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Search Results (1,362)

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12 pages, 1273 KiB  
Article
New Terpenoids and Lignans from Phyllanthus acidus Fruits with Antioxidant Activity
by Ying Xin, Jia Xu, Na Li, Li-Ying Yang, Hong-Tao Zhu and Ying-Jun Zhang
Foods 2025, 14(3), 452; https://doi.org/10.3390/foods14030452 - 30 Jan 2025
Abstract
The fruits of Phyllanthus acidus, rich in various secondary metabolites and possessing significant antioxidant activity, have been consumed widely by many Southeast Asian people, including the Thai, Vietnamese, Burmese, Laotians, and Cambodians. An extensive investigation of the secondary metabolites of the fruits [...] Read more.
The fruits of Phyllanthus acidus, rich in various secondary metabolites and possessing significant antioxidant activity, have been consumed widely by many Southeast Asian people, including the Thai, Vietnamese, Burmese, Laotians, and Cambodians. An extensive investigation of the secondary metabolites of the fruits resulted in our obtaining 17 compounds, including four new compounds (14). The absolute configurations of 1, 3, and 4 were determined by comparing their experimental electronic circular dichroism (ECD) spectra with both reference data and computed ECD profiles. At a concentration of 40μM, terpenoids (1 and 59) showed no cytotoxic activity against five strains of human tumor cells and one of normal cells. Notably, the known lignan 13 and phenylpropanoid 15 showed obvious ABTS+ radical scavenging activities with IC50 values of 203.7 and 232.9 μM, which have a comparable impact to the positive control, Trolox (IC50 = 176.5 ± 2.0 μM). The results indicated that P. acidus fruits could be a promising sources of antioxidant food supplement. Full article
(This article belongs to the Section Food Nutrition)
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Figure 1
<p>Compounds <b>1</b>–<b>17</b> isolated from the fruits of <span class="html-italic">Phyllanthus acidus</span>.</p>
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<p>Key 2D correlations in compounds <b>1–4</b>.</p>
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<p>Experimental ECD curves of <b>1</b> and phyllanthacidoid R.</p>
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<p>Experimental and calculated ECD curves of <b>3</b> and <b>4</b>.</p>
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17 pages, 2396 KiB  
Article
Treasures Induced by Narrow-Spectrum: Volatile Phenylpropanoid and Terpene Compounds in Leaves of Lemon Basil (Ocimum × citriodorum Vis.), Sweet Basil (O. basilicum L.) and Bush Basil (O. minimum L.) Under Artificial Light City Farm Conditions
by Anna V. Shirokova, Sofya A. Dzhatdoeva, Alexander O. Ruzhitskiy, Sergey L. Belopukhov, Valeria L. Dmitrieva, Victoria E. Luneva, Lev B. Dmitriev, Victor A. Kharchenko, Azret A. Kochkarov and Elchin G. Sadykhov
Plants 2025, 14(3), 403; https://doi.org/10.3390/plants14030403 - 29 Jan 2025
Viewed by 225
Abstract
The cultivation of aromatic plants that are valuable for nutritional and medical aims under artificial conditions with narrow-band LED lighting is becoming widespread. A comparison of the effects of conventional basil field and greenhouse conditions and a city farm (CF) with LED lighting [...] Read more.
The cultivation of aromatic plants that are valuable for nutritional and medical aims under artificial conditions with narrow-band LED lighting is becoming widespread. A comparison of the effects of conventional basil field and greenhouse conditions and a city farm (CF) with LED lighting on essential oil and its components was studied in Ocimum × citriodorum Vis. “Kapriz” (OcK), O. basilicum L. “Queen Sheba” (ObQS) and O. minimum L. “Vasilisk” (OmV). Essential oil (EO) was extracted by hydrodistillation from dry leaves of the basil varieties. EO composition was studied by gas chromatography, while the number of glandular trichomes was studied by scanning electron microscopy. We found that in leaves of CF plants, ObQS and OmV increased EO yield (22.9 and 22.7 g/kg DW, respectively) compared to field conditions (10.9 and 13.7 g/kg DW, respectively). The number of glands with four-celled heads also increased. In OcK plants, the amount of EO was almost unchanged, but the number of capitate glandular trichomes was strongly increased. Biochemical analysis showed that in CF plants compared to field ones, eugenol accumulated 40% more in ObQS and three times more in OmV. In addition, 10.9% estragol was detected in the leaves of OcK plants, which was absent in field plants. Thus, LED lighting conditions increased the biosynthesis of phenylpropanoid volatile components in Ocimum. Full article
(This article belongs to the Special Issue The Growth and Development of Vegetable Crops)
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Figure 1
<p>Plants of <span class="html-italic">Ocimum × citriodorum</span> cv. “<span class="html-italic">Kapriz</span>”, <span class="html-italic">O. minimum</span> cv. “<span class="html-italic">Vasilisk</span>” and <span class="html-italic">O. basilicum</span> cv. “<span class="html-italic">Queen Sheba</span>”, grown in a field (F), greenhouse (GH) and city farm (CF).</p>
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<p>Essential oil content in basil leaves of <span class="html-italic">Ocimum × citriodorum</span> cv. “<span class="html-italic">Kapriz</span>”, <span class="html-italic">O. basilicum</span> cv. “<span class="html-italic">Queen Sheba</span>” and <span class="html-italic">O. minimum</span> cv. “<span class="html-italic">Vasilisk</span>” in the field (F), greenhouse (GH) and city farm (CF). Different letters denote the significant variations measured by Duncan’s multiple range test at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Glandular and non-glandular trichomes on leaves of <span class="html-italic">O. × citriodorum</span> cv. “<span class="html-italic">Kapriz</span>” (<b>a</b>–<b>c</b>), <span class="html-italic">O. basilicum</span> cv. “<span class="html-italic">Queen Sheba</span>” (<b>d</b>–<b>f</b>) and <span class="html-italic">O. minimum</span> cv. “<span class="html-italic">Vasilisk</span>” (<b>g</b>–<b>i</b>). Non-glandular trichomes along leaf edge: (<b>c</b>)—multicellular falcate conical hairs; (<b>f</b>)—rough trichomes; (<b>i</b>)—unicellular short rough trichomes.</p>
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<p>Mean numbers of capitate sessile and subsessile GTs on adaxial (Adax.) and abaxial (Abax.) sides of leaves of <span class="html-italic">O. × citriodorum</span> “<span class="html-italic">Kapriz</span>” (<b>A</b>), <span class="html-italic">O. basilicum</span> “<span class="html-italic">Queen Sheba</span>” (<b>B</b>) and <span class="html-italic">O. minimum</span> “<span class="html-italic">Vasilisk</span>“ (<b>C</b>).</p>
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<p>Changes in volatile compounds in the leaves of <span class="html-italic">O. × citriodorum</span> “<span class="html-italic">Kapriz</span>”, <span class="html-italic">O. basilicum</span> “<span class="html-italic">Queen Sheba</span>” and <span class="html-italic">O. minimum</span> “<span class="html-italic">Vasilisk</span>” under outdoor and indoor conditions. Designation: <span style="background:#92d050"><span class="html-italic">OcK</span></span>, <span style="background:#ff0066"><span class="html-italic">ObQS</span></span>, <span style="background:#b48fff"><span class="html-italic">OmV</span></span>; in top-down columns—<span class="html-italic">field</span>—<span class="html-italic">italic</span>, greenhouse—normal, city farm—<b>bold</b>. The arrows next to the numbers show an increase (red) or decrease (blue) in component content from different classes under city farm conditions compared to the field [<a href="#B59-plants-14-00403" class="html-bibr">59</a>], with changes.</p>
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20 pages, 3256 KiB  
Article
Chemical Biology Meets Metabolomics: The Response of Barley Seedlings to 3,5-Dichloroanthranilic Acid, a Resistance Inducer
by Claude Y. Hamany Djande, Paul A. Steenkamp and Ian A. Dubery
Molecules 2025, 30(3), 545; https://doi.org/10.3390/molecules30030545 - 25 Jan 2025
Viewed by 336
Abstract
Advances in combinatorial synthesis and high-throughput screening methods have led to renewed interest in synthetic plant immunity activators as well as priming agents. 3,5-Dichloroanthranilic acid (3,5-DCAA) is a derivative of anthranilic acid that has shown potency in activating defence mechanisms in Arabidopsis and [...] Read more.
