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14 pages, 1681 KiB  
Article
Changes in Endogenous Carotenoids, Flavonoids, and Phenolics of Drought-Stressed Broccoli Seedlings After Ascorbic Acid Preconditioning
by Linqi Cai, Lord Abbey and Mason MacDonald
Plants 2024, 13(24), 3513; https://doi.org/10.3390/plants13243513 (registering DOI) - 16 Dec 2024
Abstract
Drought is an abiotic disturbance that reduces photosynthesis, plant growth, and crop yield. Ascorbic acid (AsA) was utilized as a seed preconditioning agent to assist broccoli (Brassica oleracea var. italica) in resisting drought. However, the precise mechanism by which AsA improves [...] Read more.
Drought is an abiotic disturbance that reduces photosynthesis, plant growth, and crop yield. Ascorbic acid (AsA) was utilized as a seed preconditioning agent to assist broccoli (Brassica oleracea var. italica) in resisting drought. However, the precise mechanism by which AsA improves seedlings’ development remains unknown. One hypothesis is that AsA works via antioxidant mechanisms and reduces oxidative stress. This study aims to confirm the effect of varied concentrations of AsA (control, 0 ppm, 1 ppm, or 10 ppm) on seedling growth and changes in the antioxidant status of broccoli seedlings under regular watering or drought stress. AsA increased shoot dry mass, leaf area, net photosynthesis, and water use efficiency in watered and drought-stressed seedlings. AsA significantly (p < 0.001) increased carotenoid content in watered and drought-stressed seedlings by approximately 27% and 111%, respectively. Drought increased chlorophyll b, flavonoids, phenolics, ascorbate, and hydrogen peroxide production in control seedlings, but either had no effect or less effect on plants preconditioned with 10 ppm AsA. There was no improvement in reactive oxygen species scavenging in AsA-preconditioned seedlings compared to the control. The absence or reduction in biochemical indicators of stress suggests that preconditioned broccoli seedlings do not perceive stress the same as control seedlings. In conclusion, the consistent increase in carotenoid concentration suggests that carotenoids play some role in the preconditioning response, though the exact mechanism remains unknown. Full article
(This article belongs to the Special Issue Vegetable and Fruit Production, 2nd Edition)
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Figure 1

Figure 1
<p>(<b>a</b>) Shoot dry biomass of broccoli seedlings; (<b>b</b>) leaf area of broccoli seedlings. Each figure compares four seed preconditioning treatments in both watered and drought conditions. Bars represent an average of 8 replicates for each treatment combination. Bars with different letters are significantly different based on Tukey’s multiple means comparison at 5% significance.</p>
Full article ">Figure 2
<p>(<b>a</b>) Net photosynthesis (Pn); (<b>b</b>) evapotranspiration (E); (<b>c</b>) stomatal conductance (Gs); (<b>d</b>) water use efficiency (WUE) of broccoli seedlings. Each figure compares four seed preconditioning treatments in both watered and drought conditions. Bars represent an average of 8 replicates for each treatment combination. Bars with different letters are significantly different based on Tukey’s multiple means comparison at 5% significance.</p>
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<p>(<b>a</b>) Hydrogen peroxide production and (<b>b</b>) ROS scavenging in broccoli seedlings in 4 seed preconditioning treatments in broccoli seedlings that were watered or exposed to drought. Bars represent an average of 8 replicates for each treatment combination. Bars with different letters are significantly different based on Tukey’s multiple means comparison at 5% significance.</p>
Full article ">Figure 4
<p>(<b>a</b>) Principal component score plot, with points identified by treatment. Shaded areas indicate clusters of points associated with drought or watered conditions; (<b>b</b>) principal component loading plot. Pn = net photosynthesis, E = transpiration, Gs = stomatal conductance, WUE = water use efficiency, Chl a and b = chlorophyll a and b, and ROS = reactive oxygen species.</p>
Full article ">
27 pages, 22127 KiB  
Article
Combined Physiological and Transcriptomic Analyses of the Effects of Exogenous Trehalose on Salt Tolerance in Maize (Zea mays L.)
by Jingyi He and Hongliang Tang
Plants 2024, 13(24), 3506; https://doi.org/10.3390/plants13243506 (registering DOI) - 16 Dec 2024
Viewed by 150
Abstract
Soil salinization severely affects the quality and yield of maize. As a C4 plant with high efficiency in utilizing light and carbon dioxide, maize (Zea mays L.) is one of the most important crops worldwide. This study aims to investigate the pathways [...] Read more.
Soil salinization severely affects the quality and yield of maize. As a C4 plant with high efficiency in utilizing light and carbon dioxide, maize (Zea mays L.) is one of the most important crops worldwide. This study aims to investigate the pathways and mechanisms by which trehalose mediates the improvement of salt tolerance in maize through a combined analysis of physiology and transcriptomics. The results indicate that foliar application of trehalose treatment significantly increased maize biomass and antioxidant enzyme activity while reducing the H2O2 and Na+/K+ ratios in both the aerial and underground parts of the plant. Additionally, trehalose enhanced the total secretion of organic acids from maize roots, improving the soil microenvironment for maize growth under salt stress and alleviating Na+ toxicity. Transcriptomic data revealed that under salt stress, most differentially expressed genes (DEGs) were enriched in pathways related to photosynthesis, abscisic acid signaling, and sugar metabolism, and trehalose application increased the expression levels of these pathways, thereby mitigating the growth inhibition caused by salinity. This study elucidates mechanisms for enhancing salt tolerance in maize, providing theoretical support for improving its resilience and offering innovative strategies for utilizing a wide range of saline-alkali land. Full article
(This article belongs to the Section Plant Physiology and Metabolism)
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Figure 1
<p>Growth parameters of maize plants at different sampling time points in the hydroponic experiment under salt stress and trehalose treatment. Photos were taken, and samples were collected on the 15th (0d), 19th (S1d), 21st (S3d), and 25th (S7d), days of maize growth to measure various growth parameters: maize seedling phenotype (<b>A</b>), fresh weight (<b>B</b>), root system phenotype (<b>C</b>), dry weight (<b>D</b>), plant height (<b>E</b>), chlorophyll content, relative water content, and daily dynamic changes in the aerial parts (<b>F</b>), and stem diameter (<b>G</b>). Note: date showing the means ± standard deviation (SD). ns: no significant difference. Asterisks: using the <span class="html-italic">t</span>-test, indicating the significance levels of differences between CK (the control) and S (the salt stress), and 10T (the application of trehalose under normal nutritional) and 10TS (the application of trehalose under salt stress): *: a highly significant difference at * <span class="html-italic">p</span> &lt; 0.05, ** at <span class="html-italic">p</span> &lt; 0.01, *** at <span class="html-italic">p</span> &lt; 0.001, and **** at <span class="html-italic">p</span> &lt; 0.0001. Lowercase letters: using the one-way ANOVA (Turkey, Duncan), indicating significant differences (<span class="html-italic">p</span> &lt; 0.05) between groups under salt stress and trehalose treatment (<span class="html-italic">n</span> = 3).</p>
Full article ">Figure 1 Cont.
