>), regular—for green shoot weight, bold—for dry shoot weight; in (<b>C</b>), regular—for total stem no., bold—for nodding stem no.</p>
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<p>The effect on of barley root development exposed to VOCs of endophytic fungi <span class="html-italic">Cadophora fastigiata</span> BSG003 and <span class="html-italic">Plectosphaerella cucumerina</span> BSG006. (<b>A</b>) The effect on the number of main and lateral roots after 7 days of growth; (<b>B</b>) the effect on the number of root hairs on the 5th, 6th and 7th days in a 0.5 mm<sup>2</sup> area. (<b>C</b>–<b>E</b>) Spring barley plants grown in plate-in plate assays (top images) and microscopical images of the roots (bottom images) under the exposure of fungal cultures: (<b>C</b>) control, no fungal culture in small plates, (<b>D</b>) barley with <span class="html-italic">C. fastigiata</span> BSG003, (<b>E</b>) barley with <span class="html-italic">P. cucumerina</span> BSG010. Different letters (a, b, c and d) above the bars indicate significant differences between the treatments (<span class="html-italic">p</span> ≤ 0.05) based on Tukey’s HSD post hoc test. In (<b>A</b>), regular font for main root no., bold—for lateral root no.; in (<b>B</b>), regular—for control, bold—for <span class="html-italic">C. fastigiata</span>, italicized—for <span class="html-italic">P. cucumerina</span>.</p>
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20 pages, 2466 KiB  
Article
Silicon-Mitigated Effect on Zinc-Induced Stress Conditions: Epigenetic, Morphological, and Physiological Screening of Barley Plants
by Marzena Mazurek, Renata Tobiasz-Salach, Barbara Stadnik and Dagmara Migut
Int. J. Mol. Sci. 2025, 26(1), 104; https://doi.org/10.3390/ijms26010104 - 26 Dec 2024
Viewed by 290
Abstract
Plants are increasingly exposed to stress-induced factors, including heavy metals. Zinc, although it is a microelement, at high concentrations can be phytotoxic to plants by limiting their growth and development. The presented research confirmed the inhibition effect of Zn on morphological and physiological [...] Read more.
Plants are increasingly exposed to stress-induced factors, including heavy metals. Zinc, although it is a microelement, at high concentrations can be phytotoxic to plants by limiting their growth and development. The presented research confirmed the inhibition effect of Zn on morphological and physiological parameters in barley plants. However, the effect was Zn dose dependent (50 µM, 100 µM, and 200 µM), as well as part of the plants (above ground or roots). To mitigate the negative effects of Zn, plants were sprayed with 0.1% silicon. Silicon was proven to have a positive effect on mitigating the inhibitory effects of Zn-induced stress. In most cases, an increase in both morphological (length, elongation, fresh and dry weights, and weather content) and physiological (relative chlorophyll content and fluorescence) parameters was observed. This occurrence was dependent on the Zn dose. Epigenetic analyses confirmed differences in the DNA methylation level, both between plants subjected to stress at different strengths (50 µM, 100 µM, and 200 µM Zn) and between plants sprayed with Si or not. The differences indicate that silicon affects the epigenome of barley plants, thereby modifying the response of plants to stress factors. This modification may be the basis for plants to acquire resistance as “epigenetic memory”. Full article
(This article belongs to the Special Issue Plant Responses to Biotic and Abiotic Stresses)
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<p>Effects of the application of Zn and Si on the length (<b>A</b>) and growth (<b>B</b>) of the above-ground barley; data are expressed as mean ± SD values. * Different letters indicate significant differences between the variants of the experiment (<span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Effects of application of Zn and Si on the length (<b>A</b>) and growth (<b>B</b>) of the roots of barley; data are expressed as mean ± SD values. * Different letters indicate significant differences between the variants of the experiment (<span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Effects of the application of Zn and Si on the fresh (<b>A</b>) and dry (<b>B</b>) weights, and the water content (<b>C</b>) of the barley’s above-ground parts; data are expressed as mean ± SD values. * Different letters indicate significant differences between the variants of the experiment (<span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Effects of the application of Zn and Si on the fresh weight (<b>A</b>) and dry weight (<b>B</b>) and water content (<b>C</b>) of barley roots; data are expressed as mean ± SD values. * Different letters indicate significant differences between the variants of the experiment (<span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Effects of the application of Zn and Si on the relative chlorophyll content; data are expressed as mean ± SD values. * Different letters indicate significant differences between the variants of the experiment (<span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Effects of Zn and Si application on chlorophyll fluorescence parameters: maximum photochemical efficiency of PSII (F<sub>v</sub>/F<sub>m</sub>) (<b>A</b>), total number of active reaction centers for absorption (RC/ABS) (<b>B</b>), maximum quantum yield of primary photochemistry (F<sub>v</sub>/F<sub>0</sub>) (<b>C</b>), and performance index (PI) (<b>D</b>) in barley plants. Data are expressed as mean ± SD values. * Different letters indicate significant differences between the variants of the experiment (<span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Sample of 3 electropherograms photo-presented DNA products of selective amplification with the selected primers used. Red arrows indicate polymorphic bands (products of selective amplifications). H and M signatures indicate separate bands for combination restriction enzymes EcoRI × HpaII and EcoRI × MspI, respectively, used in the restriction step of MSAP techniques.</p>
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26 pages, 2440 KiB  
Article
Precision Nutrient and Soil Tillage Management for Sustainable Winter Barley Production (Hordeum vulgare L.) and Tillage Impact on Soil CO2 Emission
by Amare Assefa Bogale, Zoltan Kende, Akos Tarnawa, Peter Miko, Marta Birkás, Gergő Péter Kovács and Attila Percze
Agronomy 2025, 15(1), 2; https://doi.org/10.3390/agronomy15010002 - 24 Dec 2024
Viewed by 2428
Abstract
Precision sustainable agronomic practices are crucial for achieving global food security as well as mitigating climate change. A field experiment was conducted at the Hungarian University of Agriculture and Life Sciences in Gödöllő from 2023 to 2024. The study aimed to evaluate the [...] Read more.