Advances in combinatorial synthesis and high-throughput screening methods have led to renewed interest in synthetic plant immunity activators as well as priming agents. 3,5-Dichloroanthranilic acid (3,5-DCAA) is a derivative of anthranilic acid that has shown potency in activating defence mechanisms in Arabidopsis and barley. Chemical biology, which is the interface of chemistry and biology, can make use of metabolomic approaches and tools to better understand molecular mechanisms operating in complex biological systems. Here we report on the untargeted metabolomic profiling of barley seedlings treated with 3,5-DCAA to gain deeper insights into the mechanism of action of this resistance inducer. Histochemical analysis revealed the production of reactive oxygen species in the leaves upon 3,5-DCAA infiltration. Subsequently, methanolic extracts from different time periods (12, 24, and 36 h post-treatment) were analysed by ultra-high-performance liquid chromatography hyphenated to a high-resolution mass spectrometer. Both unsupervised and supervised chemometric methods were used to reveal hidden patterns and highlight metabolite variables associated with the treatment. Based on the metabolites identified, both the phenylpropanoid and octadecanoid pathways appear to be main routes activated by 3,5-DCAA. Different classes of responsive metabolites were annotated with flavonoids, more specifically flavones, which were the most dominant. Given the limited understanding of this inducer, this study offers a metabolomic analysis of the response triggered by its foliar application in barley. This additional insight could help make informed decisions for the development of more effective strategies for crop protection and improvement, ultimately contributing to crop resilience and agricultural sustainability. Full article
(This article belongs to the Section Chemical Biology)
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Graphical abstract

Graphical abstract
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<p>Qualitative determination of oxidative reactions in leaves of barley (<span class="html-italic">Hordeum vulgare</span>) treated with different concentrations of 3,5-dichloroanthranilic acid (100, 150, and 200 μM). (<b>A</b>) DAB stain for the determination of hydrogen peroxide and (<b>B</b>) NBT stain for the determination of superoxide radicals. NTC = nontreated controls, DMSO = solvent/vehicle control, PC = positive control, yeast cell wall elicitor, 100 µg mL<sup>−1</sup>.</p>
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<p>(<b>A</b>) Relative concentration of 3,5-DCAA in barley leaves following spray application of a 200 μM solution and incubation for 12, 24, and 36 h. Error bars indicate the standard deviations of the average peak areas of 3,5-DCAA present in the samples. (<b>B</b>) Structure of 3,5-dichloroanthranilic acid. Characteristic features of 3,5-DCAA-type inducers/elicitors are the backbone structure of a benzoic acid substituted with an amino group at position 2 and the presence of chlorines at positions 3 and 5.</p>
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<p>Base peak intensity (BPI) chromatograms generated by ultra-high-performance liquid chromatography—mass spectrometry (UHPLC–MS) in (<b>A</b>) negative and (<b>B</b>) positive ionisation modes for extracts of plants treated with 3,5-DCAA for 12, 24, and 36 h (light blue to dark blue, with purple representing the control of 24 h). The Y-axes are linked for comparison of relative peak intensities that are compared to the chromatogram of the control at 24 h (ElimC24h). The grey ellipses indicate some of the apparent differences at the BPI level. (<b>C</b>,<b>D</b>) show principal component analysis (PCA) score plots of ESI(–) and (+) data, respectively. All data were Pareto scaled, and the ellipses in each PCA score plot represent calculated Hoteling’s T<sup>2</sup> with a 95% confidence interval. (<b>C</b>) A seven-component model explaining 69.1% variation and predicting 50.3% variation. (<b>D</b>) An eight-component model explaining 68.3% variation and predicting 43.0% variation.</p>
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<p>Orthogonal projection to latent structures discriminant analysis (OPLS-DA) for the selection of discriminant metabolites associated with the 3,5-DCAA treatment at 24 h. (<b>A</b>) shows a permutation test (<span class="html-italic">n</span> = 100) generated to evaluate the OPLS-DA model of ‘Control vs. 3,5-DCAA (<span class="html-italic">x</span>-axis, component 1 + 2 + 0; R<sup>2</sup>X = 0.502, R<sup>2</sup>Y = 0.991, Q<sup>2</sup> = 0.980. CV-ANOVA = 6.65 × 10<sup>−11</sup>). For (<b>B</b>,<b>C</b>), the same colour code (red, blue, and green) was used, with (<b>B</b>) depicting the OPLS-DA loading S-plot showing selected features which are statistically significant in discriminating the two compared groups, control vs. 3,5-DCAA. Blue and red respectively indicate positive or negative correlation to the treatment, with green as unchanged. The reliability (correlation) and magnitude (covariance) of the samples in the model are shown on the axes as p(corr) and p [<a href="#B1-molecules-30-00545" class="html-bibr">1</a>], respectively. (<b>C</b>) Variable importance in projection (VIP) scores, &gt;1 for each selected feature or <span class="html-italic">m</span>/<span class="html-italic">z</span> variable. (<b>D</b>) OPLS-DA S-lines highlight the differences between the treatment (in blue, positive values) and the control (in red, negative values) with regards to the occurrence of discriminant variables and their relative intensities.</p>
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<p>(<b>A</b>) Venn diagram of distribution of annotated metabolites across time points, showing specific and common metabolites. The details of these metabolites are found in the text and in <a href="#molecules-30-00545-t001" class="html-table">Table 1</a>. (<b>B</b>) Allocation of annotated metabolites classes and (<b>C</b>) heatmap showing the average levels of metabolites across all treatments (control 12, 24, and 36 h vs. DCAA 12, 24, and 36 h).</p>
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<p>Relative quantification of selected metabolites and associated functions. These metabolites belong to the class of aromatic amino acids, fatty acid derivatives, phenolic acid derivatives, and flavonoids. The bar graphs are representative of the average of each metabolite peak area and give an indication of the abundance of the selected metabolite in the samples at 12, 24, and 36 h.</p>
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<p>Simplified representation of multi-step enzymatic reactions of apigenin and luteolin biosynthesis. PAL: phenylalanine ammonia lyase; C4H: cinnamate 4-hydroxylase; CCL: coumaroyl-CoA ligase; CHS: chalcone synthase; CHI: chalcone isomerase; F3′H: flavone 3′-hydroxylase; FNS: flavone synthase. The structures on the sides of the central pathway represent the core (skeleton) structure of flavonoids and flavones. FNS directs flavanones to the flavone route. Flavones are flavonoids characterised by a double bond between C-2 and C-3. The sugar residues of known natural flavonoid C-glycoside compounds are primarily attached to the C-6 and C-8 of the A ring or as 7-O-glycosides to the hydroxyl group on C-7. In addition, hydroxycinnamic acids may be attached as sinapoyl and feruloyl units to the 6” position of the sugar residues.</p>
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13 pages, 5516 KiB  
Article
Effect of Four Different Initial Drying Temperatures on Biochemical Profile and Volatilome of Black Tea
by Zaifa Shu, Huijuan Zhou, Limin Chen, Yuhua Wang, Qingyong Ji and Weizhong He
Metabolites 2025, 15(2), 74; https://doi.org/10.3390/metabo15020074 - 25 Jan 2025
Viewed by 428
Abstract
Background: Black tea processing conditions significantly affect the final taste and flavor profiles, so researchers are now focusing on developing equipment and improving the appropriate processing conditions of major black tea varieties. Methods: Here, we tested the effect of four different initial drying [...] Read more.
Background: Black tea processing conditions significantly affect the final taste and flavor profiles, so researchers are now focusing on developing equipment and improving the appropriate processing conditions of major black tea varieties. Methods: Here, we tested the effect of four different initial drying temperatures, i.e., R65 (65 °C), R85 (85 °C), R105 (105 °C), and R125 (125 °C), on the sensory and biochemical profiles and volatilome of the black tea variety “Lishui wild” (LWV). Results: Our results indicate that both 85 and 105 °C are better than 65 and 125 °C for initial drying for 20 min. R105 had the highest sensory evaluation scores due to better shape, aroma, taste, leaf base, thearubigins, theanine, caffeine, and ratio of theaflavins + thearubigins to theaflavins. Both R85 and R105 had higher catechins than R65 and R125. The LWV volatilome consisted of esters (19.89%), terpenoids (18.95%), ketones (11.3%), heterocyclic compounds (9.99%), and alcohols (8.59%). In general, acids, aldehydes, amines, aromatics, ethers, hydrocarbons, phenols, sulfur compounds, and terpenoids accumulated in higher amounts in R85 and R105. The highly accumulated compounds in R105 were associated with green, fruity, sweet, woody, floral, hawthorn, mild, nutty, powdery, rose, and rosy flavors. The main pathways affected are secondary metabolites, sesquiterpenoid and triterpenoid biosynthesis, glycerolipid metabolism, zeatin biosynthesis, phenylpropanoid biosynthesis, ABC transport, glutathione metabolism, etc. Therefore, R105 can be used to achieve the optimal taste, flavor, and aroma of LWV. Conclusions: Overall, the presented data can be used by the tea industry for processing black tea with the most optimum volatile substances, catechins, theanine, amino acids, and other compounds. Full article
(This article belongs to the Section Plant Metabolism)
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<p>(<b>A</b>) Sensory evaluation of processed tea leaves and soup. (<b>B</b>) Sensory evaluation scores for tea leaves and soup. (<b>C</b>) Degree of color differences. R65, R85, R105, and R125 refer to processing temperatures 65 °C, 85 °C, 105 °C, and 125 °C, respectively. The graphs show mean values of four replicates. Different letters above bar graphs mean significant difference at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Effect of processing temperature on biochemical profile of black tea. (<b>A</b>) Water extract (%), (<b>B</b>) pigment contents (theaflavin, thearubigins, and theabrownins), (<b>C</b>) ratios of the pigments, (<b>D</b>) amino acid, (<b>E</b>) theanine, (<b>F</b>) total polyphenols, and (<b>G</b>) % caffeine content in LWV black tea processed at different temperatures. R65, R85, R105, and R125 refer to processing temperatures 65 °C, 85 °C, 105 °C, and 125 °C, respectively. The graphs show mean values of four replicates. Different letters above bar graphs mean significant difference at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Global volatilome profile of LWV tea processed at different temperatures. (<b>A</b>) Heatmap of the relative metabolite intensities of the detected compounds in each class. (<b>B</b>) Proportion of the detected compounds in each class. (<b>C</b>) Pearson’s correlation and (<b>D</b>) principal component analysis between treatment replicates. (<b>E</b>) R65, R85, R105, and R125 refer to processing temperatures 65 °C, 85 °C, 105 °C, and 125 °C, respectively. The numbers 1–5 with each treatment indicate the replicates.</p>
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<p>Differential volatilome profile of LWV processed at different temperatures. (<b>A</b>) Number of differentially accumulated metabolites. (<b>B</b>) Heatmap of the relative metabolite intensities and (<b>C</b>) flavor profiles of DAMs between R65 and R85. (<b>D</b>) Heatmap of the relative metabolite intensities and (<b>E</b>) flavor profiles of DAMs between R85 and R105. (<b>F</b>) Heatmap of the relative metabolite intensities and (<b>G</b>) flavor profiles of DAMs between R105 and R125.</p>
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19 pages, 5920 KiB  
Article
Rootstock Selection for Resisting Cucumber Fusarium Wilt in Hainan and Corresponding Transcriptome and Metabolome Analysis
by Lingyu Wang, Qiuxia Yi, Panpan Yu, Sunjeet Kumar, Xuyang Zhang, Chenxi Wu, Zhenglong Weng, Mengyu Xing, Kaisen Huo, Yanli Chen and Guopeng Zhu
Plants 2025, 14(3), 359; https://doi.org/10.3390/plants14030359 - 24 Jan 2025
Viewed by 430
Abstract
Soilborne diseases are important problems in modern agricultural production. Fusarium oxysporum f. sp. cucumerinum (FOC) is one of the predominant soilborne pathogens threatening cucumber cultivation, especially in Hainan, China. This study assessed FOC-resistant rootstocks using incidence rate, disease severity index (DSI), and area [...] Read more.