<p>Growth parameters of maize plants at different sampling time points in the hydroponic experiment under salt stress and trehalose treatment. Photos were taken, and samples were collected on the 15th (0d), 19th (S1d), 21st (S3d), and 25th (S7d), days of maize growth to measure various growth parameters: maize seedling phenotype (<b>A</b>), fresh weight (<b>B</b>), root system phenotype (<b>C</b>), dry weight (<b>D</b>), plant height (<b>E</b>), chlorophyll content, relative water content, and daily dynamic changes in the aerial parts (<b>F</b>), and stem diameter (<b>G</b>). Note: date showing the means ± standard deviation (SD). ns: no significant difference. Asterisks: using the <span class="html-italic">t</span>-test, indicating the significance levels of differences between CK (the control) and S (the salt stress), and 10T (the application of trehalose under normal nutritional) and 10TS (the application of trehalose under salt stress): *: a highly significant difference at * <span class="html-italic">p</span> &lt; 0.05, ** at <span class="html-italic">p</span> &lt; 0.01, *** at <span class="html-italic">p</span> &lt; 0.001, and **** at <span class="html-italic">p</span> &lt; 0.0001. Lowercase letters: using the one-way ANOVA (Turkey, Duncan), indicating significant differences (<span class="html-italic">p</span> &lt; 0.05) between groups under salt stress and trehalose treatment (<span class="html-italic">n</span> = 3).</p>
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<p>Contents of osmotic substances in maize at different sampling time points in the hydroponic experiment under salt stress and trehalose treatment. The levels of proline (<b>A</b>), soluble sugar (<b>B</b>), and soluble protein (<b>C</b>) were measured at each time point. Note: date showing the means ± standard deviation (SD). ns: no significant difference. Asterisks: using the <span class="html-italic">t</span>-test, indicating the significance levels of differences between CK (the control) and S (the salt stress), or 10T (the application of trehalose under normal nutritional) and 10TS (the application of trehalose under salt stress): *: a highly significant difference at * <span class="html-italic">p</span> &lt; 0.05, ** at <span class="html-italic">p</span> &lt; 0.01, *** at <span class="html-italic">p</span> &lt; 0.001, and **** at <span class="html-italic">p</span> &lt; 0.0001. Lowercase letters: using the one-way ANOVA (Turkey, Duncan), indicating significant differences (<span class="html-italic">p</span> &lt; 0.05) between groups under salt stress and trehalose treatment (<span class="html-italic">n</span> = 3).</p>
Full article ">Figure 3
<p>The hydrogen peroxide staining phenotype of maize leaves (<b>A</b>), hydrogen peroxide content (<b>B</b>), daily variation of hydrogen peroxide content (<b>C</b>), shoot MDA (<b>D</b>), root MDA (<b>E</b>), SOD activity (<b>F</b>), POD activity (<b>G</b>), CAT activity (<b>H</b>), and APX activity (<b>I</b>) were measured. Note: date showing the means ± standard deviation (SD). ns: no significant difference. Asterisks: using the <span class="html-italic">t</span>-test, indicating the significance levels of differences between CK (the control) and S (the salt stress), or 10T (the application of trehalose under normal nutritional) and 10TS (the application of trehalose under salt stress): *: a highly significant difference at * <span class="html-italic">p</span> &lt; 0.05, ** at <span class="html-italic">p</span> &lt; 0.01, *** at <span class="html-italic">p</span> &lt; 0.001, and **** at <span class="html-italic">p</span> &lt; 0.0001. Lowercase letters: significant differences (<span class="html-italic">p</span> &lt; 0.05, one-way ANOVA) between groups under salt stress and trehalose treatment (<span class="html-italic">n</span> = 3).</p>
Full article ">Figure 4
<p>Transport of K<sup>+</sup> and Na<sup>+</sup> in maize at different sampling time points in the hydroponic experiment under salt stress and trehalose treatment. The Na<sup>+</sup> content (<b>A</b>), K<sup>+</sup> content (<b>B</b>), and the changes in Na<sup>+</sup>/K<sup>+</sup> ratio (<b>C</b>) were measured. Note: date showing the means ± standard deviation (SD). ns: no significant difference. Asterisks: using the <span class="html-italic">t</span>-test, indicating the significance levels of differences between CK (the control) and S (the salt stress), or 10T (the application of trehalose under normal nutritional) and 10TS (the application of trehalose under salt stress): *: a highly significant difference at * <span class="html-italic">p</span> &lt; 0.05, ** at <span class="html-italic">p</span> &lt; 0.01, *** at <span class="html-italic">p</span> &lt; 0.001, and **** at <span class="html-italic">p</span> &lt; 0.0001. Lowercase letters: using the one-way ANOVA (Turkey, Duncan), indicating significant differences (<span class="html-italic">p</span> &lt; 0.05) between groups under salt stress and trehalose treatment (<span class="html-italic">n</span> = 3).</p>
Full article ">Figure 5
<p>Maize phenotypes in the soil experiment under salt stress and trehalose treatment. Phenotypic photos were recorded during the seedling stage and flowering stage. Measurements included plant height (<b>A</b>), chlorophyll content in the aerial parts (<b>B</b>), relative water content (<b>C</b>), leaf relative conductivity (<b>D</b>), seedling and flowering stage phenotypes (<b>E</b>), fresh weight (<b>F</b>), root system phenotype (<b>G</b>), dry weight (<b>H</b>), root length (<b>I</b>), root surface area (<b>J</b>), average root diameter (<b>K</b>), specific root weight (<b>L</b>), root volume (<b>M</b>), and rhizosphere soil pH (<b>N</b>). Note: date showing the means ± standard deviation (SD). ns: no significant difference. Lowercase letters: using the one-way ANOVA (Turkey, Duncan), indicating significant differences (<span class="html-italic">p</span> &lt; 0.05) between CK (the control), S (the salt stress), 10T (the application of trehalose under normal nutritional) and 10TS (the application of trehalose under salt stress) (<span class="html-italic">n</span> = 6).</p>