Precision sustainable agronomic practices are crucial for achieving global food security as well as mitigating climate change. A field experiment was conducted at the Hungarian University of Agriculture and Life Sciences in Gödöllő from 2023 to 2024. The study aimed to evaluate the effects of soil tillage and foliar nutrient supplementation on winter barley yield, associated characteristics, and soil CO2 emissions. Employing a split-plot design with three replications, the experiment included four nutrient treatments (control, bio-cereal, bio-algae, and MgSMnZn blend) and two soil tillage type (i.e., plowing and cultivator). The study found that soil CO2 emissions were influenced by the crop growth stage across both tillage treatments throughout the growing seasons, but the tillage system itself did not have an effect. Similarly, the leaf chlorophyll content was not affected by tillage and nutrient treatments. Plant height, the leaf area index (LAI), and thousand kernel weights (TKW) were significantly affected by nutrient treatments across the growing seasons. Both nutrient and tillage treatments also had a notable effect on the number of productive tillers in winter barley. Moreover, nutrient and tillage treatments consistently influenced grain yield across the two growing seasons, and their interaction significantly impacted both grain yield and thousand kernel weights. The bio-cereal nutrient treatment combined with plowing tillage yielded the highest values for most parameters throughout the growing seasons. Therefore, it can be concluded that the combination of bio-cereal nutrient treatments and plowing tillage can boost winter barley yields. Notably, soil CO2 emissions peak during the crops’ reproductive stage, surpassing levels from early growth. Full article
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<p>Meteorological data recorded during the 2023 and 2024 cropping season at Godollo (<a href="https://www.meteoblue.com" target="_blank">https://www.meteoblue.com</a>, accessed on 2 November 2024).</p>
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<p>Environmental Gas Monitor 5 (EGM-5), a portable gas analyzer instrument (<a href="https://images.app.goo.gl/KYdJtY5VsNaxdrwj9" target="_blank">https://images.app.goo.gl/KYdJtY5VsNaxdrwj9</a>, accessed on 2 November 2024).</p>
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<p>Response of soil CO<sub>2</sub> at different growth stages of winter barley in different soil tillage treatments. BBCH19-29 = leaf development–tillering stage; BBCH30-49 = stem elongation–booting stage; BBCH51-73 = beginning to heading–early milky stage.</p>
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<p>Mean of relative soil–plant analysis development (SPAD values) of winter barley at different time points across different tillage types.</p>
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<p>Mean of leaf area index (LAI) values of winter barley at various recording time points across different tillage types.</p>
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<p>Interaction effects of nutrient and tillage on grain yield of winter barley in 2023 and 2024 growing seasons.</p>
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<p>Interaction effects of nutrient and tillage treatment on thousand kernel weight of winter barley in 2023 and 2024 growing seasons.</p>
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18 pages, 3297 KiB  
Article
Comprehensive Physio-Biochemical Evaluation Reveals Promising Genotypes and Mechanisms for Cadmium Tolerance in Tibetan Hull-Less Barley
by Md Rafat Al Foysal, Cheng-Wei Qiu, Jakkrit Sreesaeng, Saad Elhabashy, Delara Akhter, Shuo Zhang, Shou-Heng Shi and Feibo Wu
Plants 2024, 13(24), 3593; https://doi.org/10.3390/plants13243593 - 23 Dec 2024
Viewed by 465
Abstract
Cadmium (Cd) toxicity in agricultural soil is increasing globally and significantly impacts crop production and food safety. Tibetan hull-less barley (Hordeum vulgare L. var. nudum), an important staple food and economic crop, exhibits high genetic diversity and is uniquely adapted to [...] Read more.
Cadmium (Cd) toxicity in agricultural soil is increasing globally and significantly impacts crop production and food safety. Tibetan hull-less barley (Hordeum vulgare L. var. nudum), an important staple food and economic crop, exhibits high genetic diversity and is uniquely adapted to the harsh conditions of the Qinghai–Tibet Plateau. This study utilized hydroponic experiments to evaluate the genotypic differences in Cd tolerance among 71 Tibetan hull-less barley genotypes. Physiological assessments revealed significant reductions in various growth parameters under Cd stress compared to normal conditions: soil–plant analysis development (SPAD) value, shoot height, root length, shoot and root fresh weight, shoot and root dry weight, of 11.74%, 39.69%, 48.09%, 52.88%, 58.39%, 40.59%, and 40.52%, respectively. Principal component analysis (PCA) revealed key traits contributing to Cd stress responses, explaining 76.81% and 46.56% of the variance in the preliminary and secondary selection. The genotypes exhibited varying degrees of Cd tolerance, with X178, X192, X215, X140, and X162 showing high tolerance, while X38 was the most sensitive based on the integrated score and PCA results. Validation experiments confirmed X178 as the most tolerant genotype and X38 as the most sensitive, with observed variations in morphological, physiological, and biochemical parameters, as well as mineral nutrient responses to Cd stress. Cd-tolerant genotypes exhibited higher chlorophyll content, net photosynthesis rates, and effective photochemical capacity of photosystem II, along with an increased Cd translocation rate and reduced oxidative stress. This was accompanied by elevated activities of antioxidant enzymes, including superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT), indicating a robust stress response mechanism. These findings could facilitate the development of high-tolerance cultivars, with X178 as a promising candidate for further research and cultivation in Cd-contaminated soils. Full article
(This article belongs to the Special Issue The Genetic Improvement of Barley)
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<p>Differences in growth traits and integrated scores among 71 barley varieties under Cd stress. (<b>A</b>–<b>G</b>) Percentage reduction in various growth parameters after 15 days of exposure to 20 µM Cd stress compared to control conditions. (<b>H</b>) Integrated score based on these growth parameters; The growth parameters of barley seedlings were assessed as a percentage of the control to evaluate the impact of Cd stress. FW = fresh weight, DW = dry weight. <span style="color:#00B050">● </span>Tolerant, <span style="color:#D2A000">● </span>sensitive, ○ not considered for further evaluation. Data are presented as means of three biological replicates (n = 3). The inset “|” indicates the least significant difference (LSD) at the 0.05 probability level between varieties.</p>