Soilborne diseases are important problems in modern agricultural production. Fusarium oxysporum f. sp. cucumerinum (FOC) is one of the predominant soilborne pathogens threatening cucumber cultivation, especially in Hainan, China. This study assessed FOC-resistant rootstocks using incidence rate, disease severity index (DSI), and area under the disease severity index curve (AUDRC), revealing “JinJiaZhen (Mc-4)” as resistant and “JinGangZhuan 1901 (Mc-18)” as susceptible. Comprehensive transcriptome and metabolome analyses were conducted to investigate the defense mechanisms of these rootstocks, revealing key pathways, such as the mitogen-activated protein kinase (MAPK) signaling pathway, starch and sucrose metabolism, and phenylpropanoid biosynthesis, which are crucial for plant disease resistance. Additionally, the study compared the resistance mechanisms of two other rootstocks, Mc-4 and Mc-18, against FOC infection through transcriptomic and metabolomic analyses. Mc-4 exhibited a higher number of differentially expressed genes (DEGs) related to phenylpropanoid biosynthesis compared to Mc-18. Untargeted metabolomics identified 4093 metabolites, with phenylpropanoid biosynthesis, isoquinoline alkaloid biosynthesis, and porphyrin metabolism as primary annotated pathways. On the sixth day post-inoculation, when the number of DEGs and differentially accumulated metabolites (DAMs) was highest, phenylpropanoid biosynthesis emerged as a key pathway in Mc-4, with 37 DEGs and 8 DAMs identified. Notably, Mc-4 showed upregulated expression of genes encoding enzymes involved in phenylpropanoid biosynthesis and increased accumulation of related metabolites, such as coniferyl-aldehyde, coniferyl alcohol, and coniferyl acetate. These findings highlight the differential defense mechanisms between resistant and sensitive rootstocks and provide insights into plant–pathogen interactions. This study’s results will contribute to the development of better and disease-free cucumber varieties, promoting sustainable agriculture. Full article
(This article belongs to the Special Issue Plant Immune Mechanisms)
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<p>Photographs of rootstocks inoculated with FOC after 21 days. As can be seen from the figure, the rootstock JinJiaZhen exhibits better disease resistance compared with other rootstocks in some degree. Even at 21 days post-inoculation, this variety maintains a healthy state with relatively green leaves, indicating its superior resistance to <span class="html-italic">Fusarium</span> wilt. This contrast is quite evident when compared with other rootstocks that are markedly affected by the pathogen, such as QuanFuTaiLang and GenLiShen, where the plants display obvious wilting symptoms.</p>
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<p>AUDRC results of rootstocks with resistance level of “resistant (R)”. The X-axis in <a href="#plants-14-00359-f002" class="html-fig">Figure 2</a> represents the time elapsed since the inoculation of the rootstocks. The 21st day of the experiment is normalized to 1 on the X-axis, while the first day is normalized to 0. For reference, the 5th day is represented as 0.2 on the X-axis. This time transformation was introduced to align with the definition of the area under the receiver operating characteristic (AUC) curve in machine learning, ensuring that the highest AUC value is standardized to 1. This normalization will facilitate future comparisons between different experiments.</p>
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<p>Analysis of transcriptional differences between Mc-4 and Mc-18 under FOC stress conditions. (<b>A</b>) Transcriptome correlation analysis under FOC stress. (<b>B</b>) Changes in DEGs in Mc-4 and Mc-18 under FOC stress. “Up” represents upregulated genes, and “down” represents downregulated genes. (<b>C</b>,<b>D</b>,<b>G</b>,<b>H</b>) Venn analysis ((<b>C</b>) Mc-4 upregulated differential genes; (<b>D</b>) Mc-4 downregulated differential genes; (<b>G</b>) Mc-18 upregulated differential genes; (<b>H</b>) Mc-18 downregulated differential genes). (<b>E</b>,<b>F</b>,<b>I</b>–<b>L</b>) KEGG pathways of differentially expressed genes ((<b>E</b>) Mc-4 upregulated differential genes; (<b>F</b>) Mc-4 downregulated differential genes; (<b>I</b>) Mc-18 upregulated differential genes; (<b>J</b>) Mc-18 downregulated differential genes; (<b>K</b>) A6 vs. B6 upregulated differential genes; (<b>L</b>) A6 vs. B6 downregulated differential genes). A0, and B0: Mc-4 and Mc-18, which were not inoculated with FOC, served as the control group. A2, A4, and A6: Mc-4 was treated with FOC for 2, 4, and 6 days. B2, B4, and B6: Mc-18 was treated with FOC for 2, 4, and 6 days. The length of the bars in the bar chart and the numbers on the right side indicate the number of DEGs between groups. A higher number of DEGs suggests that the pathway may be an important contributor to the differences observed between groups.</p>
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<p>Analysis of metabolome differences between Mc−4 and Mc−18 under FOC stress conditions. (<b>A</b>) PCA of DAMs. (<b>B</b>) Correlation analysis of DAMs. (<b>C</b>) The DAMs in Mc−4 and Mc−18. The length of the bars in the bar chart and the numbers on the right side represent the number of DAMs between groups. (<b>D</b>) Changes in DAMs in the Mc−4 and Mc−18 under FOC stress. “Up” represents upregulated metabolites, and “down” represents downregulated metabolites. (<b>E</b>) KEGG pathway of A0 vs. B0 differential metabolites. (<b>F</b>) KEGG pathway of A6 vs. B6 differential metabolites. A0, and B0: Mc−4 and Mc−18, which were not inoculated with FOC, served as the control group. A2, A4, and A6: Mc−4 was treated with FOC for 2, 4, and 6 days. B2, B4, and B6: Mc−18 was treated with FOC for 2, 4, and 6 days. The darker color and larger size of the triangles and circles in the figure indicate that the corresponding metabolic pathway plays an important role.</p>
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<p>Phenylpropanoid biosynthesis of A6, B6 in Mc−4 and Mc−18 under FOC stress.</p>
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<p>WGCNA analysis of Mc−4 and Mc−18 under FOC stress. (<b>A</b>) Hierarchical cluster trees show the co-expression modules identified by WGCNA. (<b>B</b>) Co-expression modules by WGCNA. Relationships between modules (left) and traits (bottom). Red and blue represent positive and negative correlations, respectively, with coefficient values and <span class="html-italic">p</span>-values.</p>
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24 pages, 7713 KiB  
Article
Integrating Physiology, Transcriptome, and Metabolomics Reveals the Potential Mechanism of Nitric Oxide Concentration-Dependent Regulation of Embryo Germination in Sorbus pohuashanensis
by Caihong Zhao, Yue Zhang and Ling Yang
Plants 2025, 14(3), 344; https://doi.org/10.3390/plants14030344 - 23 Jan 2025
Viewed by 407
Abstract
Nitric oxide (NO) breaks a seed’s dormancy and stimulates germination by signaling. However, the key physiological metabolic pathways and molecular regulatory mechanisms are still unclear. Therefore, this study used physiological, transcriptomic, and metabolomics methods to analyze the key genes and metabolites involved in [...] Read more.
Nitric oxide (NO) breaks a seed’s dormancy and stimulates germination by signaling. However, the key physiological metabolic pathways and molecular regulatory mechanisms are still unclear. Therefore, this study used physiological, transcriptomic, and metabolomics methods to analyze the key genes and metabolites involved in the NO regulation of plant embryo germination and their potential regulatory mechanisms. The physiological analysis results indicate that the appropriate concentration of NO increased the content of NO and hydrogen peroxide (H2O2) in cells, stimulated the synthesis of ethylene and jasmonic acid (JA), induced a decrease in abscisic acid (ABA) content, antagonistic to the gibberellin (GA3) effect, and promoted embryo germination and subsequent seedling growth. However, the high concentrations of NO caused excessive accumulation of H2O2, destroyed the reactive oxygen species (ROS) balance, and inhibited embryo germination and seedling growth. The combined analysis of transcriptomics and metabolomics showed that the genes related to phenylpropanoid (phenylalanine ammonia-lyase, trans-cinnamate 4-monooxygenase, ferulate-5-hydroxylase, coniferyl-alcohol glucosyltransferase), and flavonoid synthesis (10 genes such as CHS) were significantly up-regulated during embryo germination. The high concentration of exogenous NO inhibited embryo germination by up-regulating the expression of 4-coumaric acid coenzyme A ligase (4CL) and negatively regulating the expression of flavonoid synthesis genes. This suggests that NO concentration-dependently regulates phenylpropanoid and flavonoid biosynthesis, thereby affecting ROS metabolism and hormone levels, and ultimately regulates the dormancy and germination of Sorbus pohuashanensis embryos. Full article
(This article belongs to the Special Issue Sexual and Asexual Reproduction in Forest Plants)
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<p>Effect of different concentrations of SNP on the embryo germination rate of <span class="html-italic">Sorbus pohuashanensis</span>. (Note: ns means <span class="html-italic">p</span> &gt; 0.05, ** means <span class="html-italic">p</span> ≤ 0.01, **** means <span class="html-italic">p</span> ≤ 0.0001).</p>
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<p>Germination model of an embryo of <span class="html-italic">Sorbus pohuashanensis</span>. (<b>a</b>) The phenotype of <span class="html-italic">Sorbus pohuashanensis</span> embryos cultured to day 8 after treatment with different SNP concentrations; (<b>b</b>) There are three stages of embryo germination of <span class="html-italic">Sorbus pohuashanensis</span>: 0–3 d is pre-germination, 3–5 d is mid-germination, and 5–8 d is late-germination.</p>
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<p>Effect of different concentrations of SNP treatment on the RNA content of <span class="html-italic">Sorbus pohuashanensis</span> embryos. (<b>a</b>) NO content. (<b>b</b>) NOS. (<b>c</b>) NR. Note: Different small letters in the figure indicate significant differences between the different treatments (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Effect of different SNP concentrations on the content of reactive oxygen species in the embryos of <span class="html-italic">Sorbus pohuashanensis</span>. (<b>a</b>) H<sub>2</sub>O<sub>2</sub> content. (<b>b</b>) superoxide anion formation rate. (<b>c</b>) malondialdehyde content. (<b>d</b>) SOD content. (<b>e</b>) POD content. (<b>f</b>) CAT content. (<b>g</b>) soluble protein content. (<b>h</b>) glutathione (<b>i</b>) glutathione (oxidized). Note: Different small letters in the figure indicate significant differences between the different treatments (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Effect of different concentrations of SNP treatment on the hormone content of <span class="html-italic">Sorbus pohuashanensis</span> embryos. (<b>a</b>) ABA content. (<b>b</b>) GA<sub>3</sub>/ABA. (<b>c</b>) ACC/ABA. (<b>d</b>) JA/ABA. Note: Different small letters in the figure indicate significant differences between the different treatments (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Correlation analysis of RNS, ROS, and hormones. Note, the diagonal represents the sample name. The color from blue to red indicates the change of correlation from negative to positive. The larger the figure of the upper and lower triangular regions, the greater the correlation, and vice versa.</p>
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<p>Effects of different concentrations of NO treatment on the transcriptome changes of <span class="html-italic">Sorbus pohuashanensis</span> embryos. (<b>a</b>) The number of differential genes in <span class="html-italic">Sorbus pohuashanensis</span>. (<b>b</b>) Differential enrichment analysis of <span class="html-italic">Sorbus pohuashanensis</span>.</p>
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<p>Verification of differential genes by qRT-PCR.</p>