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<p>Organic acid content secreted by maize roots in the soil experiment under salt stress and trehalose treatment. The contents of fumaric acid (<b>A</b>), aconitic acid (<b>B</b>), lactic acid (<b>C</b>), citric acid (<b>D</b>), maleic acid (<b>E</b>), malic acid (<b>F</b>), oxalic acid (<b>G</b>), total organic acid content secreted by roots under normal conditions (<b>H</b>), and total organic acid content secreted by roots under salt stress (<b>I</b>) were measured. The data were presented as means ± standard deviation (SD) from three replicates. ns: no significant difference. In panels (<b>A</b>–<b>G</b>), different letters indicate significant differences (*: highly significant difference at * <span class="html-italic">p</span> &lt; 0.05, ** at <span class="html-italic">p</span> &lt; 0.01, and *** at <span class="html-italic">p</span> &lt; 0.001) between the four treatments using the <span class="html-italic">t</span>-test. Different asterisks indicate the significance levels of differences between CK (the control) and S (salt stress), or T (the application of trehalose under normal conditions) and TS (the application of trehalose under salt stress) using the one-way ANOVA (Turkey, Duncan). In panels (<b>H</b>,<b>I</b>), the contents of various organic acids are marked within the bar chart, and asterisks indicate the significance levels of differences between CK and T, and between S and TS (<span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">n</span> = 3).</p>
Full article ">Figure 7
<p>Volcano plot of differentially expressed genes (the <span class="html-italic">x</span>-axis represents the fold change in the expression levels of differentially expressed genes, and the <span class="html-italic">y</span>-axis represents the logarithm of the <span class="html-italic">p</span>-value from the differential expression analysis. The number of upregulated and downregulated genes between treatment groups and experimental groups in pairwise comparisons (<b>A</b>–<b>F</b>) (CK: the control, S: salt stress, T: the application of trehalose under normal conditions, TS: the ap-plication of trehalose under salt stress.) Red points indicate upregulated genes, blue points indicate downregulated genes, and gray points indicate genes with no significant differential expression) (<span class="html-italic">Q</span> value &lt; 0.05, <span class="html-italic">n</span> = 3).</p>
Full article ">Figure 8
<p>Transcriptional differences in photosynthesis in maize leaves under salt stress and trehalose treatment. The colored blocks represent log2 TPM values (CK: the control, S: salt stress, T: the application of trehalose under normal conditions, TS: the application of trehalose under salt stress). Red and blue indicate significantly upregulated and downregulated genes, respectively (log2 |TPM| ≥ 1, <span class="html-italic">Q</span> value &lt; 0.05, <span class="html-italic">n</span> = 3). For enzyme reactions, the direction of arrows indicates the order of signal transduction. In the dark reaction, transcription proteins and transcription factors are represented by orange boxes, metabolites by orange squares, and key metabolites by a darker orange. PPC: phosphoenolpyruvate carboxylase; ppdk: pyruvate, orthophosphate dikinase; PCKA: phosphoenolpyruvate carboxykinase; MDH1: malate dehydrogenase; mae B: malate dehydrogenase; pgk: phosphoglycerate kinase; GAPA: glyceraldehyde 3-phosphate dehydrogenase; GAPB: glyceraldehyde-3-phosphate dehydrogenase (NAD(P)<sup>+</sup>); GAPDH: glyceraldehyde-3-phosphate dehydrogenase; TPI: triosephosphate isomerase; PRK: phosphoribulokinase; rbcL: ribulose-bisphosphate carboxylase large chain; xfp: xylulose-5-phosphate/fructose-6-phosphate phosphoketolase; rpi A: ribose 5-phosphate isomerase A; tktA: transketolase; glpx-SEBP: fructose-1,6-bisphosphatase II/sedoheptulose-1,7-bisphosphatase; ALDO: fructose-bisphosphate aldolase, class I; FBP: fructose-1,6-bisphosphatase I; RPE: ribulose-phosphate 3-epimerase. Photosystem II (PSII) proteins (PsbA, PsbB, PsbC, PsbD, PsbE, PsbF, PsbO, PsbP, and PsbV); PSI proteins (PsaA, PsaB, PsaC, and PsaD); cytochrome b6 complex (PetD, PetE, PetF, PetH, and PetD); F-type ATPase proteins (ATPaseα, β, γ, ε, δ; a, b and c).</p>
Full article ">Figure 9
<p>Relative expression levels of genes at different time points under hydroponic conditions. Sampling time points were 0 h, 6 h, 12 h, and 24 h after the onset of salt stress. The relative expression levels of <span class="html-italic">ZmTRE1</span> (<b>A</b>), <span class="html-italic">ZmTPP2</span> (<b>B</b>), <span class="html-italic">ZmPP2C6</span> (<b>C</b>), <span class="html-italic">ZmPYL9</span> (<b>D</b>), and <span class="html-italic">ZmSnRK2.12</span> (<b>E</b>) were measured. Note: date showing the means ± standard deviation (SD). ns: no significant difference. Different letters indicate significant differences between the four treatments (CK: the control, S: salt stress, T: the application of trehalose under normal conditions, TS: the application of trehalose under salt stress) (<span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">n</span> = 3; Duncan test).</p>