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<p>Differences in growth traits and integrated scores among the seven barley genotypes. (<b>A</b>–<b>G</b>) Percentage reduction in seven growth traits after 10 days of exposure to 20 µM Cd stress, expressed as a percentage of the control values. (<b>H</b>) Integrated scores for each genotype. FW = fresh weight; DW = dry weight. <span style="color:#FFC000">■</span> Tolerant genotypes, <span style="color:#A8D08D">■</span> sensitive genotype, <span style="color:yellow">■</span> check genotype (a Cd-tolerant reported previously [<a href="#B22-plants-13-03593" class="html-bibr">22</a>]). Data are presented as means ± SD (n = 3). One-way ANOVA was used, and multiple comparisons were made using Duncan’s test. Different letters indicate significant differences at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Bi-plot based on principle component analysis of reduction percentage of barley seedling morphological characters under 20 µM Cd stress conditions. (<b>A</b>) Preliminary selection (15 days after treatment), (<b>B</b>) secondary selection (10 days after treatment). (SPAD = SPAD value, SH = shoot height, RL = root length, SFW = shoot fresh weight, RFW = root fresh weight, RDW = root dry weight and SDW = shoot dry weight, IS = integrated score).</p>
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<p>Phenotypical observation of X178, Weisuobuzhi, and X38 under control and 20 µM Cd stress (10 days after treatment, 15 days after germination). Differences in growth traits of tolerant genotype (X178), check genotype (Weisuobuzhi), and sensitive genotype (X38) varieties after 15 days under control and 20 µM Cd stress. (<b>A</b>–<b>F</b>) Six growth traits. FW = fresh weight, DW = dry weight. Data are presented as means ± SD (n = 3). One-way ANOVA was used, and multiple comparisons were made using Duncan’s test. Different letters indicate significant differences at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Effects of photosynthesis parameters of the tolerant genotype (X178), check genotype (Weisuobuzhi), and sensitive genotype (X38) under control and 20 µM Cd stress. (<b>A</b>) SPAD value; (<b>B</b>) net photosynthetic rate, Pn; (<b>C</b>) stomatal conductance, Gs; (<b>D</b>) intercellular carbon dioxide concentration, Ci; (<b>E</b>) transpiration rate, Tr; (<b>F</b>) effective photochemical efficiency of photosystem II, PhiPS2. Data are presented as means ± SD (n = 3). One-way ANOVA was used, and multiple comparisons were made using Duncan’s test. Different letters indicate significant differences at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Cd content in shoot (<b>A</b>) and root (<b>B</b>) of barley seedlings after 15 days of 20 µM Cd treatment. DW, dry weight. Translocation factor = Cd concentration in shoot/Cd concentration in the root (<b>C</b>). Data are presented as means ± SD (n = 3). One-way ANOVA was used, and multiple comparisons were made using Duncan’s test. Different letters indicate significant differences at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Effects of Cd stress on the concentrations of Zn, Cu, Mn, and Fe (mg kg<sup>−1</sup> dry weight) in shoot (<b>A</b>–<b>D</b>) and root (<b>E</b>–<b>H</b>) of barley seedlings after 15 days of 20 µM Cd treatment. Data are presented as means ± SD (n = 3). One-way ANOVA was used, and multiple comparisons were made using Duncan’s test. Different letters indicate significant differences at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Effects of Cd on contents of malondialdehyde (MDA (<b>A</b>)), hydrogen peroxide (H<sub>2</sub>O<sub>2</sub> (<b>B</b>)), and antioxidant enzyme activities of SOD (<b>C</b>), POD (<b>D</b>), and CAT (<b>E</b>) of leaves in barley seedlings after 10 days of Cd treatment. Data are presented as means ± SD (n = 3). One-way ANOVA was used, and multiple comparisons were made using Duncan’s test. Different letters indicate significant differences at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Effects of Cd toxicity on morpho-physiological, elemental (shoot and root), oxidative, and antioxidant (leaves) parameters of barley. Net photosynthetic rate (Pn), stomatal conductance (Gs), intercellular carbon dioxide concentration (Ci), transpiration rate (Tr), effective photochemical efficiency of photosystem II (PhiPS2), malondialdehyde (MDA), hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>), superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT). Each parameter changes in measured parameters under Cd treatment compared to the control.</p>
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9 pages, 923 KiB  
Article
Quantitative Trait Locus Analysis for Panicle and Flag Leaf Traits in Barley (Hordeum vulgare L.) Based on a High-Density Genetic Linkage Map
by Yichen Ye, Shuting Wen, Guo Zhang, Xingzhe Yang, Dawei Xue, Yunxia Fang and Xiaoqin Zhang
Agronomy 2024, 14(12), 2953; https://doi.org/10.3390/agronomy14122953 - 11 Dec 2024
Viewed by 589
Abstract
The yield of barley (Hordeum vulgare L.) is determined by many factors, which have always been research hotspots for agronomists and molecular scientists. In this study, five important agronomic traits related to panicle and flag leaf, including awn length (AL), panicle length [...] Read more.
The yield of barley (Hordeum vulgare L.) is determined by many factors, which have always been research hotspots for agronomists and molecular scientists. In this study, five important agronomic traits related to panicle and flag leaf, including awn length (AL), panicle length (PL), panicle neck length (NL), flag leaf length (LL) and flag leaf width (LW), were investigated and quantitative trait locus (QTL) analyses were carried out. Using a high-density genetic map of 134 recombinant inbred lines based on specific-locus amplified fragment sequencing (SLAF-seq) technology, a total of 32 QTLs were identified, which explained 12.4% to 50% of the phenotypic variation. Among them, qAL5, qNL2, qNL3, qNL6, qPL2, and qLW2 were detected in 3 consecutive years and all of the contribution rates were more than 13.8%, revealing that these QTLs were stable major QTLs and were less affected by environmental factors. Furthermore, LL and LW exhibited significant positive correlations and the localization intervals of qLL2 and qLL3 were highly overlapped with those of qLW2 and qLW3, respectively, indicating that qLL2 and qLW2, qLL3 and qLW3 may be regulated by the same genes. Full article
(This article belongs to the Special Issue Advances in Crop Molecular Breeding and Genetics)
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<p>Frequency distribution of agronomic traits in the RIL population. (<b>A</b>–<b>E</b>) are the frequency distribution of panicle lengthe, panicle neck length, awn length, flag leaf length and flag leaf width, respectively. The <span class="html-italic">X</span>-axis represents the length in centimeters, and the <span class="html-italic">Y</span>-axis represents the number of plants. Red, blue and black arrows represent the location of the parental GP and H602 in 2017, 2018 and 2019, respectively. RIL, recombinant inbred line; GP, golden promise.</p>
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<p>Localization of 32 QTLs for barley yield on the genetic linkage map. Black, red and green boxes represent QTLs in 2017, 2018 and 2019, respectively. Circles represent QTLs detected in 3 consecutive years. Black lines indicate SLAF markers.</p>
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23 pages, 6131 KiB  
Article
Salicylic Acid Mediates Chitosan-Induced Immune Responses and Growth Enhancement in Barley
by Pawel Poznanski, Abdullah Shalmani, Marcin Bryla and Waclaw Orczyk
Int. J. Mol. Sci. 2024, 25(24), 13244; https://doi.org/10.3390/ijms252413244 - 10 Dec 2024
Viewed by 741
Abstract
Chitosan (CS), derived from the partial deacetylation and hydrolysis of chitin, varies in the degree of deacetylation, molecular weight, and origin, influencing its biological effects, including antifungal properties. In plants, CS triggers immune responses and stimulates biomass growth. Previously, we found that the [...] Read more.