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<p>(<b>a</b>) PCA scores were detected in <span class="html-italic">Sorbus pohuashanensis</span> embryos at 3 h and 3 d after different concentrations of SNP treatment. PCA scores were derived from all metabolites detected in the three replicate samples under each treatment. (<b>b</b>) Venn diagram of DAMs among various pair-wise comparisons.</p>
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<p>Difference analysis of gene expression after treatment with different concentrations of SNP. (<b>a</b>) PG-3h vs. CK-3h; (<b>b</b>) PG-3d vs. CK-3d; (<b>c</b>) IG-3h vs. CK-3h; (<b>d</b>) IG-3d vs. CK-3d.</p>
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<p>KEGG pathways of DEGS and DAMs. (<b>a</b>) PG-3h vs. CK-3h. (<b>b</b>) IG-3h vs. CK-3h.</p>
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<p>Transcriptome and metabolome combined analysis of nine quadrants. The black dotted line, from left to right, from top to bottom, is divided into 1-9 quadrants. (<b>a</b>) PG-3h vs. CK-3h, (<b>b</b>) IG-3h vs. CK-3h.</p>
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<p>Regulatory co-expression network of different genes and different metabolites related to the biosynthesis of phenylpropanoids and flavonoids. The Cytoscape software (version 3.10.0) was used to visualize the network. (<b>a</b>) PG-3h vs. CK-3h, (<b>b</b>) IG-3h vs. CK-3h.</p>
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<p>Phenylpropanoid biosynthetic pathway. (<b>a</b>) NO concentration-dependent regulation of phenylpropanoid biosynthesis signaling pathway during embryo germination of <span class="html-italic">Sorbus pohuashanensis</span>. The blue frame indicates PG-3h vs. CK-3h, and the yellow frame indicates that IG-3h vs. CK-3h influences the pathway of phenylpropanoid biosynthesis compared to PG-3h vs. CK-3h. Red indicates upregulated genes/metabolites, blue indicates upregulated and downregulated genes/metabolites, and green indicates downregulated genes/metabolites. (<b>b</b>) Heatmap of gene and metabolite expression levels. Note, PAL, phenylalanine ammonia-lyase; CYP73A, trans-cinnamate 4-monooxygenase; 4CL, 4-coumarate-CoA ligase; CCR, cinnamoyl-CoA reductase; HCT, shikimate O-hydroxycinnamoyltransferase; CSE, caffeoylshikimate esterase; COMT, caffeic acid 3-O-methyltransferase/acetylserotonin O-methyltransferase; CYP98A(C3′H), 5-O-(4-coumaroyl)-D-quinate 3′-monooxygenase; K22395, cinnamyl-alcohol dehydrogenase; POD, peroxidase; UGT72E, coniferyl-alcohol glucosyltransferase; CAD, cinnamyl-alcohol dehydrogenase; REF1, coniferyl-aldehyde dehydrogenase; CYP84A (F5H), ferulat-5-hydroxylase.</p>
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<p>The pathway of flavonoid biosynthesis pathway. (<b>a</b>) NO concentration-dependent regulation of flavonoid biosynthesis signaling pathway during embryo germination of <span class="html-italic">Sorbus pohuashanensis</span>. The blue outer frame indicates PG-3h compared to CK-3h, and the grey outer frame indicates IG-3h compared to CK-3h. (<b>b</b>) Heatmap of gene and metabolite expression levels. Note, CHI, chalcone isomerase; CHS, chalcone synthase, FLS, flavonol synthase; NAC, naringenin chalcone; CYP75B1, flavonoid 3′-monooxygenase; CYP73A, trans-cinnamate 4-monooxygenase; HCT, shikimate O-hydroxycinnamoyl transferase; CYP98A(C3′H), 5-O-(4-coumaroyl)-D-quinate 3′-monooxygenase; ANR, anthocyanidin reductase; PTG1, phlorizin synthase.</p>
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14 pages, 1562 KiB  
Article
GC-MS Profiling of Ethanol-Extracted Polyherbal Compounds from Medicinal Plant (Citrullus colocynthis, Curcuma longa, and Myristica fragrans): In Silico and Analytical Insights into Diabetic Neuropathy Therapy via Targeting the Aldose Reductase
by Mohd Adnan Kausar, Sadaf Anwar, Halima Mustafa Elagib, Kehkashan Parveen, Malik Asif Hussain, Mohammad Zeeshan Najm, Abhinav Nair and Subhabrata Kar
Curr. Issues Mol. Biol. 2025, 47(2), 75; https://doi.org/10.3390/cimb47020075 - 23 Jan 2025
Viewed by 583
Abstract
Diabetic neuropathy is one of the severe complications of diabetes, which affects the quality of life in a patient and increases the risk of amputations and chronic wounds. Current therapeutic approaches are symptomatically oriented, focusing on comfort and non-inflammatory aspects without addressing the [...] Read more.
Diabetic neuropathy is one of the severe complications of diabetes, which affects the quality of life in a patient and increases the risk of amputations and chronic wounds. Current therapeutic approaches are symptomatically oriented, focusing on comfort and non-inflammatory aspects without addressing the mechanism or molecular target of the disease. The present study investigates the therapeutic effects of an ethanolic polyherbal extract from Citrullus colocynthis (Bitter Apple), Curcuma longa (Turmeric), and Myristica fragrans (Nutmeg) using advanced in silico and analytical methods. According to the findings, PHE showed the presence of a total of 39 bioactive compounds in GC–MS analysis, which include alcohols, fatty acids, terpenoids, esters, neolignans, phenylpropanoids, and steroids. Three of the compounds—-4-isopropyl-1,6-dimethyl-1,2,3,4-tetrahydronaphthalene (−11.4 kcal/mol), (1S,2R)-2-(4-allyl-2,6-dimethoxyphenoxy)-1-(3,4,5-trimethoxyphenyl)-1-propanol (−9.8 kcal/mol) and (S)-5-Allyl-2-((1-(3,4-dimethoxyphenyl)propan-2-yl)oxy)-1,3-dimethoxybenzene (−10.3 kcal/mol)—followed the Lipinski rule and showed the binding affinity with aldol reductase. Docking experiments showed that compound 4-isopropyl-1,6-dimethyl-1,2,3,4-tetrahydronaphthalene (−11.4 kcal/mol) has high-affinity binding to aldose reductase, an enzyme involved in diabetic neuropathy pathophysiology, whereas molecular dynamics simulations show long-range persistence of the interaction of (S)-5-Allyl-2-((1-(3,4-dimethoxyphenyl)propan-2-yl)oxy)-1,3-dimethoxybenzene with aldol reductase in physiological conditions. Therefore, this combination of herbal therapy and advanced computational/analytical techniques could be leading towards innovative, multi-targeted therapies against diabetic neuropathy. Nevertheless, further studies in vivo are required to confirm the efficacy, safety, and pharmacokinetics of the PHE in biological systems. Full article
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<p>Schematic representation of polyol pathway of glucose metabolism, accumulation of sorbitol causes osmotic stress, which leads to the pathogenesis of diabetic complication.</p>
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<p>GC-MS chromatogram of PHE showing the presence of different compounds at different retention times. * Tentatively Identified Compounds (TIC).</p>
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<p>Presents a representative image illustrating the docking analysis of selected ligands with high binding affinity for aldol reductase. (i) The 3D interaction of the ligands with aldol reductase is depicted, with each ligand represented in stick form and shown in different colors: (<b>A</b>) (i) 4-isopropyl-1,6-dimethyl-1,2,3,4-tetrahydronaphthalene (in magenta); (<b>B</b>) (i) (1S,2R)-2-(4-allyl-2,6-dimethoxyphenoxy)-1-(3,4,5-trimethoxyphenyl)-1-propanol (in cyan); and (<b>C</b>) (i) (S)-5-Allyl-2-((1-(3,4-dimethoxyphenyl)propan-2-yl)oxy)-1,3-dimethoxybenzene (in blue). (<b>A</b>) (ii), (<b>B</b>) (ii), (<b>C</b>) (ii) The 2D interaction of the ligands with aldol reductase is generated by LigPlot v2.2.</p>
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<p>(<b>A</b>) Represents the RMSD graph of unbound protein (blue color) and protein-bound ligand (red color) over 200 ns of simulation. (<b>B</b>) Represents the RMSF graph of unbound protein (red color) and protein-bound ligand complex (green color) over 200 ns of simulation.</p>
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13 pages, 3076 KiB  
Article
Comprehensive Nutrient Profiling and Untargeted Metabolomic Assessment of Siraitia grosvenorii from Different Regions and Varying Degrees of Processing
by Yuqiang Liu, Weiqian Yu, Mengfei Bi, Yuting Zhang, Yuan Guan and Tiemin Jiang
Appl. Sci. 2025, 15(3), 1020; https://doi.org/10.3390/app15031020 - 21 Jan 2025
Viewed by 366
Abstract
The primary objective of this study was to compare the nutrition and metabolite profiles of Siraitia grosvenorii from different regions (namely Yongfu and Longsheng) and processing stages. Our findings showed that fresh Siraitia grosvenorii from Longsheng contained higher levels of total sugars, protein, [...] Read more.
The primary objective of this study was to compare the nutrition and metabolite profiles of Siraitia grosvenorii from different regions (namely Yongfu and Longsheng) and processing stages. Our findings showed that fresh Siraitia grosvenorii from Longsheng contained higher levels of total sugars, protein, and crude fat compared with those from Yongfu, though both regions had similar dietary fiber and ash content. Dried Yongfu Siraitia grosvenorii showed increased nutrient levels. A mineral analysis revealed that fresh Siraitia grosvenorii from Yongfu had the highest levels of calcium, magnesium, and potassium along with distinct differences in other mineral concentrations compared with Longsheng. Notably, fresh Yongfu fruits had higher mineral content than dried ones, except for aluminum and selenium. Through an untargeted metabolomics analysis, we identified 470 metabolites, showing significant variation between fresh samples from Yongfu and Longsheng and between fresh and dried Yongfu samples. Key metabolites included carboxylic acids, fatty acyls, and organooxygen compounds. Additionally, we observed significant enrichment in metabolic pathways such as phenylpropanoid biosynthesis, galactose metabolism, and linoleic acid metabolism, with notable differences in metabolite regulation depending on the region and processing stage. These findings highlight the influence of regional environmental factors and drying processes on the nutrient and metabolite composition of Siraitia grosvenorii. Full article
(This article belongs to the Special Issue New Trends in the Structure Characterization of Food)
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<p>The OPLS-DA models for the data from the positive and negative ionization modes. (<b>A</b>–<b>D</b>) Permutation plots. (<b>E</b>–<b>H</b>) Score plots.</p>
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<p>Differential metabolites statistics histogram (VIP ≥ 1 and <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Clustering heatmap of differential metabolites. The columns represent samples and the rows represent metabolites. The gradient color is used to represent the magnitude of the quantitative values. The redder the color, the higher the expression level, and the bluer the color, the lower the expression level.</p>
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<p>Metabolic pathway influence factor bubble chart. The abscissa represents the impact values enriched in different metabolic pathways, while the ordinate represents the enrichment pathways. The size of the dots indicates the number of corresponding metabolites in the pathway. The color is related to the <span class="html-italic">p</span>-value. The redder the color, the smaller the <span class="html-italic">p</span>-value, and the bluer the color, the larger the <span class="html-italic">p</span>-value.</p>
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17 pages, 3385 KiB  
Review
The Role of E3 Ubiquitin Ligase Gene FBK in Ubiquitination Modification of Protein and Its Potential Function in Plant Growth, Development, Secondary Metabolism, and Stress Response
by Yuting Wu, Yankang Zhang, Wanlin Ni, Qinghuang Li, Min Zhou and Zhou Li
Int. J. Mol. Sci. 2025, 26(2), 821; https://doi.org/10.3390/ijms26020821 - 19 Jan 2025
Viewed by 398
Abstract
As a crucial post-translational modification (PTM), protein ubiquitination mediates the breakdown of particular proteins, which plays a pivotal role in a large number of biological processes including plant growth, development, and stress response. The ubiquitin-proteasome system (UPS) consists of ubiquitin (Ub), ubiquitinase, deubiquitinating [...] Read more.