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<p>In the soil cultivation experiment, the expression analysis of maize genes <span class="html-italic">ZmTRE1</span>, <span class="html-italic">ZmTPP2</span>, <span class="html-italic">ZmPP2C6</span>, <span class="html-italic">ZmPYL9</span>, and <span class="html-italic">ZmSnRK2.12</span> in different tissues under various treatments was conducted (<span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">n</span> = 3) (CK: the control, S: salt stress, T: the application of trehalose under normal conditions, TS: the application of trehalose under salt stress). A plant simulation cartoon (<b>A</b>) was used to create two forms of gene expression heat maps: a heat map (<b>B</b>) and a matrix heat map (<b>C</b>), showing the expression levels of the above genes in stem, leaf, female, male, and fruit. In the cartoon heat map, red indicates high expression levels, while blue indicates low expression levels. In the matrix heat map, red indicates high expression levels, and white indicates low expression levels.</p>
Full article ">Figure 11
<p>Experimental design diagram. The time points with gray shading represent the sampling times in the hydroponic experiment. Tre: 10 mM trehalose was sprayed twice daily, at 9 a.m. and 9 p.m. A 150 mM NaCl solution was used to simulate salt stress in the hydroponic experiment. Soil containing 0.175% NaCl was used to simulate growth in saline soil.</p>
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<p>Mechanism diagram of trehalose alleviating salt stress in maize.</p>
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7 pages, 1086 KiB  
Proceeding Paper
Changes in Photosynthetic Pigment Concentrations Induced by Pinewood Nematode Infection of In Vitro Pine Shoots
by Gonçalo Pereira and Jorge M. S. Faria
Environ. Earth Sci. Proc. 2024, 31(1), 5; https://doi.org/10.3390/eesp2024031005 - 16 Dec 2024
Viewed by 22
Abstract
The pinewood nematode (PWN), Bursaphelenchus xylophilus, infects susceptible pine species and causes pine wilt disease (PWD). The first visible symptoms are yellowing and drooping of pine needles due to compromised biochemical reactions of photosynthesis, as a result of damage to the tree’s [...] Read more.
The pinewood nematode (PWN), Bursaphelenchus xylophilus, infects susceptible pine species and causes pine wilt disease (PWD). The first visible symptoms are yellowing and drooping of pine needles due to compromised biochemical reactions of photosynthesis, as a result of damage to the tree’s water column. In vitro cultures are useful tools to study minute biochemical changes because they easily enable reproducibility and genetic homogeneity. In the present work, in vitro maritime pine (Pinus pinaster) shoot cultures were used to simulate PWD, by infecting with PWN in asepsis. Changes in the levels of photopigments, i.e., chlorophyll a and b, carotenoids, and stress related anthocyanins, were followed through spectrophotometry. Infection with the PWN led to a 30% decrease in shoot concentrations of chlorophyll a and a 50% reduction on chlorophyll b. Concentrations of carotenoids increased by 70%, while for anthocyanins no statistically significant changes were observed. PWN phytophagy seems to trigger chlorophyll degradation and production of carotenoids, most probably as a response to oxidative stress. This preliminary study allows gauging the impacts of PWN infection in pine, at the initial stages of PWD, as a contribution to developing, for example, an early detection tool for this phytoparasite. Full article
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Figure 1
<p><span class="html-italic">Pinus pinaster</span> seedling shoot cultured in multiplication medium [culture medium supplemented with 0.5 µg/L of 6-benzylaminopurine (BAP) and 0.1 µg/L of indole-3-butyric acid (IBA)] (<b>a</b>), in vitro microshoots in elongation medium (culture medium supplemented with 3 g/L of activated charcoal) (<b>b</b>), and in vitro explants before (<b>c</b>) and after (<b>d</b>) pinewood nematode infection. Bar = 1 cm.</p>
Full article ">Figure 1 Cont.
<p><span class="html-italic">Pinus pinaster</span> seedling shoot cultured in multiplication medium [culture medium supplemented with 0.5 µg/L of 6-benzylaminopurine (BAP) and 0.1 µg/L of indole-3-butyric acid (IBA)] (<b>a</b>), in vitro microshoots in elongation medium (culture medium supplemented with 3 g/L of activated charcoal) (<b>b</b>), and in vitro explants before (<b>c</b>) and after (<b>d</b>) pinewood nematode infection. Bar = 1 cm.</p>
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<p>Quantification of chlorophyll <span class="html-italic">a</span> (Chl <span class="html-italic">a</span>) and <span class="html-italic">b</span> (Chl <span class="html-italic">b</span>), carotenoids (Carot.) (µg/g FW) and anthocyanins (Antho.) (µmol/g FW) through spectrophotometry, from extracts of in vitro pine shoots without (green bars) and with the pinewood nematode (orange bars). Asterisks over the error bars indicate significant differences between these conditions (<span class="html-italic">p</span> &lt; 0.05).</p>
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16 pages, 6401 KiB  
Article
Estimation of Water Interception of Winter Wheat Canopy Under Sprinkler Irrigation Using UAV Image Data
by Xueqing Zhou, Haijun Liu and Lun Li
Water 2024, 16(24), 3609; https://doi.org/10.3390/w16243609 (registering DOI) - 15 Dec 2024
Viewed by 295
Abstract
Canopy water interception is a key parameter to study the hydrological cycle, water utilization efficiency, and energy balance in terrestrial ecosystems. Especially in sprinkler-irrigated farmlands, the canopy interception further influences field energy distribution and microclimate, then plant transpiration and photosynthesis, and finally crop [...] Read more.