Chitosan (CS), derived from the partial deacetylation and hydrolysis of chitin, varies in the degree of deacetylation, molecular weight, and origin, influencing its biological effects, including antifungal properties. In plants, CS triggers immune responses and stimulates biomass growth. Previously, we found that the antifungal activity of CS was strongly dependent on its physicochemical properties. This study revealed that the chitosan batch CS_10 with the strongest antifungal activity also effectively activated plant immune responses and promoted biomass growth. Barley treated with CS_10 exhibited systemic acquired resistance (SAR), characterized by micronecrotic reactions upon Puccinia hordei (Ph) inoculation and reduced symptoms following Fusarium graminearum (Fg) infection, representing biotrophic and necrotrophic pathogens, respectively. CS_10 treatment (concentration 200 ppm) also enhanced plant biomass growth (by 11% to 15%) and promoted the accumulation of salicylic acid (SA), a hormone that regulates both plant immune responses and growth. Low levels of exogenous SA applied to plants mirrored the stimulation observed with CS_10 treatment, suggesting SA as a key regulator of CS_10-induced responses. Transcriptomic analysis identified SA-regulated genes as drivers of enhanced immunity and biomass stimulation. Thus, CS_10 not only fortifies plant defenses against pathogens like Ph and Fg but also boosts growth through SA-dependent pathways. Full article
(This article belongs to the Special Issue The Chitosan Biomaterials: Advances and Challenges—2nd Edition)
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<p>Representative picture of infection symptoms on the third barley leaves inoculated with <span class="html-italic">Fusarium graminearum</span> (<span class="html-italic">Fg</span>) in plants where the second leaves were mock (<span class="html-italic">Hv</span>-mock)- or chitosan_10 (CS)-treated (<span class="html-italic">Hv</span>_CS) (<b>A</b>). Relative number of <span class="html-italic">Fg TRI4</span> gene copies (<span class="html-italic">Fg_TRI5</span>) per one copy of barley <span class="html-italic">EFG1</span> gene (<span class="html-italic">Hv_EFG1</span>) is shown. The results are from five independent biological repetitions and the average values of genes’ quantification are shown (<b>B</b>). Asterisks indicate significance level (based on one-way ANOVA and Tukey’s post hoc test) ** <span class="html-italic">p</span> ≤ 0.01.</p>
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<p>Plant–pathogen interaction of barley plants treated with mock (<span class="html-italic">Hv</span>-mock) or with chitosan_10 solution (<span class="html-italic">Hv</span>-CS) followed by inoculation with <span class="html-italic">Puccinia hordei</span> (<span class="html-italic">Ph</span>) urediniospores. The CS_10 or mock treatments were applied to the second leaves of the plants, and the third leaves of the same plants were inoculated with <span class="html-italic">Ph</span> urediniospores. This approach allowed us to detect the results of plant immune response induced by the CS-10 and not a direct inhibitory effect of the CS-10 on the pathogen. Representative pictures of infection symptoms on the third leaves of mock- and CS_10-treated plants scored six days post-inoculation (<b>A</b>). Representative pictures of microscopic observation of infection sites of calcofluor white stained leaf samples scored from 1 to 5 days post-inoculation. Scale bars = 100 µm (<b>B</b>). Representative pictures of leaf samples stained with DAB. Scale bars = 100 µm (<b>C</b>). The rates of micronecrotic reactions in <span class="html-italic">Ph</span> infection units on barley leaves. The mean values and standard deviation were calculated based on scoring one entire leaf from each time point and three biological replicates (<b>D</b>).</p>
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<p>Concentration of total salicylic acid (SA) in barley leaves collected one day (1 d) and three days (3 d) after mock (<span class="html-italic">Hv</span>-mock) or chitosan_10 treatment (<span class="html-italic">Hv</span>-CS), or inoculation with <span class="html-italic">F. graminearum</span> (<span class="html-italic">Fg</span>) (<span class="html-italic">Hv</span>_<span class="html-italic">Fg</span>). Asterisks indicate significance level (based on one-way ANOVA and LSD post hoc test) * <span class="html-italic">p</span> ≤ 0.05 and ** <span class="html-italic">p</span> ≤ 0.01.</p>
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<p>Relative biomass gain of barley seedlings after 19 days of cultivation in Hoagland medium after treatment with seven chitosan batches (200 ppm): CS_5, CS_8-15, CS_10, CS_10-120, CS_30-100, CS_100-300, and CS_300-1000. For each sample, 40 separate plants have been tested (<b>A</b>). Relative biomass gain of barley seedlings after 19 days of cultivation in Hoagland medium after chitosan (CS_10, 200 ppm) and after salicylic acid (SA, 50 μM and 400 μM) treatment (<b>B</b>). Each box represents the percentile in range 25–75; the whiskers represent the 10 and 90 percentiles. Asterisks indicate significance level (based on one-way ANOVA and Tukey’s post hoc test) * <span class="html-italic">p</span> ≤ 0.05, ** <span class="html-italic">p</span> ≤ 0.01, and *** <span class="html-italic">p</span> ≤ 0.001.</p>
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<p>Representative picture of leaf samples used for RNA-seq analysis. <span class="html-italic">Hv</span>_mock—leaves treated with mock solution containing 0.05% acetic acid; <span class="html-italic">Hv</span>_CS—leaves treated with CS_10 (solutions of CS_10 also contained 0.05% acetic acid); <span class="html-italic">Hv</span>_<span class="html-italic">Fg</span>—leaves inoculated with <span class="html-italic">F. graminearum</span> (<span class="html-italic">Fg</span>); <span class="html-italic">Hv</span>_<span class="html-italic">Fg</span>_CS—leaves inoculated with <span class="html-italic">Fg</span> and treated with CS.</p>
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<p>Hierarchical clustering heatmap of tested variants: leaf control samples (<span class="html-italic">Hv</span>_mock), leaves treated with CS_10 (<span class="html-italic">Hv</span>_CS), inoculated with <span class="html-italic">F.</span> graminearum (<span class="html-italic">Fg</span>) (<span class="html-italic">Hv</span>_<span class="html-italic">Fg</span>), and treated with CS_10 and inoculated with <span class="html-italic">Fg</span> (<span class="html-italic">Hv</span>_<span class="html-italic">Fg</span>_CS). The three columns in each variant represent the three biological replicates (<b>A</b>). Correlation matrix of all three biological replicates of each tested variant (<b>B</b>). Principal component analysis of all tested variants (<b>C</b>).</p>
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<p>Numbers of differentially expressed genes (DEGs) in analyzed samples in relation to the control (<span class="html-italic">Hv</span>_mock). The tested variants include leaves treated with CS_10 (<span class="html-italic">Hv</span>-CS), leaves inoculated with <span class="html-italic">F. graminearum</span> (<span class="html-italic">Fg</span>) (<span class="html-italic">Hv</span>-<span class="html-italic">Fg</span>), and leaves treated with CS_10 and inoculated with <span class="html-italic">Fg</span> (<span class="html-italic">Hv</span>_<span class="html-italic">Fg</span>_CS) (<b>A</b>). Venn diagrams showing number of differentially expressed genes (DEGs) in each the three tested variants in relation to mock-treated control samples. Variants: leaves treated with CS_10 (<span class="html-italic">Hv</span>-CS), leaves inoculated with <span class="html-italic">Fg</span> (<span class="html-italic">Hv</span>-<span class="html-italic">Fg</span>), and leaves treated with CS_10 and inoculated with <span class="html-italic">Fg</span> (<span class="html-italic">Hv_Fg</span>_CS) (<b>B</b>). Presented genes are based on a cutoff value of FDR &lt; 0.05 and log2fold change &gt; 2.</p>
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<p>Top five Gene Ontology terms sorted by fold enrichment across chitosan_10 treated barley (<span class="html-italic">Hv</span>_CS) and <span class="html-italic">F. graminearum</span> inoculated barley (<span class="html-italic">Hv</span>_<span class="html-italic">Fg</span>) categorized into BPs (biological processes), MF (molecular function) and CC (cellular component) gene sets.</p>
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<p>Regulation pattern of PAL- and ICS-encoding genes in variants of chitosan_10-treated (<span class="html-italic">Hv</span>_CS), <span class="html-italic">F. graminearum</span> (<span class="html-italic">Fg</span>)-inoculated (<span class="html-italic">Hv_Fg</span>), and CS_10-treated and <span class="html-italic">Fg</span>-inoculated barley.</p>
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<p>Regulation pattern of genes encoding NPR1, NPR3, and NPR4 regulators, selected WRKY transcription factors and pathogenesis-related (PR) proteins in variants of chitosan_10-treated (<span class="html-italic">Hv</span>_CS), <span class="html-italic">F. graminearum</span> (<span class="html-italic">Fg</span>)-inoculated (<span class="html-italic">Hv_Fg</span>), and chitosan_10-treated and <span class="html-italic">Fg</span>-inoculated barley plants. The blue color indicates the SA-related genes and pathways.</p>
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<p>Validation of RNA-seq differentially expressed genes (DEGs) using RT-qPCR of four genes <span class="html-italic">NPR1</span>, <span class="html-italic">PR9</span>, <span class="html-italic">PR4</span>, and <span class="html-italic">PR14</span>. The log2-fold change values (<b>A</b>) and the linear regression between the log2-fold change of RNA-seq and RT-qPCR quantification are shown. The points represent individual results for each gene and the three variants (<b>B</b>).</p>
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<p>Schematic representation of experimental variants for transcriptome sequencing. Briefly, 14-day-old plants were inoculated with <span class="html-italic">F. graminearum</span> (<span class="html-italic">Fg</span>), followed by chitosan_10 (CS) treatment two days later and a collection of samples 5 days later. Description of tested variants: <span class="html-italic">Hv</span>_mock—barley treated with mock solution; <span class="html-italic">Hv</span>_CS—barley treated with chitosan 200 ppm; <span class="html-italic">Hv_Fg</span>_CS—barley inoculated with <span class="html-italic">Fg</span> and treated with chitosan 200 ppm; and <span class="html-italic">Hv_Fg</span>—barley inoculated with <span class="html-italic">Fg</span>.</p>
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<p>Schematic timeline of biomass measurements and chitosan (CS) or salicylic acid (SA) treatments (<b>A</b>). Representative picture of barley plants grown in semi-hydroponics (<b>B</b>).</p>
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12 pages, 1656 KiB  
Article
Shedding New Light on the Hull-Pericarp Adhesion Mechanisms of Barley Grains by Transcriptomics Analysis of Isogenic NUD1 and nud1 Lines
by Sophia V. Gerasimova, Anna M. Korotkova, Tamires de S. Rodrigues, Alexander Vikhorev, Ekaterina V. Kolosovskaya, Gennady V. Vasiliev, Michael Melzer, Christian W. Hertig, Jochen Kumlehn and Elena K. Khlestkina
Int. J. Mol. Sci. 2024, 25(23), 13108; https://doi.org/10.3390/ijms252313108 - 6 Dec 2024
Viewed by 722
Abstract
In barley having adherent hulls, an irreversible connection between the pericarp with both palea and lemma is formed during grain maturation. A mutation in the NUDUM 1 (NUD1) gene prevents this connection and leads to the formation of barley with non-adherent [...] Read more.