As a crucial post-translational modification (PTM), protein ubiquitination mediates the breakdown of particular proteins, which plays a pivotal role in a large number of biological processes including plant growth, development, and stress response. The ubiquitin-proteasome system (UPS) consists of ubiquitin (Ub), ubiquitinase, deubiquitinating enzyme (DUB), and 26S proteasome mediates more than 80% of protein degradation for protein turnover in plants. For the ubiquitinases, including ubiquitin-activating enzyme (E1), ubiquitin-conjugating enzyme (E2), and ubiquitin ligase (E3), the FBK (F-box Kelch repeat protein) is an essential component of multi-subunit E3 ligase SCF (Skp1-Cullin 1-F-box) involved in the specific recognition of target proteins in the UPS. Many FBK genes have been identified in different plant species, which regulates plant growth and development through affecting endogenous phytohormones as well as plant tolerance to various biotic and abiotic stresses associated with changes in secondary metabolites such as phenylpropanoid, phenolic acid, flavonoid, lignin, wax, etc. The review summarizes the significance of the ubiquitination modification of protein, the role of UPS in protein degradation, and the possible function of FBK genes involved in plant growth, development, secondary metabolism, and stress response, which provides a systematic and comprehensive understanding of the mechanism of ubiquitination and potential function of FBKs in plant species. Full article
(This article belongs to the Special Issue New Insights into Environmental Stresses and Plants)
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<p>Classification of different types of ubiquitination processes: (<b>a</b>) mono-ubiquitination, (<b>b</b>) multimono-ubiquitination, (<b>c</b>) linear polyubiquitination, and (<b>d</b>) branching polyubiquitination.</p>
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<p>Post-translational modifications (PTMs), ubiquitination, and ubiquitin–26S proteasome system (UPS) in plants. Ub, ubiquitin; DUB, deubiquitinating enzyme; RING, Really Interesting New Gene; HECT, Homology to E6-associated Carboxy-Terminus; RBR, Ring Between Ring; CRLs, Cullin-RING Ligases; APC/C, Anaphase Promoting Complex/Cyclosome; CBC VHL, Cullin-Elongin-BC-VHL; SCF, SKP1-Cullin1-F-box; BTB, Bric-a-brac-Tram track-Broad; DDB, DNA damage-binding domain-containing; APC, an-aphase-promoting complex; CUL1, Cullin1; RBX1, RING Box-1; SKP1, S-phase Kinase-associated Protein 1; FBK, Kelch structure; FBL, LRR repeat-rich structural domain; FBW, WD40 repeat structure; FBT, Tub structure; FBP, Phloem Protein 2 domain; FBA-D, F-box structure-associated domain. A rectangular box represents a component which cooperates with other components to perform a function in the system, and an oval box represents a subfamily member which exhibits an independent function in the system. Text highlighted in red is the F-Box gene which is discussed in details in this review.</p>
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<p>A working model of protein degradation depending on the ubiquitin–26S proteasome system (UPS) in plants. ADP, adenosine diphosphate; ATP, adenosine triphosphate; CUL1, Cullin1; DUB, deubiquitinating enzyme; E1, ubiquitin-activating enzyme; E2, ubiquitin-conjugating enzyme; E3, ubiquitin-ligating enzyme; RBX1, RING Box-1; SKP1, S-phase Kinase-associated Protein 1; SCF, Skp1-Cullin 1-F-box; Ub, ubiquitin.</p>
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<p>The function of <span class="html-italic">FBK</span> genes related to secondary metabolism in different plant species. The red or green background indicates that the gene positively or negatively regulates the biosynthesis of secondary metabolites, respectively: red=positive and green=negative. Different blue backgrounds indicate different secondary metabolites. All genes in the figure encode F-box protein with Kelch structures. The numbers in parentheses indicate references related to relevant findings. Genes and their correlative references: <span class="html-italic">AtSnRK1</span> [<a href="#B83-ijms-26-00821" class="html-bibr">83</a>]; <span class="html-italic">AtKFB01</span> [<a href="#B24-ijms-26-00821" class="html-bibr">24</a>,<a href="#B80-ijms-26-00821" class="html-bibr">80</a>,<a href="#B85-ijms-26-00821" class="html-bibr">85</a>]; <span class="html-italic">AtKFB20</span> [<a href="#B24-ijms-26-00821" class="html-bibr">24</a>,<a href="#B80-ijms-26-00821" class="html-bibr">80</a>,<a href="#B81-ijms-26-00821" class="html-bibr">81</a>,<a href="#B85-ijms-26-00821" class="html-bibr">85</a>]; <span class="html-italic">AtKFB50</span> [<a href="#B24-ijms-26-00821" class="html-bibr">24</a>,<a href="#B80-ijms-26-00821" class="html-bibr">80</a>,<a href="#B85-ijms-26-00821" class="html-bibr">85</a>]; <span class="html-italic">KFB39</span> [<a href="#B80-ijms-26-00821" class="html-bibr">80</a>,<a href="#B85-ijms-26-00821" class="html-bibr">85</a>]; <span class="html-italic">KFB<sup>CHS</sup> </span> [<a href="#B87-ijms-26-00821" class="html-bibr">87</a>]; <span class="html-italic">CmKFB</span> [<a href="#B86-ijms-26-00821" class="html-bibr">86</a>]; <span class="html-italic">OsFBK1</span> [<a href="#B77-ijms-26-00821" class="html-bibr">77</a>]; <span class="html-italic">PeKFB9</span> [<a href="#B61-ijms-26-00821" class="html-bibr">61</a>]; <span class="html-italic">SAGL1</span> [<a href="#B82-ijms-26-00821" class="html-bibr">82</a>,<a href="#B89-ijms-26-00821" class="html-bibr">89</a>]; <span class="html-italic">SKIP11</span> [<a href="#B90-ijms-26-00821" class="html-bibr">90</a>]; <span class="html-italic">SmKFB5</span> [<a href="#B84-ijms-26-00821" class="html-bibr">84</a>]; <span class="html-italic">StFBK</span> [<a href="#B60-ijms-26-00821" class="html-bibr">60</a>]; <span class="html-italic">VviKFB07</span> [<a href="#B88-ijms-26-00821" class="html-bibr">88</a>].</p>
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14 pages, 1704 KiB  
Article
Lignin Metabolism Is Crucial in the Plant Responses to Tambocerus elongatus (Shen) in Camellia sinensis L.
by Wenli Wang, Xiaogui Zhou, Qiang Hu, Qiuhong Wang, Yanjun Zhou, Jingbo Yu, Shibei Ge, Lan Zhang, Huawei Guo, Meijun Tang and Xin Li
Plants 2025, 14(2), 260; https://doi.org/10.3390/plants14020260 - 17 Jan 2025
Viewed by 442
Abstract
Tambocerus elongatus (Shen) (Hemiptera: Cicadellidae) is a devastating insect pest species of Camellia sinensis, significantly affecting the yield and quality of tea. Due to growing concerns over the irrational use of insecticides and associated food safety, it is crucial to better understand [...] Read more.