Canopy water interception is a key parameter to study the hydrological cycle, water utilization efficiency, and energy balance in terrestrial ecosystems. Especially in sprinkler-irrigated farmlands, the canopy interception further influences field energy distribution and microclimate, then plant transpiration and photosynthesis, and finally crop yield and water productivity. To reduce the field damage and increase measurement accuracy under traditional canopy water interception measurement, UAVs equipped with multispectral cameras were used to extract in situ crop canopy information. Based on the correlation coefficient (r), vegetative indices that are sensitive to canopy interception were screened out and then used to develop canopy interception models using linear regression (LR), random forest (RF), and back propagation neural network (BPNN) methods, and lastly these models were evaluated by root mean square error (RMSE) and mean relative error (MRE). Results show the canopy water interception is first closely related to relative normalized difference vegetation index (R△NDVI) with r of 0.76. The first seven indices with r from high to low are R△NDVI, reflectance values of the blue band (Blue), reflectance values of the near-infrared band (Nir), three-band gradient difference vegetation index (TGDVI), difference vegetation index (DVI), normalized difference red edge index (NDRE), and soil-adjusted vegetation index (SAVI) were chosen to develop canopy interception models. All the developed linear regression models based on three indices (R△NDVI, Blue, and NDRE), the RF model, and the BPNN model performed well in canopy water interception estimation (r: 0.53–0.76, RMSE: 0.18–0.27 mm, MRE: 21–27%) when the interception is less than 1.4 mm. The three methods underestimate the canopy interception by 18–32% when interception is higher than 1.4 mm, which could be due to the saturation of NDVI when leaf area index is higher than 4.0. Because linear regression is easy to perform, then the linear regression method with NDVI is recommended for canopy interception estimation of sprinkler-irrigated winter wheat. The proposed linear regression method and the R△NDVI index can further be used to estimate the canopy water interception of other plants as well as forest canopy. Full article
(This article belongs to the Special Issue Agricultural Water-Land-Plant System Engineering)
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Figure 1
<p>Map of experimental location and experimental field in this study.</p>
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<p>Heat map of correlation analysis between vegetation indices and canopy water interception. Note: * indicates the correlation coefficient between the two indices is significant at 0.05 level; ** indicates the relationship is significant at 0.01 level.</p>
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<p>Performance of linear regression models using unary and multiple vegetative indices. Panel (<b>a</b>) represents the linear model based on R<sub>△NDVI</sub> (model 7 in <a href="#water-16-03609-t003" class="html-table">Table 3</a>); (<b>b</b>) represents the model based on R<sub>△NDVI</sub> and Blue (model 8 in <a href="#water-16-03609-t003" class="html-table">Table 3</a>); (<b>c</b>) represents model based on R<sub>△NDVI</sub>, Blue, and NDRE (model 11 in <a href="#water-16-03609-t003" class="html-table">Table 3</a>).</p>
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<p>The estimated and measured canopy interceptions by RF model in the model developing and calibrating processes.</p>
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<p>The estimated and measured canopy interceptions by BP neural network model in the model developing and calibrating processes.</p>
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<p>The relationship between normalized difference vegetation index (NDVI) and leaf area index (LAI) in winter wheat.</p>
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43 pages, 28157 KiB  
Article
Exploring the Physiological and Molecular Mechanisms by Which Potassium Regulates Low-Temperature Tolerance of Coconut (Cocos nucifera L.) Seedlings
by Lilan Lu, Yuping Wang, Md. Abu Sayed, Amjad Iqbal and Yaodong Yang
Agronomy 2024, 14(12), 2983; https://doi.org/10.3390/agronomy14122983 (registering DOI) - 14 Dec 2024
Viewed by 272
Abstract
Coconut holds significant importance as a fruit and oilseed crop in tropical and subtropical regions. However, low-temperature (LT) stress has caused substantial reductions in yield and economics and impedes coconut production, therefore constraining its widespread cultivation and utilization. The appropriate application of potassium [...] Read more.
Coconut holds significant importance as a fruit and oilseed crop in tropical and subtropical regions. However, low-temperature (LT) stress has caused substantial reductions in yield and economics and impedes coconut production, therefore constraining its widespread cultivation and utilization. The appropriate application of potassium (K) has the potential to enhance the cold tolerance of crops and mitigate cold damage, but the regulatory mechanisms by which K improves coconut adaptability to cold stress remain poorly understood. Transcriptome and metabolomic analyses were performed on coconut seedlings treated with LT (5 °C) and room temperature (25 °C) under various K conditions: K0 (0.1 mM KCL), KL (2 mM KCL), KM (4 mM KCL), and KH (8 mM KCL). Correlation analysis with physiological indicators was also conducted. The findings indicated that K absorption, nutrient or osmotic regulation, accumulation of substances, photosynthesis, hormone metabolism, and reactive oxygen species (ROS) clearance pathways played crucial roles in the adaptation of coconut seedlings to LT stress. LT stress disrupted the homeostasis of hormones, antioxidant enzyme activity, chlorophyll, K, and the regulation of