In barley having adherent hulls, an irreversible connection between the pericarp with both palea and lemma is formed during grain maturation. A mutation in the NUDUM 1 (NUD1) gene prevents this connection and leads to the formation of barley with non-adherent hulls. A genetic model of two isogenic lines was used to elucidate the genetic mechanisms of hull adhesion: a doubled haploid line having adherent hulls and its derivative with non-adherent hulls obtained by targeted mutagenesis of the NUD1 gene. Comparative transcriptomics analysis of the grain coats was performed at two stages of development: the milk stage, when the hulls can still be easily detached from the pericarp, and the dough stage when the hull adhesion process occurs. It was shown that the main differences in the transcriptomes lie in the genes related to DNA replication and chromatin assembly, cell wall organization, and cuticle formation. Meanwhile, genes involved in lipid biosynthesis mostly show minor differences in expression between stages and genotypes and represent a limited set of active genes. Among the 3-ketoacyl-CoA synthase (KCS) genes active during grain development, candidates for key enzymes responsible for very long-chain fatty acid elongation were identified. Full article
(This article belongs to the Section Molecular Plant Sciences)
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<p>Transcriptome comparison between outer grain tissues of <span class="html-italic">nud1</span> knockout and wild-type lines at different developmental stages. UpSet plot shows the intersection of differentially expressed genes (DEGs) across four comparisons: MSnud vs. MSwt (milk stage, <span class="html-italic">nud1</span> knockout vs. wild-type), DSnud vs. DSwt (dough stage, <span class="html-italic">nud1</span> knockout vs. wild-type), and their respective upregulated and downregulated subsets. The <span class="html-italic">x</span>-axis represents the total number of DEGs in each category, and the <span class="html-italic">y</span>-axis indicates the number of shared DEGs between the compared variants. Venn diagram illustrates the overlap of DEGs across the four comparisons, highlighting the shared and unique gene sets. MSnud, DSnud: transcriptomic libraries from the <span class="html-italic">nud1</span> knockout line at the milk and dough stages, respectively. MSwt, DSwt: transcriptomic libraries from the wild-type Golden Promise line at the milk and dough stages, respectively.</p>
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<p>Protein-protein interaction (PPI) network building from the gene expression analysis of the 431 differentially expressed genes in hull-less barley (<span class="html-italic">nud1</span>) compared to WT (<span class="html-italic">NUD1</span>) at the dough stage. The image illustrates three main clusters related to cell cycle regulation (neon blue border), lipid metabolism (yellow border), and cell wall organization (pink border).</p>
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<p>Lipid biosynthesis gene expression heatmap. Gene expression (RPKM) is shown across different developmental stages, including the milk stage (MS) and dough stage (DS), in both the wild type (wt, <span class="html-italic">NUD1</span>) and <span class="html-italic">nud1</span> knockout line (nud). KCS—3-ketoacyl-CoA synthase, KCR—3-ketoacyl-CoA reductase, HCD—b-hydroxyacyl-CoA dehydratase, ECR—enoyl-CoAreductase, CER1, CER3—acyl-CoA de-carbonylases, MAH1—midchain alkane hydroxylase, CER4—fatty acyl-CoA reductase, and WSD1—wax ester synthase. Heatmap of <span class="html-italic">Gly-Asp-Ser-Leu (GDSL) MOTIF ESTERASE/ACYLTRANSFERASE</span>/LIPASE (<span class="html-italic">GDSL</span>) and <span class="html-italic">3-KETOACYL-CoA SYNTHASE</span> (<span class="html-italic">KCS)</span> gene family expression patterns. Gene expression (RPKM) is shown across different developmental stages, including the milk stage (MS) and dough stage (DS), in both the wild-type (wt, <span class="html-italic">NUD1</span>) and <span class="html-italic">nud1</span> knockout line (nud). The gray rectangles on the heatmap indicate the absence of gene expression.</p>
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19 pages, 4835 KiB  
Article
Abundant Genetic Diversity Harbored by Traditional Naked Barley Varieties on Tibetan Plateau: Implications in Their Effective Conservation and Utilization
by NiMa QuZhen, Lhundrup Namgyal, Dawa Dondrup, Ying Wang, Zhi Wang, Xing-Xing Cai, Bao-Rong Lu and La Qiong
Biology 2024, 13(12), 1018; https://doi.org/10.3390/biology13121018 - 5 Dec 2024
Viewed by 625
Abstract
Naked barley (Hordeum vulgare var. nudum) is a staple food crop, contributing significantly to global food security. Understanding genetic diversity will facilitate its effective conservation and utilization. To determine genetic diversity and its distribution within and among varieties, we characterized 30 [...] Read more.