Tambocerus elongatus (Shen) (Hemiptera: Cicadellidae) is a devastating insect pest species of Camellia sinensis, significantly affecting the yield and quality of tea. Due to growing concerns over the irrational use of insecticides and associated food safety, it is crucial to better understand the innate resistance mechanism of tea trees to T. elongatus. This study aims to explore the responses of tea trees to different levels of T. elongatus infestation. We first focused on the primary metabolism and found that the amino acid levels decreased significantly with increasing T. elongatus infestation, while sugar accumulation showed an opposite trend. Moreover, secondary metabolite analysis showed a significant increase in flavonoid compounds and lignin content after T. elongatus infestation. Metabolomics analysis of the flavonoid compounds revealed a decrease in the proanthocyanidin level and an increase in anthocyanidin glycosides (anthocyanins and their derivatives) after T. elongatus infestation. T. elongatus infestation also caused a decrease in the abundance of non-ester catechins and an increase in the abundance of ester catechins. Furthermore, the gene expression analysis revealed that transcripts of genes involved in flavonoid biosynthesis, such as CsCHI, CsF3H, CsF3′H, CsFNS, CsFLS, and CsUFGT, were down-regulated, while genes involved in the lignin pathway were up-regulated by insect infestation, suggesting that lignin probably plays a pivotal role in tea plant response to T. elongatus infestation. Analysis of the expression of related genes indicates that the jasmonate (JA) pathway primarily responds to leafhopper damage. These findings suggest that the lignin pathway and JA play a preferential role in tea plant response to T. elongatus. Furthermore, the production of saccharides and the accumulation of anthocyanin glycosides in the flavonoid metabolic pathway are critical during this stress response. Further exploration of the roles of anthocyanin glycosides and lignin in tea tree resistance could provide a theoretical basis for understanding the defense mechanism of tea trees against T. elongatus damage. Full article
(This article belongs to the Special Issue Sustainable Strategies for Tea Crops Protection)
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<p><span class="html-italic">T. elongatus</span> feeding affects the abundance of major amino acids and sugars in tea leaves. Relative abundance of (<b>a</b>). Theanine; (<b>b</b>). Arginine; (<b>c</b>). Aspartic acid; (<b>d</b>). Glutamic acid; and (<b>e</b>). Major sugars in tea buds. The color gradient from red to blue indicates the abundance of sugars from high to low. CK: control, no larval infestation; MD: moderate infestation level, 5 larvae per bud; SD: severe infestation level, 10 larvae per bud. The relative abundance of various amino acids was determined based on the peak areas from the metabolomics results. The mean denoted by the different lower-case letters indicates statistically significant differences between the treatments according to Duncan’s Multiple Range Test (DMRT) at <span class="html-italic">p</span> &lt; 0.05, where ns represents non-significant and error bars indicate standard deviation.</p>
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<p>Flavonoid, total lignin, and flavonoid-targeted metabolite contents in different infestation levels of <span class="html-italic">T. elongatus</span>. (<b>a</b>). Content of total flavonoids. (<b>b</b>). Content of total lignin. (<b>c</b>). Abundance of Proanthocyanidin. (<b>d</b>). Abundance of anthocyanins and their glycosides. (<b>e</b>). Abundance of the main catechins. CK: control, no larval infestation; MD: moderate infestation level, 5 larvae per bud; SD: severe infestation level, 10 larvae per bud. The color gradient from red to blue indicates the abundance from high to low. The data denoted by the different lower-case letters indicated significant differences between the treatments (DMRT, <span class="html-italic">p</span> &lt; 0.05) and error bars indicate standard deviation. The content values are based on the dry weight of tea leaves.</p>
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<p>Expression of genes involved in the phenylpropanoid metabolic pathway. PAL, Phenylalanine ammonia-lyase; C4H, Cinnamic acid 4-hydroxylase; 4CL, 4-Coumarinyl-CoA ligase; CHS, Chalcone synthase; CHI, Chalcone isomerase; F3H, Flavanone 3-hydroxylase; F3′H, Flavonoid 3′-hydroxylase; F3′5′H, Flavonoid 3′5′-hydroxylase; FNS, Flavonoid synthase; FLS, Flavonol synthetase; UFGT, UDP-glycose flavonoid glycosyltransferase; DFR, Dihydroflavonol reductase; ANS, Anthocyanin synthase; ANR, Afsnthocyanidin reductase; LAR, Leucoanthocyanidin reductase; HCT, Hydroxycinnamoyl acyltransferase; C3H, p-Coumaric acid 3 hydroxylase; CSE, Caffeoyl shikimate esterase; COMT, Caffeic acid O-methyltransferase; F5H, Ferulic acid 5-hydroxylase; CcoAOMT, Caffeoyl-CoA-O-methyltransferase; CCR, Coumarin-CoA reductase; CAD, Cinnamyl alcohol dehydrogenase; PER, Peroxidase; LAC, Laccase. CK: control, no larval infestation; MD: moderate infestation level, 5 larvae per bud; SD: severe infestation level, 10 larvae per bud. The color gradient from red to blue indicates the relative transcript expression levels from high to low.</p>
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<p>Hormone-related gene expression. (<b>a</b>). LOX2, lipoxygenase 2; (<b>b</b>). LOXC, lipoxygenase C; (<b>c</b>). AOC, allene oxide cyclase; (<b>d</b>). OPR3, 12-oxophytodienoate reductase; (<b>e</b>). JAZ1, JAZ protein 1, a key transcriptional repressor during JA signaling; (<b>f</b>). MYC2a, the JA pathway MYC2a transcription factors; (<b>g</b>). MYC2c, the JA pathway MYC2c transcription factors; (<b>h</b>). NPR1, Nonexpressor of pathogenesis-related genes 1 in SA pathway; (<b>i</b>). ICS1, isochorismate synthase 1 in SA pathway. CK: control, no larval infestation; MD: moderate infestation level, 5 larvae per bud; SD: severe infestation level, 10 larvae per bud. The data denoted by the different lower-case letters indicated significant differences between the treatments (DMRT, <span class="html-italic">p</span> &lt; 0.05), where ns represents non-significant and error bars indicate standard deviation.</p>
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<p>The sketch illustrates the response of <span class="html-italic">Camellia sinensis</span> L. to <span class="html-italic">Tambocerus elongatus</span> infestation through secondary metabolism and defense hormone pathways. When infested by <span class="html-italic">Tambocerus elongatus</span>, tea plants activate jasmonic acid, a hormone significantly associated with pest resistance. This activation triggers the induction of flavonoids and lignans, particularly lignin, in the secondary metabolic pathway, thereby enhancing the plant’s resilience against the pest. In this model, solid lines denote pathways that play a pivotal role, and arrows indicate up-regulation.</p>
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17 pages, 3811 KiB  
Article
The Entry of Pollinating Fig Wasps Plays a Pivotal Role in the Developmental Phase and Metabolic Expression Changes in Ficus hookeriana Figs
by Ying Zhang, Yunfang Guan, Zongbo Li, Yan Wang, Changqi Chen, Xiaoyan Yang and Yuan Zhang
Forests 2025, 16(1), 165; https://doi.org/10.3390/f16010165 - 16 Jan 2025
Viewed by 494
Abstract
The fig (the syconium of the Ficus tree) and its pollinating fig wasp represent exceptional examples for researching plant–insect interactions due to their remarkable specificity in species interaction and mutually beneficial symbiotic relationship. However, the mechanisms underlying the developmental process of monoecious figs [...] Read more.
The fig (the syconium of the Ficus tree) and its pollinating fig wasp represent exceptional examples for researching plant–insect interactions due to their remarkable specificity in species interaction and mutually beneficial symbiotic relationship. However, the mechanisms underlying the developmental process of monoecious figs in response to the entry of pollinating fig wasps (pollinators) and the metabolic changes occurring during this process remain elusive. Our study employed a combination of controlled experiments in the field and LC-MS methods to investigate the impact of pollinating fig wasp entry on the developmental phase of figs, as well as the metabolic alterations occurring during this process. A total of 381 metabolites and 155 differential metabolites were identified, with the predominant classes of metabolites being organic acids, lipids, and benzene aromatic compounds. The results suggest that in the absence of wasp entry, the receptive phase of fig would exhibit an extended duration. However, upon the entry of fig wasps, the receptive phase of figs would terminate within a span of 1 to 2 days, concomitant with substantial fluctuations in the composition and proportions of metabolites within the fig. Our research focuses on the analysis of linoleic acid metabolism, phenylpropanoid biosynthesis, and flavonoid biosynthesis pathways. Our findings suggest that the entry of wasps triggers alterations in the metabolic regulatory mechanisms of figs. Prior to wasp entry, metabolites primarily regulate fig growth and development. However, after wasp entry, metabolites predominantly govern lipid accumulation and the establishment of defense mechanisms, indicating a transition in fig development. This metabolic perspective explains why figs promptly enter an interflower phase that is not attractive to pollinating fig wasps after their entry, and how figs achieve reproductive balance through the regulation of different metabolic pathways. This study provides scientific evidence for elucidating the stability mechanism of the fig wasp mutualistic system. Full article
(This article belongs to the Section Forest Ecophysiology and Biology)
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<p>Life history of monoecious fig and its pollinating fig wasps.</p>
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<p>The duration of fig receptivity following pollinator entry and in the absence of pollinators. Note: ***: <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Quality control of samples: (<b>a</b>) Total Ion Current Overlap Plot in Mass Spectrometry; (<b>b</b>) Metabolite clustering heatmap. BF: prior to figs entry by pollinators; AF: subsequent to figs entry by pollinators. Note: The relative abundance of metabolites depicted in Figure (<b>b</b>) is represented by color intensity, where red signifies increased expression and blue denotes decreased expression. Positive values indicate up-regulated metabolites, while negative values indicate down-regulated metabolites. Metabolites with similar expression patterns are clustered on the left side of the dendrogram, forming a hierarchical tree of differentially expressed metabolites.</p>
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<p>Metabolite composition analysis: (<b>a</b>) Metabolite categorization pie chart; (<b>b</b>) Metabolite grouping comparison stack; BF: prior to figs entry by pollinators; AF: subsequent to figs entry by pollinators.</p>
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<p>Volcano plot of the differential expression of the metabolites.</p>
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<p>KEGG enrichment analysis and network pathway analysis.</p>
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<p>Linoleic acid metabolism pathway. BF: prior to figs entry by pollinators; AF: subsequent to figs entry by pollinators. Note: Blue and red indicate down-regulation and up-regulation, respectively. Note: The relative abundance of metabolites depicted is represented by color intensity, where red signifies increased expression and blue denotes decreased expression. Positive values indicate up-regulated metabolites, while negative values indicate down-regulated metabolites.</p>
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<p>Phenylpropanoid biosynthesis pathway and flavonoid biosynthesis pathway; BF: prior to figs entry by pollinators; AF: subsequent to figs entry by pollinators. Note: Blue and red indicate down-regulation and up-regulation, respectively. Note: The relative abundance of metabolites depicted is represented by color intensity, where red signifies increased expression and blue denotes decreased expression. Positive values indicate up-regulated metabolites, while negative values indicate down-regulated metabolites.</p>
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18 pages, 10098 KiB  
Article
Integrated Genetic Diversity and Multi-Omics Analysis of Colour Formation in Safflower
by Yonghua Qin, Kangjun Fan, Aidiya Yimamu, Peng Zhan, Lu Lv, Gang Li, Jiao Liu, Zunhong Hu, Xingchu Yan, Xueli Hu, Hong Liu and Rui Qin
Int. J. Mol. Sci. 2025, 26(2), 647; https://doi.org/10.3390/ijms26020647 - 14 Jan 2025
Viewed by 424
Abstract
Safflower (Carthamus tinctorius L.) is a medicinal and edible cash crop that is widely cultivated worldwide. However, the genetic diversity of safflower germplasm resources and the reasons for the variations in safflower flower colour remain unclear. In this study, we used a [...] Read more.