nutrients and osmolytes. This stress also leads to the downregulation of genes and metabolites related to K transporters, hormone metabolism, transcription factors, and the metabolism of nutrients and osmolytes. Applying K helped maintain the homeostasis of hormones, antioxidant enzyme activity, chlorophyll, K, and the regulation of nutrients and osmolytes, promoted the removal of ROS, and reduced malondialdehyde, consequently diminishing the damage caused by LT stress to coconut seedlings. Furthermore, the comprehensive analysis of metabolomics and transcriptomics highlighted the importance of carbohydrate metabolism, biosynthesis of other secondary metabolites, amino acid metabolism, lipid metabolism, and ABC transporters in K’s role in improving coconut seedlings’ tolerance to LT stress. This study identified the pivotal biological pathways, regulatory genes, and metabolites implicated in K regulation of coconut seedlings to acclimate to LT stress. Full article
(This article belongs to the Special Issue Application of Multi-Omics and Systems Biology in Crop Breeding)
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Figure 1

Figure 1
<p>Investigating the effect of different K levels on the growth of coconut seedlings under LT and RT conditions. Note: (<b>a</b>) Phenotypes of coconut seedlings treated with different K levels under LT and RT conditions; (<b>b</b>) Structural diagram of paraffin sections of coconut seedling leaves treated with different K levels under LT and RT conditions. CP, Cytoplasm; CW, cell wall; CD, cell duct; CN, cell nucleus. LT, RT, K<sub>0</sub>, K<sub>L</sub>, K<sub>M</sub>, and K<sub>H</sub> are detailed in <a href="#agronomy-14-02983-t001" class="html-table">Table 1</a>.</p>
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<p>Physiological characteristics of coconut seedling leaves across various K levels under LT and RT conditions. (<b>a</b>) Endogenous hormones; (<b>b</b>) Enzyme activities. Note: POD, peroxidase; SOD, superoxide dismutase; CAT, catalase; APX, ascorbic acid peroxidase; IAA, auxin; ABA, abscisic acid; ZR, zein; GA, gibberellin. The values are the average of three biological replicates and three detection experiment replicates (n = 6). The vertical bar represents the average standard error. The statistical significance was calculated by the Student’s t-test, and “*” indicated a significant difference at the <span class="html-italic">p</span> &lt; 0.05 level. LT, RT, K<sub>0</sub>, K<sub>L</sub>, K<sub>M</sub>, and K<sub>H</sub> are detailed in <a href="#agronomy-14-02983-t001" class="html-table">Table 1</a>.</p>
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<p>Examining the top 20 KEGG enrichments of DEGs in coconut seedling leaves in different K levels under LT and RT conditions. Note: DEGs, differentially expressed genes; (<b>a</b>) K<sub>0</sub> vs. K<sub>L</sub> in LT; (<b>b</b>) K<sub>0</sub> vs. K<sub>M</sub> in LT; (<b>c</b>) K<sub>0</sub> vs. K<sub>H</sub> in LT; (<b>d</b>) K<sub>0</sub> vs. K<sub>L</sub> in RT; (<b>e</b>) K<sub>0</sub> vs. K<sub>M</sub> in RT; (<b>f</b>) K<sub>0</sub> vs. K<sub>H</sub> in RT. LT, RT, K<sub>0</sub>, K<sub>L</sub>, K<sub>M</sub>, and K<sub>H</sub> are detailed in <a href="#agronomy-14-02983-t001" class="html-table">Table 1</a>.</p>
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<p>Heat map of KEGG enriched DEGs in coconut seedling leaves in different K levels under LT and RT conditions. Note: DEGs, differentially expressed genes; A, K<sub>0</sub> vs. K<sub>L</sub>; B, K<sub>0</sub> vs. K<sub>M</sub>; C, K<sub>0</sub> vs. K<sub>H</sub>; (<b>a</b>) Plant hormone signal translation; (<b>b</b>) Flavonoid biosynthesis; (<b>c</b>) Alpha-Linolenic acid metabolism; (<b>d</b>) Starch and sucrose metabolism; (<b>e</b>) Amino sugar and nucleoside sugar metabolism; (<b>f</b>) Glycerophospholipid metabolism; (<b>g</b>) Galactose metabolism. LT, RT, K<sub>0</sub>, K<sub>L</sub>, K<sub>M</sub>, and K<sub>H</sub> are detailed in <a href="#agronomy-14-02983-t001" class="html-table">Table 1</a>.</p>
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<p>Heat map of DEGs of major transcription factors in coconut seedling leaves under different K levels under LT and RT conditions. Note: DEGs, differentially expressed genes, A, K<sub>0</sub> vs. K<sub>L</sub>; B, K<sub>0</sub> vs. K<sub>M</sub>; C, K<sub>0</sub> vs. K<sub>H</sub>; LT, RT, are detailed in <a href="#agronomy-14-02983-t001" class="html-table">Table 1</a>.</p>
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<p>Expression of DAMs in key pathways occurs in K<sub>0</sub> vs. K<sub>L</sub>, K<sub>0</sub> vs. K<sub>M</sub>, and K<sub>0</sub> vs. K<sub>H</sub> in LT and RT. Note: DAMs, differentially accumulated metabolites; DAMs are displayed in red, highlighted font. The redder the color of the heat map, the more significant the upregulation of DAMs; the bluer the color, the more significant the downregulation of DAMs. LT, RT, K<sub>0</sub>, K<sub>L</sub>, K<sub>M</sub>, and K<sub>H</sub> are detailed in <a href="#agronomy-14-02983-t001" class="html-table">Table 1</a>.</p>
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<p>Expression of DAMs in key pathways occurs in K<sub>0</sub> vs. K<sub>L</sub>, K<sub>0</sub> vs. K<sub>M</sub>, and K<sub>0</sub> vs. K<sub>H</sub> in LT and RT. Note: DAMs, differentially accumulated metabolites; DAMs are displayed in red, highlighted font. The redder the color of the heat map, the more significant the upregulation of DAMs; The bluer the color, the more significant the downregulation of DAMs. LT, RT, K<sub>0</sub>, K<sub>L</sub>, K<sub>M</sub>, and K<sub>H</sub> are detailed in <a href="#agronomy-14-02983-t001" class="html-table">Table 1</a>.</p>