Naked barley (Hordeum vulgare var. nudum) is a staple food crop, contributing significantly to global food security. Understanding genetic diversity will facilitate its effective conservation and utilization. To determine genetic diversity and its distribution within and among varieties, we characterized 30 naked barley varieties from Tibet, representing the traditional, modern, and germplasm-resources-bank gene pools, by analyzing SSR molecular fingerprints. The results demonstrate abundant genetic diversity in Tibetan naked barley varieties, particularly those in the traditional gene pool that holds much more private (unique) alleles. Principal coordinates and STRUCTURE analyses indicate substantial deviation of the modern varieties from the traditional and germplasm-resources-bank varieties. A considerable amount of seed mixture is detected in the modern varieties, suggesting the practices of using mixed seeds in modern-variety cultivation. Cluster analyses further indicate the narrow genetic background of the modern varieties, likely due to the limited number of traditional/germplasm-resources-bank varieties applied in breeding. Relationships between increases in genetic diversity and sample sizes within naked barley varieties highlight the importance of effective sampling strategies for field collections. The findings from this study have important implications for the sustainable utilization and effective conservation of different types of naked barley germplasm, both in Tibet and in other regions around the world. Full article
(This article belongs to the Section Plant Science)
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<p>Panicles of two naked barley varieties from the Qinghai-Tibet Plateau, with the traditional variety (Nienachareng) showing on the left and the modern variety (Zangqing 2000) on the right.</p>
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<p>Bar plots indicating different genetic components of the 900 samples representing traditional (T-1~10), modern (M-1~10), and germplasm-resources-bank stored (G-1~10) naked barley varieties, based on the STRUCTURE analysis of SSR markers at the most optimal <span class="html-italic">K</span>-value (<span class="html-italic">K</span> = 7) and their neighboring <span class="html-italic">K</span>-values. Each sample is represented by a single vertical line, proportional to different genetic components. Many samples showed an admixture of genetic components. Different colors represent different genetic components.</p>
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<p>Scatterplots demonstrating genetic relationships of 900 samples representing traditional (red triangles), modern (blue dots), and germplasm-resources-bank stored (green squares) naked barley varieties from Tibet of China, based on the principal coordinate analysis of SSR markers.</p>
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<p>UPGMA dendrogram illustrating genetic relationships of traditional (T), modern (M), and germplasm-resources-bank stored (G) naked barley varieties (1–10) from Tibet of China, based on analyses of the pairwise Nei’s genetic similarity of SSR markers. Shaded clusters indicate the M naked barley varieties (M-1~M10) that are related to T-8, T-9, and G-5.</p>
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<p>The charts generated based on the regression analysis illustrating increases in genetic diversity (0~100%) with the increased number of samples/individuals from 5~30 intervals in the traditional (T), modern (M), and germplasm-resources-bank stored (G) naked barley varieties. Genetic diversity was represented by the parameters of Nei’s genetic diversity (<span class="html-italic">H<sub>e</sub></span>), Shannon information index (<span class="html-italic">I</span>), and the percentage of polymorphic loci (<span class="html-italic">P</span>). The lines indicate the regression fitting curves; the rings represent the average values calculated from each of the 10 varieties in the T, M, and G gene pools.</p>
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13 pages, 2027 KiB  
Article
Antihypertensive Effect of Perla and Esmeralda Barley (Hordeum vulgare L.) Sprouts in an Induction Model with L-NAME In Vivo
by Abigail García-Castro, Alma D. Román-Gutiérrez, Fabiola A. Guzmán-Ortiz and Raquel Cariño-Cortés
Metabolites 2024, 14(12), 678; https://doi.org/10.3390/metabo14120678 - 3 Dec 2024
Viewed by 1812
Abstract
Background: Hypertension is one of the leading causes of premature death worldwide. Despite advances in conventional treatments, there remains a significant need for more effective and natural alternatives to control hypertension. In this context, sprouted barley extracts have emerged as a potential therapeutic [...] Read more.
Background: Hypertension is one of the leading causes of premature death worldwide. Despite advances in conventional treatments, there remains a significant need for more effective and natural alternatives to control hypertension. In this context, sprouted barley extracts have emerged as a potential therapeutic option. This study presents the evaluation of the bioactive properties of extracts from two varieties of barley germinated for different periods (3, 5, and 7 days), focusing on their potential to regulate blood pressure mechanisms. Objectives/Methods: The main objective was to assess the effects of these extracts on blood pressure regulation in N(ω)-Nitro-L-Arginine Methyl Ester (L-NAME)-induced hypertensive rats. Renal (creatinine, urea, uric acid, and total protein) and endothelial (NOx levels) function, angiotensin-converting enzyme (ACE) I and II activity, and histopathological effects on heart and kidney tissues were evaluated. Results: In particular, Esmeralda barley extract demonstrated 83% inhibition of ACE activity in vitro. Furthermore, the combined administration of sprouted barley extract (SBE) and captopril significantly reduced blood pressure and ACE I and II activity by 22%, 81%, and 76%, respectively, after 3, 5, and 7 days of germination. The treatment also led to reductions in protein, creatinine, uric acid, and urea levels by 3%, 38%, 42%, and 48%, respectively, along with a 66% increase in plasma NO concentrations. Conclusions: This study highlights the bioactive properties of barley extracts with different germination times, emphasizing their potential health benefits as a more effective alternative to conventional antihypertensive therapies. Full article
(This article belongs to the Special Issue Plants and Plant-Based Foods for Metabolic Disease Prevention)
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<p>Decrease in a ngiotensin-converting e nzyme activity (percent). Bars represent the mean ± SD. The significance levels are represented by the value of <span class="html-italic">p</span> &lt; 0.05 (* <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.0001) compared to the control group (c aptopril). Different letters indicate significant differences between varieties and the same day of germination. ANOVA followed by a Tukey test was performed.</p>
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<p>(<b>a</b>) Effects of barley extracts and captopril supplementation on serum ACE I activity. (<b>b</b>) Effects of barley extracts and captopril supplementation on kidney ACE II activity. Each bar represents mean ± SD. Significance levels indicated by <span class="html-italic">p</span> &lt; 0.05 (* <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.0001) when compared with L-NAME group (ANOVA followed by Tukey test).</p>
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<p>Histopathological changes in cardiac muscle (H-E stain). (<b>A</b>) Normotensive group, normal cardiac. The arrow indicates the transverse striation of the longitudinal section of the cardiac muscle.; (<b>B</b>) L-NAME group, chronic inflammation; (<b>C</b>) L-NAME + Captopril group, inflammatory cells decreased inflammation; (<b>D</b>) L-NAME + Esmeralda group, inflammatory cells are observed, moderate inflammation; (<b>E</b>) L-NAME + Perla group, reduction in inflammation; (<b>F</b>) L-NAME + Perla + Esmeralda group, without histopathological changes; (<b>G</b>) L-NAME Esmeralda + Captopril group, inflammation relief. The arrows in (<b>B</b>–<b>D</b>), indicate the infiltration of proinflammatory cells.</p>
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<p>Histopathological changes in renal glomerulus. The arrows indicate the following changes in the renal glomerulus: (<b>A</b>) Normotensive group, without observable changes; (<b>B</b>) L-NAME group, degeneration and necrosis of renal glomerulus; (<b>C</b>) L-NAME + Captopril group, partial adhesion of glomerulus to Bowman’s capsules; (<b>D</b>) L-NAME + Esmeralda group, slight glomerular necrosis; (<b>E</b>) L-NAME + Perla group, Bowman space dilation; (<b>F</b>) L-NAME + Perla + Esmeralda group, decreased inflammation of renal glomerulus; (<b>G</b>) L-NAME Esmeralda + Captopril group, without observable histological changes.</p>
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26 pages, 1867 KiB  
Article
Enhancing Soil Microbial Activity and Spelt (Triticum spelta L.) Yield in Organic Farming Through Biofertilizer and Green Manure Applications
by Rafał Górski, Alicja Niewiadomska, Anna Płaza, Agnieszka Wolna-Maruwka, Dorota Swędrzyńska, Katarzyna Głuchowska and Robert Rosa
Agronomy 2024, 14(12), 2845; https://doi.org/10.3390/agronomy14122845 - 28 Nov 2024
Viewed by 437
Abstract
At present, there is growing consumer interest in Triticum spelta L., which has high nutritional value. This species is recommended for cultivation in organic farming. In this system of agriculture, biofertilizers are an alternative to mineral fertilization. Biofertilizers stimulate plant growth by providing [...] Read more.