Safflower (Carthamus tinctorius L.) is a medicinal and edible cash crop that is widely cultivated worldwide. However, the genetic diversity of safflower germplasm resources and the reasons for the variations in safflower flower colour remain unclear. In this study, we used a combination of agronomic traits and Indel markers to assess the genetic diversity of 614 safflower germplasm resources. The results showed that most of the evaluated agronomic traits had high variability. The mean values of the Shannon’s information index (I) and polymorphism information content (PIC) in 50 pairs of Indel markers were 0.551 and 0.296, respectively. The population structure, neighbour-joining phylogeny, and principal coordinate analyses classified all genotypes into four subgroups, and 214 safflower core germplasms were constructed. Multiple analyses of genetic diversity parameters, range conformity, and the percentage of variance difference showed that the core germplasm did not differ significantly and could represent the original germplasm better. Transcriptome and metabolome analyses revealed that flavonoid synthesis-related genes, including CHS, F3H, ANS, and BZ1, were differentially expressed in different coloured safflowers. Most significantly, different genes and metabolite compounds in white safflowers were enriched upstream from the phenylpropanoid metabolic pathway to the production of naringenin, whereas those in red safflowers were concentrated in the downstream pathway from eriodictyol. Meanwhile, the preliminary quantification of anthocyanins and carotenoids extracted from red, orange, and white types of safflower showed that the level of both anthocyanins and carotenoids were highest in red types. This work provides new insights into the formation of different safflower flower colours and in the conservation and management of safflower germplasm. Full article
(This article belongs to the Special Issue Transcriptional Regulation in Plant Development: 2nd Edition)
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<p>Agronomic traits analysis of 614 safflower germplasm. (<b>A</b>): Correlation analysis of 11 agronomic traits. (<b>B</b>): Principal component analysis of 11 agronomic traits.</p>
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<p>Population analysis of 614 safflower germplasm. (<b>A</b>): Cluster analysis based on neighbour-joining (NJ) method. (<b>B</b>): Score plot generated using PCoA.</p>
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<p>qPCR validation of phenotypic and Indel marker association analysis loci. (<b>A</b>): Heatmap of expression of five loci in red and white flower safflower at four periods. (<b>B</b>–<b>F</b>): qPCR validation of five loci, respectively. The error bars represent S.E.M. Statistically significant differences were tested by One-way ANOVA. All the above experiments were repeated three times independently.</p>
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<p>Metabolome mapping of three different flower colours. (<b>A</b>): The K means analysis of metabolites identified in three different colour groups. (<b>B</b>): Heat map of expressions levels for the Cluster1, 3, and 6 of DAMs.</p>
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<p>Transcriptome group mapping of three different flower colours. (<b>A</b>): Venn plots of three compared combinations with R vs. W, R vs. Y, and W vs. Y. (<b>B</b>): Expression profiles heatmap of flavonoid pathway structural genes in different colours of safflowers. (<b>C</b>): GO enrichment histogram of R group compared with W group. (<b>D</b>): GO enrichment histogram of R group compared with Y group. (<b>E</b>): GO enrichment histogram of W group compared with Y group.</p>
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<p>Schematic representation of structural gene expression levels and metabolite content in the biosynthetic pathway of flavonoids in different coloured safflower.</p>
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<p>Two pigments were extracted from the different coloured flowers of safflower. (<b>A</b>,<b>B</b>): Three types of floral anthocyanin extracts and contents. (<b>C</b>,<b>D</b>): Three types of floral carotenoids extracts and contents. The error bars represent S.E.M. Statistically significant differences were tested by One-way ANOVA. All the above experiments were repeated three times independently.</p>
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27 pages, 5467 KiB  
Article
GWAS Identifies SNP Markers and Candidate Genes for Off-Flavours and Protein Content in Faba Bean (Vicia faba L.)
by Antonio Lippolis, Boudewijn Hollebrands, Valentina Acierno, Catrienus de Jong, Laurice Pouvreau, João Paulo, Salvador A. Gezan and Luisa M. Trindade
Plants 2025, 14(2), 193; https://doi.org/10.3390/plants14020193 - 11 Jan 2025
Viewed by 770
Abstract
Faba bean (Vicia faba L.) is a valuable ingredient in plant-based foods such as meat and dairy analogues. However, its typical taste and aroma are considered off-flavours in these food applications, representing a bottleneck during processing. Breeding is needed to develop varieties [...] Read more.
Faba bean (Vicia faba L.) is a valuable ingredient in plant-based foods such as meat and dairy analogues. However, its typical taste and aroma are considered off-flavours in these food applications, representing a bottleneck during processing. Breeding is needed to develop varieties with minimal off-flavours and high protein content. The genetic regulation of these traits is underexplored. To dissect their genetic architecture, we performed a genome-wide association study (GWAS). A total of 245 faba bean accessions (the CGN population) were genotyped using the 90K-SPET targeted assay. These accessions were phenotyped in 2021 and 2022 in the Netherlands for protein, oil, fatty acids, lipid-derived products, phenolic acids, flavonoids, and tannins. The CGN population showed large phenotypic variation and moderate-to-high narrow-sense heritability for most traits. The growing environment significantly affected all traits, with trait-specific genotype-by-year (GxY) interactions. Condensed tannins and fatty acids were the most stable across the two years and had the highest heritability estimates (h2 > 0.6). GWAS identified a total of 148 single nucleotide polymorphisms (SNPs) loci in 2021 and 167 in 2022. Key candidate regulators included genes involved in lipid biosynthesis (ATS2, KAS, LPP), amino acid transport (CAT4) for protein storage, zero tannins locus-1 (zt-1), and regulators of the phenylpropanoid pathway, such as a shikimate kinase gene and transcription factors bHLH137-like and MYB. These results pave the way for validation studies and biotechnological applications to improve the quality of faba bean-based foods. Full article
(This article belongs to the Section Plant Genetics, Genomics and Biotechnology)
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<p>SNP coverage along the six chromosomes of faba bean (<span class="html-italic">Vicia faba</span> L.). Chromosome 6 had the largest gap among adjacent SNPs, followed by chromosome 5 and 4. The specific chromosome length represents the regions covered by the markers.</p>
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<p>Correlation network based on type-A additive genetic correlations (ρ<sub>type-A</sub>) in 2021 (<b>left</b>) and 2022 (<b>right</b>). Only absolute ρ<sub>type-A</sub> &gt; 0.2 are displayed. Each node (black circle) represents a different trait, and each edge (line) represents the absolute genetic correlation among traits. As the legend indicates, thick red lines represent strong correlations, while thin blue lines represent weaker correlations. The labels’ background colour groups molecules according to their chemical classes.</p>
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<p>Number of SNPs detected by GWAS per trait in 2021 (<b>top</b>) and 2022 (<b>bottom</b>) based on LOD ≥ 4.5 and the Bonferroni correction threshold. Grey labels on the x-axis indicate no data available.</p>
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<p>Manhattan plots for protein content and off-flavours (2021). The black dashed line represents the LOD threshold of 4.5, and the red dashed line indicates the Bonferroni threshold. Each dots represent a SNP. SNP locations on chromosomes are indicated on the x-axis.</p>
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<p>Manhattan plots for protein content and off-flavours (2022). The black dashed line represents the LOD threshold of 4.5, and the red dashed line indicates the Bonferroni threshold. Each dots represent a SNP. SNP locations on chromosomes are indicated on the x-axis.</p>
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<p>Manhattan plots for tannins based on the GWAS performed in 2021. The black dashed line represents the LOD threshold set at 4.5, while the red dashed lines represent the Bonferroni-corrected threshold. Significant signals on chromosome 2 spanned positions 671,378,841 bp to 953,872,991 bp. The x-axis labels “chr1L” and “chr1S” indicate the official division of the very large chromosome 1, as defined by the faba bean genome consortium.</p>
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<p>Faba bean plants were grown for two years in field trials in the Netherlands. Two datasets were generated: phenotypic (chemical) and genotypic (SNP) data. Phenotypic data: a subset of samples was chemically analysed (training set), while the remaining samples (field plots) were predicted using Near-Infrared Spectroscopy (NIRS). Both phenotypic and genotypic data were used to perform a genome-wide association study (GWAS).</p>
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24 pages, 3197 KiB  
Article
Integrated Physiological, Transcriptomic and Metabolomic Analyses of the Response of Rice to Aniline Toxicity
by Jingjing Wang, Ruixin Wang, Lei Liu, Wenrui Zhang, Zhonghuan Yin, Rui Guo, Dan Wang and Changhong Guo
Int. J. Mol. Sci. 2025, 26(2), 582; https://doi.org/10.3390/ijms26020582 - 11 Jan 2025
Viewed by 428
Abstract
The accumulation of aniline in the natural environment poses a potential threat to crops, and thus, investigating the effects of aniline on plants holds practical implications for agricultural engineering and its affiliated industries. This study combined physiological, transcriptomic, and metabolomic methods to investigate [...] Read more.