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<p>Expression of DEGs and DAMs in key pathways found in K<sub>0</sub> vs. K<sub>L</sub>, K<sub>0</sub> vs. K<sub>M</sub>, and K<sub>0</sub> vs. K<sub>H</sub> in LT and RT. Note: DEGs, differentially expressed genes; DAMs, differentially accumulated metabolites; (<b>a</b>) Starch and sucrose metabolism, amino sugar and nucleate sugar metabolism, pyrimidine metabolism, galactose metabolism, and ascorbate and alarate metabolism; (<b>b</b>) Cysteine and methionine metabolism, glycerophoric metabolism, and ABC transporters; (<b>c</b>) Biosynthesis of amino acids; (<b>d</b>). Alpha-linolenic acid metabolism. DEGs are displayed in blue boxes, while DAMs are displayed in red, highlighted font. The redder the color of the heat map, the more significant the upregulation of DEGs and DAMs; The greener and bluer the color, the more significant the downregulation of DEGs and DAMs. LT, RT, K<sub>0</sub>, K<sub>L</sub>, K<sub>M</sub>, and K<sub>H</sub> are detailed in <a href="#agronomy-14-02983-t001" class="html-table">Table 1</a>.</p>
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<p>Expression of DEGs and DAMs in key pathways observed in K<sub>0</sub> vs. K<sub>L</sub>, K<sub>0</sub> vs. K<sub>M</sub>, and K<sub>0</sub> vs. K<sub>H</sub> in LT and RT. Note: DEGs, differentially expressed genes; DAMs, differentially accumulated metabolites; (<b>a</b>) Phenolpropanoid biosynthesis; (<b>b</b>) Flavonoid biosynthesis; (<b>c</b>) Tyrosine metabolism; (<b>d</b>) Lysine degradation. DEGs are shown in blue boxes, while DAMs are highlighted in red font. The redder the color of the heatmap, the more significant the upregulation of DEGs and DAMs; the greener and bluer the color, the more significant the downregulation of DEGs and DAMs. LT, RT, K<sub>0</sub>, K<sub>L</sub>, K<sub>M</sub>, and K<sub>H</sub> are detailed in <a href="#agronomy-14-02983-t001" class="html-table">Table 1</a>.</p>
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<p>A network regulation diagram of DAMs and DEGs in key metabolic pathways based on the Pearson correlation coefficient model. Note: DEGs, differentially expressed genes; DAMs, differentially accumulated metabolites; (<b>a</b>) Starch and sucrose metabolism (Ko00500); (<b>b</b>) Amino sugar and nucleate sugar metabolism (Ko00520); (<b>c</b>) Glycerophospholipid metabolism (Ko00564); (<b>d</b>) Galactose metabolism (Ko00052); (<b>e</b>) ABC transporters (Ko02010); (<b>f</b>) Cysteine and methionine metabolism (Ko00270); 1, UDP-glucose (meta_46); 2, N-Glycolylneuraminic acid (meta_997); 3, 1-Linoleoylcerophosphocholine (meta_172); 4, Phosphocholine (meta_53); 5, Choline (meta_19); 6, Stachyose (meta_343); 7, N-Acetyl-D-galactosamine (meta_241); 8, Raffinose (meta_402); 9, Betaine (meta_116); 10, Glutathione (meta_208); 11, DL-Methionine sulfoxide (meta_1083); 12, S-Adenosylhomocysteine (meta_842). Filtered and plotted based on absolute values with correlation coefficients &gt; 0.8. The straight line between DEGs and DAMs represents correlation, with thicker and darker lines indicating greater positive correlation and thinner and lighter lines indicating greater negative correlation.</p>
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<p>A network regulation diagram of DEGs and DAMs in key metabolic pathways based on the Pearson correlation coefficient model. Note: DEGs, differentially expressed genes; DAMs, differentially accumulated metabolites; (<b>a</b>) alpha-Linolenic acid metabolism (Ko 00592); (<b>b</b>) Tyrosine metabolism (Ko00350); (<b>c</b>) Flavonoid biosynthesis (Ko00941); (<b>d</b>) Phenolpropanoid biosynthesis (Ko00940); 1, Traumatic acid (meta_743); 2, alpha-Linolenic acid(meta_551); 3, 12-oxo-10E-dodecenoic acid (meta_392); 4, 9(S)-HpOTrE (meta_29); 5, 13(S)-HOTrE (meta_31); 6, Gentisaldehyde (meta_715); 7, 2,5-Dihydroxybenzaldehyde (meta_432); 8, Gentisic acid (meta_66); 9, DL-Vanillylmandelic acid (meta_672); 10, 2-(4-Hydroxyphenyl) ethanal (meta_1042); 11, 4-Hydroxypheylpyruvate(meta_580); 12, Succinic acid (meta_9); 13, Phenol(meta_743); 14, Cyanidin (meta_423); 15, Neohesperidin (meta_530); 16, Chlorogenate (meta_569); 17,Chlorogenic acid (meta_52); 18, Trans-Cinnamate (meta_923); 19, Caffeic acid (meta_10); 20, 3,5-Dimethoxy-4-hydroxycinnamic acid(meta_632); 21, Coumarin (meta_216); 22, 4-Hydroxy-3-methoxycinnamaldehyde (meta_211). Filtered and plotted based on absolute values with correlation coefficients &gt; 0.8. The straight line between DEGs and DAMs represents correlation, with red lines indicating a positive correlation, thicker and darker red lines indicating a greater positive correlation, green lines indicating a negative correlation, and thicker and darker green lines indicating a greater negative correlation.</p>
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<p>A schematic diagram of the mechanism by which K enhances the cold tolerance of coconut seedlings. Note: Rectangles represent genes, while ovals represent metabolites. Red font indicates upregulation of gene and metabolite expression, while green font indicates downregulation of gene and metabolite expression. Orange font indicates both upregulation and downregulation of gene and metabolite expression.</p>
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19 pages, 2228 KiB  
Article
Genotypic Differences Among Scions and Rootstocks Involved with Oxidative Damage and Ionic Toxicity in Cashew Plants Under Salinity
by Eugênio Silva Araújo Júnior, Anselmo Ferreira Silva, Josemir Moura Maia, Elania Freire da Silva, Alexandre Maniçoba da Rosa Ferraz Jardim, Hugo Rafael Bentzen Santos, Carlos Alberto Vieira Souza, Adriano do Nascimento Simões, Eduardo Souza and Sérgio Luiz Ferreira-Silva
Horticulturae 2024, 10(12), 1341; https://doi.org/10.3390/horticulturae10121341 - 14 Dec 2024
Viewed by 179
Abstract
The aim of this study was to evaluate the influence of scion/rootstock genotypes on ionic toxicity, oxidative damage, and photosynthesis in cashew plants subjected to salt stress. Scion/rootstock combinations (CCP 76/CCP 76, CCP 76/CCP 09, CCP 09/CCP 09 and CCP 09/CCP 76) were [...] Read more.