At present, there is growing consumer interest in Triticum spelta L., which has high nutritional value. This species is recommended for cultivation in organic farming. In this system of agriculture, biofertilizers are an alternative to mineral fertilization. Biofertilizers stimulate plant growth by providing nutrients through the biological fixation of molecular nitrogen from the air or by increasing the availability of insoluble nutrients in the soil and by synthesizing substances that stimulate plant growth. Green manure biomass and root secretions provide growth material for soil microorganisms, and microorganisms return nutrients to the soil and plants through nutrient decomposition and conversion. Considering the many benefits of using biofertilizers and growing cereals with cover crops for green manure in cereal rotations, field research was carried out on an organic farm to evaluate the soil microbes and the amount of biomass from green manures and their follow-up effect on Triticum spelta L. yields using biofertilizers. Two factors were researched: (I) biofertilizers: control object (no biofertilizer), Azotobacter chroococcum + Azospirillum lipoferum Br 17, Arthrobacter agilis + Bacillus megaterium var. phosphaticum, and combined application of atmospheric nitrogen-fixing bacteria with phosphate solubilizing bacteria; (II) green manures: control object (no green manure application), Trifolium pratense L., Trifolium pratense L. + Lolium multiflorum L., and Lolium multiflorum L. The results show that the most favorable abundance of microorganisms determined in the soil after harvesting Hordeum vulgare L. was recorded after the application of biofertilizers containing atmospheric nitrogen-fixing bacteria with phosphate-solubilizing bacteria under a mixture of Trifolium pratense L. with Lolium multiflorum L. Plowing green manure from a mixture of Trifolium pratense L. with Lolium multiflorum L. resulted in an average increase of 39% in grain yield of Triticum spelta L., while the application of a biofertilizer containing Azotobacter chroococcum + Azospirillum lipoferum Br 17 + Arthrobacter agilis + Bacillus megaterium var. phosphaticum resulted in an average increase of 63%. The proposed spelt wheat cultivation technique can be recommended for agricultural practice due to the positive response of grain yield, but it may also be an important direction for further research to reduce the negative impact of agriculture on the environment. Full article
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<p>The total number of bacteria in the soil after harvesting <span class="html-italic">Hordeum vulgare</span> L. was influenced by the application of biofertilizer and living mulch (means across 2019–2021), CFU 10<sup>5</sup> g<sup>−1</sup> dm soil. Values in bars represent mean values from three years of field research + standard deviation for individual experimental objects and mean values of main effects + standard deviation (BF1—<span class="html-italic">Azotobacter chroococcum</span> + <span class="html-italic">Azospirillum lipoferum</span> Br 17; BF2—<span class="html-italic">Arthrobacter agilis</span> + <span class="html-italic">Bacillus megaterium</span> var. <span class="html-italic">phosphaticum</span>; BF3—<span class="html-italic">Azotobacter chroococcum</span> + <span class="html-italic">Azospirillum lipoferum</span> Br 17 + <span class="html-italic">Arthrobacter agilis</span> + <span class="html-italic">Bacillus megaterium</span> var. <span class="html-italic">phosphaticum</span>)<span class="html-italic">;</span> the same small letter (a, b, c, d) indicates no significant difference between values for biofertilizer × LM interaction; the same capital letter (A, B, C) indicates no significant difference between mean values for biofertilizer and LM.</p>
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<p>The number of <span class="html-italic">Actinobacteria</span> in the soil after harvesting <span class="html-italic">Hordeum vulgare</span> L. was influenced by the application of biofertilizer and living mulch (means across 2019–2021), CFU 10<sup>4</sup> g<sup>−1</sup> dm soil. Values in bars represent mean values from three years of field research + standard deviation for individual experimental objects and mean values of main effects + standard deviation (BF1—<span class="html-italic">Azotobacter chroococcum</span> + <span class="html-italic">Azospirillum lipoferum</span> Br 17; BF2—<span class="html-italic">Arthrobacter agilis</span> + <span class="html-italic">Bacillus megaterium</span> var. <span class="html-italic">phosphaticum</span>; BF3—<span class="html-italic">Azotobacter chroococcum</span> + <span class="html-italic">Azospirillum lipoferum</span> Br 17 + <span class="html-italic">Arthrobacter agilis</span> + <span class="html-italic">Bacillus megaterium</span> var. <span class="html-italic">phosphaticum</span>)<span class="html-italic">;</span> same small letter (a, b, c) indicates no significant difference between values for biofertilizer × LM interaction; the same capital letter (A, B, C) indicates no significant difference between mean values for biofertilizer and LM.</p>
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<p>The number of fungi in the soil after harvesting <span class="html-italic">Hordeum vulgare</span> L. was influenced by the application of biofertilizer and living mulch (means across 2019–2021), CFU 10<sup>4</sup> g<sup>−1</sup> dm soil. Values in bars represent mean values from three years of field research + standard deviation for individual experimental objects and mean values of main effects + standard deviation (BF1—<span class="html-italic">Azotobacter chroococcum</span> + <span class="html-italic">Azospirillum lipoferum</span> Br 17; BF2—<span class="html-italic">Arthrobacter agilis</span> + <span class="html-italic">Bacillus megaterium</span> var. <span class="html-italic">phosphaticum</span>; BF3—<span class="html-italic">Azotobacter chroococcum</span> + <span class="html-italic">Azospirillum lipoferum</span> Br 17 + <span class="html-italic">Arthrobacter agilis</span> + <span class="html-italic">Bacillus megaterium</span> var. <span class="html-italic">phosphaticum</span>)<span class="html-italic">;</span> the same small letter (a, b, c, d) indicates no significant difference between values for biofertilizer × LM interaction; the same capital letter (A, B, C, D) indicates no significant difference between mean values for biofertilizer and LM.</p>
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<p>The total number of bacteria/number of <span class="html-italic">Actinobacteria</span> in the soil after harvesting <span class="html-italic">Hordeum vulgare</span> L. was influenced by the application of biofertilizer and living mulch (means across 2019–2021). Values in bars represent mean values from three years of field research + standard deviation for individual experimental objects and mean values of main effects + standard deviation (BF1—<span class="html-italic">Azotobacter chroococcum</span> + <span class="html-italic">Azospirillum lipoferum</span> Br 17; BF2—<span class="html-italic">Arthrobacter agilis</span> + <span class="html-italic">Bacillus megaterium</span> var. <span class="html-italic">phosphaticum</span>; BF3—<span class="html-italic">Azotobacter chroococcum</span> + <span class="html-italic">Azospirillum lipoferum</span> Br 17 + <span class="html-italic">Arthrobacter agilis</span> + <span class="html-italic">Bacillus megaterium</span> var. <span class="html-italic">phosphaticum</span>)<span class="html-italic">;</span> the same small letter (a, b, c, d) indicates no significant difference between values for biofertilizer × LM interaction; the same capital letter (A, B, C, D) indicates no significant difference between mean values for biofertilizer and LM.</p>