The accumulation of aniline in the natural environment poses a potential threat to crops, and thus, investigating the effects of aniline on plants holds practical implications for agricultural engineering and its affiliated industries. This study combined physiological, transcriptomic, and metabolomic methods to investigate the growth status and molecular-level response mechanisms of rice under stress from varying concentrations of aniline. At a concentration of 1 mg/L, aniline exhibited a slight growth-promoting effect on rice. However, higher concentrations of aniline significantly inhibited rice growth and even caused notable damage to the rice seedlings. Physiological data indicated that under aniline stress, the membrane of rice underwent oxidative damage. Furthermore, when the concentration of aniline was excessively high, the cells suffered severe damage, resulting in the inhibition of antioxidant enzyme synthesis and activity. Transcriptomic and metabolomic analyses indicated that the phenylpropanoid biosynthesis pathway became quite active under aniline stress, with alterations in various enzymes and metabolites related to lignin synthesis. In addition to the phenylpropanoid biosynthesis pathway, amino acid metabolism, lipid metabolism, and purine metabolism were also critical pathways related to rice’s response to aniline stress. Significant changes occurred in the expression levels of multiple genes (e.g., PRX, C4H, GST, and ilvH, among others) associated with functions such as antioxidant activity, membrane remodeling, signal transduction, and nitrogen supply. Similarly, notable alterations were observed in the accumulation of various metabolites (for instance, glutamic acid, phosphatidic acid, phosphatidylglycerol, and asparagine, etc.) related to these functions. Our research findings have unveiled the potential of compounds such as phenylpropanoids and amino acids in assisting rice to cope with aniline stress. A more in-depth and detailed exploration of the specific mechanisms by which these substances function in the process of plant resistance to aniline stress (for instance, utilizing carbon-14 isotope tracing to monitor the metabolic pathway of aniline within plants) will facilitate the cultivation of plant varieties that are resistant to aniline. This will undoubtedly benefit activities such as ensuring food production and quality in aniline-contaminated environments, as well as utilizing plants for the remediation of aniline-polluted environments. Full article
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<p>The effect of different concentrations of aniline on (<b>a</b>) the growth status of rice seedlings (the white bar on the left represents 5 cm), (<b>b</b>) the length of root and shoot, (<b>c</b>) the weight of root and shoot, and the effect on the content of (<b>d</b>) MDA, (<b>e</b>) H<sub>2</sub>O<sub>2</sub>, (<b>f</b>) soluble protein, (<b>g</b>) soluble sugar, and the activity of (<b>h</b>) SOD, (<b>i</b>) POD, and (<b>j</b>) CAT in root and shoot. ANOVA was utilized to compare the statistical differences among different groups, and lowercase letters indicates significant differences.</p>
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<p>Up-regulated (red solid circles, denoted as “up”), down-regulated (blue solid circles, denoted as “down”) and non-significant (gray solid circles, denoted as “nosig”) genes identified in the (<b>a</b>) AN1 and (<b>b</b>) AN40 groups, respectively, compared to the AN0 group.</p>
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<p>The top 20 terms with the highest enrichment significance in the (<b>a</b>) AN1 and (<b>b</b>) AN40 groups.</p>
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<p>The top 20 pathways ranked by enrichment significance levels in the (<b>a</b>) AN1 and (<b>b</b>) AN40 groups.</p>
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<p>Results of qRT-PCR verification. From (<b>a</b>–<b>p</b>): comparison between the relative expression measured by qRT-PCR (green bars corresponding to the left Y-axis) and the expression levels obtained by RNA-seq (red symbolled lines corresponding to the right Y-axis) of the randomly selected 16 DEGs. The asterisks “*” and “**” indicate <span class="html-italic">p</span>-values less than 0.05 and 0.01, respectively. (<b>q</b>): fitting and correlation between the qRT-PCR and RNA-seq results.</p>
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<p>(<b>a</b>) PCA and OPLS-DA for (<b>b</b>) AN1 vs. AN0 and (<b>c</b>) AN40 vs. AN0 of the metabolomic data.</p>
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<p>(<b>a</b>) Number of up-regulated and down-regulated DAMs identified in the AN1 and AN40 groups, respectively. (<b>b</b>) Venn diagram of DAM sets corresponding to the AN1 and AN40 groups.</p>
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<p>The top 20 DAMs with the highest VIP scores in the AN1 group and their relative expression levels in each sample.</p>
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<p>The top 20 DAMs with the highest VIP scores in the AN40 group and their relative expression levels in each sample.</p>
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<p>The top 20 pathways with the highest enrichment significance in the AN1 group.</p>
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<p>The top 20 pathways with the highest enrichment significance in the AN40 group.</p>
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<p>The DAM pathway network of rice under aniline stress. The gray tiles without numerical labels indicate no significant difference.</p>
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<p>The DEGs and DAMs identified in the phenylpropanoid biosynthesis pathway and their relative expression levels.</p>
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22 pages, 9142 KiB  
Article
Ethyl Acetate Extract of Cichorium glandulosum Activates the P21/Nrf2/HO-1 Pathway to Alleviate Oxidative Stress in a Mouse Model of Alcoholic Liver Disease
by Shuwen Qi, Chunzi Zhang, Junlin Yan, Xiaoyan Ma, Yewei Zhong, Wenhui Hou, Juan Zhang, Tuxia Pang and Xiaoli Ma
Metabolites 2025, 15(1), 41; https://doi.org/10.3390/metabo15010041 - 10 Jan 2025
Viewed by 572
Abstract
Background: Alcoholic liver disease (ALD) is a significant global health concern, primarily resulting from chronic alcohol consumption, with oxidative stress as a key driver. The ethyl acetate extract of Cichorium glandulosum (CGE) exhibits antioxidant and hepatoprotective properties, but its detailed mechanism of action [...] Read more.
Background: Alcoholic liver disease (ALD) is a significant global health concern, primarily resulting from chronic alcohol consumption, with oxidative stress as a key driver. The ethyl acetate extract of Cichorium glandulosum (CGE) exhibits antioxidant and hepatoprotective properties, but its detailed mechanism of action against ALD remains unclear. This study investigates the effects and mechanisms of CGE in alleviating alcohol-induced oxidative stress and liver injury. Methods: Ultra-Performance Liquid Chromatography coupled with Quadrupole-Orbitrap Mass Spectrometry (UPLC-Q-Orbitrap-MS) was used to identify CGE components. A C57BL/6J mouse model of ALD was established via daily oral ethanol (56%) for six weeks, with CGE treatment at low (100 mg/kg) and high doses (200 mg/kg). Silibinin (100 mg/kg) served as a positive control. Liver function markers, oxidative stress indicators, and inflammatory markers were assessed. Transcriptomic and network pharmacology analyses identified key genes and pathways, validated by reverse transcription quantitative polymerase chain reaction (RT-qPCR) and Western blotting. Results: UPLC-Q-Orbitrap-MS identified 81 CGE compounds, mainly including terpenoids, flavonoids, and phenylpropanoids. CGE significantly ameliorated liver injury by reducing alanine aminotransferase (ALT), aspartate aminotransferase (AST), and alkaline phosphatase (ALP) levels and enhancing antioxidative markers such as total antioxidant capacity (T-AOC) and total superoxide dismutase (T-SOD) while lowering hepatic malondialdehyde (MDA) levels. Inflammation was mitigated through reduced levels of Tumor Necrosis Factor Alpha (TNF-α), Interleukin-1 Beta (IL-1β), and C-X-C Motif Chemokine Ligand 10 (CXCL-10). Transcriptomic and network pharmacology analysis revealed seven key antioxidant-related genes, including HMOX1, RSAD2, BCL6, CDKN1A, THBD, SLC2A4, and TGFβ3, validated by RT-qPCR. CGE activated the P21/Nuclear Factor Erythroid 2-Related Factor 2 (Nrf2)/Heme Oxygenase-1 (HO-1) signaling axis, increasing P21, Nrf2, and HO-1 protein levels while suppressing Kelch-like ECH-associated Protein 1 (Keap1) expression. Conclusions: CGE mitigates oxidative stress and liver injury by activating the P21/Nrf2/HO-1 pathway and regulating antioxidant genes. Its hepatoprotective effects and multi-target mechanisms highlight CGE’s potential as a promising therapeutic candidate for ALD treatment. Full article
(This article belongs to the Special Issue Plants and Plant-Based Foods for Metabolic Disease Prevention)
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<p>Total ion chromatogram (TIC).</p>
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<p>Proposed fragmentation pathways of representative compounds. (<b>A</b>) Secondary mass spectra of lactucin and its proposed fragmentation pathways. (<b>B</b>) Secondary mass spectra of isoquercitrin and its proposed fragmentation pathways. (<b>C</b>) Secondary mass spectra of fraxin and its proposed fragmentation pathways.</p>
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<p>CGE alleviates alcohol-induced liver injury and enhances antioxidative stress levels in mice. (<b>A</b>). Animal experiment design. (<b>B</b>). The effects of CGE on the body weight of ALD mice. (<b>C</b>). The effects of CGE on the liver index of ALD mice. (<b>D</b>). Liver tissue H&amp;E staining (200×, scale bar = 100 μm). (<b>E</b>). The effects of CGE on serum AST levels in ALD mice. (<b>F</b>). The effects of CGE on serum ALT levels in ALD mice. (<b>G</b>). The effects of CGE on serum ALP levels in ALD mice. (<b>H</b>). The effects of CGE on hepatic TNF-α levels in ALD mice. (<b>I</b>). The effects of CGE on hepatic IL-1β levels in ALD mice. (<b>J</b>). The effects of CGE on hepatic CXCL-10 levels in ALD mice. (<b>K</b>). The effects of CGE on serum T-AOC levels in ALD mice. (<b>L</b>). The effects of CGE on serum T-SOD levels in ALD mice. (<b>M</b>). The effects of CGE on hepatic MDA levels in ALD mice. Data are presented as mean ± SD (n = 6). Relative to the control group, <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01, and <sup>###</sup> <span class="html-italic">p</span> &lt; 0.001; relative to the model group, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Differential gene screening, enrichment analysis, and identification of key antioxidative genes from transcriptomics. (<b>A</b>). Volcano plot for Con vs. Mod. (<b>B</b>). Volcano plot for Mod vs. CGE-H. (<b>C</b>). Number of markedly upregulated and downregulated genes within the Con vs. Mod and Mod vs. CGE-H groups. (<b>D</b>). Venn diagram of DEGs from Con vs. Mod, DEGs from Mod vs. CGE-H, and oxidative stress-related genes. (<b>E</b>). Bubble chart of GO analysis for CGE’s potential antioxidative stress-related genes. (<b>F</b>). Sankey diagram of KEGG pathways for CGE’s potential antioxidative stress genes. (<b>G</b>). PPI network of 130 genes associated with antioxidative stress under CGE regulation. (<b>H</b>). Identification of the top 20 key genes using the Degree algorithm in the cytoHubba plugin. Lines between circles represent interactions between genes, and circle colors ranging from red to yellow indicate interaction strength from high to low. (<b>I</b>). Using the GeneMANIA database, four genes interacting with <span class="html-italic">NFE2L2</span> were identified from the 20 key genes.</p>
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<p>Mechanisms of CGE against ALD-induced oxidative damage identified via network pharmacology analysis. (<b>A</b>). Venn diagram of oxidative stress-related genes, CGE differential genes, and ALD genes, identifying potential gene targets of CGE against ALD-induced oxidative stress. (<b>B</b>). CGE compound–target network diagram. (<b>C</b>). GO analysis for potential genes of CGE against ALD-induced oxidative stress. (<b>D</b>). KEGG analysis for potential genes of CGE against ALD-induced oxidative stress.</p>
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<p>Integrated analysis of transcriptomics, network pharmacology, and key antioxidative genes. (<b>A</b>). KEGG Sankey diagram of the top 20 key genes identified using the Degree algorithm in the cytoHubba plugin. (<b>B</b>). Intersection pathways of network pharmacology, transcriptomics, and antioxidative core genes.</p>
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<p>The validation of key antioxidative stress genes regulated by CGE. (<b>A</b>–<b>G</b>). Relative mRNA expression levels of <span class="html-italic">HMOX1</span>, <span class="html-italic">CDKN1A</span>, <span class="html-italic">THBD</span>, <span class="html-italic">RSAD</span>, <span class="html-italic">SLC2A4</span>, <span class="html-italic">BCL6</span>, <span class="html-italic">TGFβ3</span>. The results are presented as mean ± SD (n = 6). Relative to the control group, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01, <sup>###</sup> <span class="html-italic">p</span> &lt; 0.001; relative to the model group, * <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>The process through which CGE mitigates oxidative stress in ALD mice involves the activation of the P21/Nrf2/HO-1 signaling pathway. (<b>A</b>). The analysis of protein expression levels and quantification for Nrf2, Keap1, HMOX1, and P21 in liver tissue. The results are presented as mean ± SD (n = 6). Statistical significance is indicated as follows: <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01, and <sup>###</sup> <span class="html-italic">p</span> &lt; 0.001 compared to the control group; ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001 compared to the model group. (<b>B</b>). CGE activates the P21/Nrf2/HO-1 pathway to reduce oxidative stress in ALD mice.</p>
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