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<p>The total number of bacteria/number of fungi in the soil after harvesting <span class="html-italic">Hordeum vulgare</span> L. was influenced by the application of biofertilizer and living mulch (means across 2019–2021). Values in bars represent mean values from three years of field research + standard deviation for individual experimental objects and mean values of main effects + standard deviation (BF1—<span class="html-italic">Azotobacter chroococcum</span> + <span class="html-italic">Azospirillum lipoferum</span> Br 17; BF2—<span class="html-italic">Arthrobacter agilis</span> + <span class="html-italic">Bacillus megaterium</span> var. <span class="html-italic">phosphaticum</span>; BF3—<span class="html-italic">Azotobacter chroococcum</span> + <span class="html-italic">Azospirillum lipoferum</span> Br 17 + <span class="html-italic">Arthrobacter agilis</span> + <span class="html-italic">Bacillus megaterium</span> var. <span class="html-italic">phosphaticum</span>)<span class="html-italic">;</span> the same capital letter (A, B, C) indicates no significant difference between mean values for biofertilizer and LM.</p>
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<p>The total number of bacteria + number of <span class="html-italic">Actinobacteria</span>/number of fungi in the soil after harvesting <span class="html-italic">Hordeum vulgare</span> L. was influenced by the application of biofertilizer and living mulch (means across 2019–2021). Values in bars represent mean values from three years of field research + standard deviation for individual experimental objects and mean values of main effects + standard deviation (BF1—<span class="html-italic">Azotobacter chroococcum</span> + <span class="html-italic">Azospirillum lipoferum</span> Br 17; BF2—<span class="html-italic">Arthrobacter agilis</span> + <span class="html-italic">Bacillus megaterium</span> var. <span class="html-italic">phosphaticum</span>; BF3—<span class="html-italic">Azotobacter chroococcum</span> + <span class="html-italic">Azospirillum lipoferum</span> Br 17 + <span class="html-italic">Arthrobacter agilis</span> + <span class="html-italic">Bacillus megaterium</span> var. <span class="html-italic">phosphaticum</span>)<span class="html-italic">;</span> the same small letter (a, b, c, d) indicates no significant difference between values for biofertilizer × LM interaction; the same capital letter (A, B, C) indicates no significant difference between mean values for biofertilizer and LM.</p>
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15 pages, 4997 KiB  
Article
A Comprehensive Analysis of the 9-Cis Epoxy Carotenoid Dioxygenase Gene Family and Their Responses to Salt Stress in Hordeum vulgare L.
by Fatima Omari Alzahrani
Plants 2024, 13(23), 3327; https://doi.org/10.3390/plants13233327 - 27 Nov 2024
Viewed by 547
Abstract
Barley (Hordeum vulgare L.) is among the earliest crops to be cultivated and is also considered a crucial staple crop. Nevertheless, the negative effects of abiotic stress on both the quality and productivity of barley are significant. Nine-cis-epoxycarotenoid dioxygenases (NCEDs) are rate-limiting [...] Read more.
Barley (Hordeum vulgare L.) is among the earliest crops to be cultivated and is also considered a crucial staple crop. Nevertheless, the negative effects of abiotic stress on both the quality and productivity of barley are significant. Nine-cis-epoxycarotenoid dioxygenases (NCEDs) are rate-limiting enzymes in plants that cleave carotenoids and produce abscisic acid (ABA). The poor utilization of barley NCEDs in stress-resistant genetic breeding is due to the lack of appropriate information about their potential function in abiotic stress. The current study revealed five NCED genes in the barley genome (HvNCED1HvNCED5), which are distributed unevenly on barley chromosomes. The PF03055 domain is present in all HvNCEDs, and they encode 413~643 amino acids. Phylogenetic analysis showed that NCED genes were categorized into three distinct clades, confirming the homology of NCED genes between H. vulgare L., Arabidopsis thaliana L., and Oryza sativa L. Expression analysis revealed that HvNCED1 is significantly upregulated under high salt stress, indicating its potential role in enhancing salt tolerance. In contrast, HvNCED3 and HvNCED4 exhibited downregulation, suggesting a complex regulatory mechanism in response to varying salt stress levels. These findings will enhance our comprehension of the genetic composition and evolutionary development of the HvNCED gene family and provide a basis for future research on their role in response to salt-induced stress. Full article
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Figure 1

Figure 1
<p>The phylogenetic tree of the <span class="html-italic">HvNCED</span> gene family, along with sequences from <span class="html-italic">Arabidopsis thaliana</span>, <span class="html-italic">Oryza sativa</span>, and <span class="html-italic">Hordeum vulgare.</span> The clades are represented using a range of distinct colors. The values of the bootstraps are provided.</p>
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<p>Chromosomal locations and gene duplication of the <span class="html-italic">HvNCED</span> gene family. The red line indicates the synteny between HvNCED3 and <span class="html-italic">HvNCED4</span> genes.</p>
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<p>The synteny analysis of the <span class="html-italic">NCED</span> gene family members in three plant species: <span class="html-italic">Arabidopsis thaliana</span>, <span class="html-italic">Oryza sativa</span>, and <span class="html-italic">Hordeum vulgare</span>. The background of the image displays grey lines that show the synteny of the complete genome, whereas the red lines specifically demonstrate the synteny of the <span class="html-italic">NCED</span> genes.</p>
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<p>The cis-element analysis of <span class="html-italic">HvNCEDs</span>.</p>
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<p>The <span class="html-italic">HvNCED</span> genes’ intron/exon structures are shown. Exons are represented by the pink boxes, introns by the black lines, and untranslated regions (UTRs) by the blue boxes.</p>
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<p>HvNCED protein’s conserved motifs. Different preserved motifs are represented by various colored boxes.</p>
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<p>(<b>A</b>): Expression profile of <span class="html-italic">HvNCED</span>s in different tissues of <span class="html-italic">Hordeum vulgare</span> (root, leaves, and flowers). (<b>B</b>): Expression profile of <span class="html-italic">HvNCED</span>s in anthers at four specific time intervals (0.3–0.4 mm, 0.5–0.9 mm, 1.0–1.2 mm, 1.3–1.4 mm).</p>
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<p>(<b>A</b>): Expression profile of <span class="html-italic">HvNCED</span>s across drought-tolerant and drought-sensitive barley genotypes. (<b>B</b>): Expression profile of <span class="html-italic">HvNCED</span>s across control (22 °C) and heat-stressed barley plants (35 °C).</p>
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<p>RT-qPCR analysis of the <span class="html-italic">HvNCED</span>s (<span class="html-italic">HvNCED1</span>, <span class="html-italic">HvNCED3</span>, <span class="html-italic">HvNCED4</span>) in the leaves of Hordeum <span class="html-italic">vulgare</span> under salt stress (<span class="html-italic">p</span> &lt; 0.01).</p>
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