The aim of this study was to evaluate the influence of scion/rootstock genotypes on ionic toxicity, oxidative damage, and photosynthesis in cashew plants subjected to salt stress. Scion/rootstock combinations (CCP 76/CCP 76, CCP 76/CCP 09, CCP 09/CCP 09 and CCP 09/CCP 76) were obtained by reciprocal grafting between two genotypes (CCP 76 and CCP 09) of dwarf cashew and subjected to increased NaCl (0, 50 and 100 mM) for 30 days. Plants with CCP 76 scions had higher leaf fresh weights compared to plants with CCP 09 scions in both moderate (50 mM)- and high (100 mM)-salinity conditions. Under moderate levels of salinity, CCP 76 scions showed lower stomatal conductance, which is associated with weaker leaf toxicity symptoms, as well as lower Na+ content and higher K+ content in the leaves. Thus, the better foliar exclusion of Na+ by CCP 76 scions can be attributed to greater stomatal control, which allows for better growth and sufficient foliar K+ nutrition to mitigate foliar toxicity. Under high levels of salinity, a reduction in net photosynthesis occurred in all scion/rootstock combinations, which was apparently due to stomatal and non-stomatal restrictions. The activities of the oxidative enzymes (superoxide dismutase—SOD; ascorbate peroxidase—APX; and phenol peroxidase—POD) were little influenced by salinity, while there was a significant increase in the non-enzymatic antioxidants ascorbate (AsA) and glutathione (GSH). In addition, a reduction in photochemical activity was observed under saline conditions, suggesting that photosystems possess a potential protective mechanism. It was observed that the stomatal closure exhibited by the CCP 76 scion genotype may exert relative control over the flow of Na+ to the shoots under salt stress conditions. Taken together, the data show that, in the two genotypes evaluated, oxidative protection was more associated with reduced photochemical activity and higher levels of non-enzymatic antioxidants (AsA and GSH) than it was with the SOD-APX-POD enzymatic system. Full article
(This article belongs to the Section Genetics, Genomics, Breeding, and Biotechnology (G2B2))
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<p>Fresh weight of leaves (<b>a</b>) and roots (<b>b</b>) of four scion/rootstock combinations subjected to salinity by increasing the concentration of NaCl (0, 50 and 100 mM) in the nutrient solution for 30 days. The bars represent the means of four replicates ± standard deviation (SD), and those with the same lowercase letter within the saline treatments and uppercase letter between the saline levels do not differ from each other when assessed using Tukey’s test (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Net photosynthesis [<span class="html-italic">P<sub>N</sub></span>] (<b>a</b>), stomatal conductance [<span class="html-italic">g<sub>S</sub></span>] (<b>b</b>), and instantaneous carboxylation efficiency [<span class="html-italic">P<sub>N</sub></span>/<span class="html-italic">C<sub>i</sub></span>] (<b>c</b>) in four cashew scions/rootstocks combinations subjected to salinity by increasing the concentration of NaCl (0, 50 and 100 mM) in the nutrient solution for 30 days. The bars represent the mean value of four repetitions ± SD, and those with the same lowercase letter within the saline treatments and uppercase between the saline levels do not differ when assessed using Tukey’s test (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The maximum quantum yield [F<sub>v</sub>/F<sub>m</sub>] (<b>a</b>) and actual quantum yield [ΔF/F<sub>m</sub><sup>′</sup>] (<b>b</b>) of primary photochemistry, apparent electron transport rate [ETR] (<b>c</b>), and photochemical extinction [qP] (<b>d</b>) in four cashew scions/rootstock combinations subjected to salinity by increasing the concentration of NaCl (0, 50, and 100 mM) in the nutrient solution for 30 days. The bars represent the means of four repetitions ± SD, and those with the same lowercase letter within the saline treatments and uppercase letter between the saline levels do not differ when assessed using Tukey’s test (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Changes in the levels of hydrogen peroxide [H<sub>2</sub>O<sub>2</sub>] (<b>a</b>), TBARSs (<b>b</b>), ascorbate [AsA] (<b>c</b>), and glutathione [GSH] (<b>d</b>) in the leaves of four cashew scions/rootstock combinations subjected to salinity by increasing the concentration of NaCl (0, 50 and 100 mM) in the nutrient solution for 30 days. The bars represent the mean values of four repetitions ± SD, and those with the same lowercase letter within the saline treatments and uppercase letter between the saline levels do not differ from each other when assessed by Tukey’s test (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Activities of the enzymes superoxide dismutase [SOD] (<b>a</b>), ascorbate peroxidase [APX] (<b>b</b>), and phenol peroxidase [POD] (<b>c</b>) in the leaves of four cashew scions/rootstock combinations subjected to salinity by increasing the concentration of NaCl (0, 50 and 100 mM) in the nutrient solution for 30 days. The bars represent the means of four replicates ± SD, and those with the same lowercase letter within the saline treatments and uppercase letter between the saline levels do not differ from each other when assessed using Tukey’s test (<span class="html-italic">p</span> &lt; 0.05).</p>
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25 pages, 8251 KiB  
Article
Effects of Far-Red Light and Ultraviolet Light-A on Growth, Photosynthesis, Transcriptome, and Metabolome of Mint (Mentha haplocalyx Briq.)
by Lishu Yu, Lijun Bu, Dandan Li, Kaili Zhu, Yongxue Zhang, Shaofang Wu, Liying Chang, Xiaotao Ding and Yuping Jiang
Plants 2024, 13(24), 3495; https://doi.org/10.3390/plants13243495 (registering DOI) - 14 Dec 2024
Viewed by 265
Abstract
To investigate the effects of different light qualities on the growth, photosynthesis, transcriptome, and metabolome of mint, three treatments were designed: (1) 7R3B (70% red light and 30% blue light, CK); (2) 7R3B+ far-red light (FR); (3) 7R3B+ ultraviolet light A (UVA). The [...] Read more.
To investigate the effects of different light qualities on the growth, photosynthesis, transcriptome, and metabolome of mint, three treatments were designed: (1) 7R3B (70% red light and 30% blue light, CK); (2) 7R3B+ far-red light (FR); (3) 7R3B+ ultraviolet light A (UVA). The results showed that supplemental FR significantly promoted the growth and photosynthesis of mint, as evidenced by the increase in plant height, plant width, biomass, effective quantum yield of PSII photochemistry (Fv’/Fm’), maximal quantum yield of PSII (Fv/Fm), and performance index (PI). UVA and CK exhibited minimal differences. Transcriptomic and metabolomic analysis indicated that a total of 788 differentially expressed genes (DEGs) and 2291 differential accumulated metabolites (DAMs) were identified under FR treatment, mainly related to plant hormone signal transduction, phenylpropanoid biosynthesis, and flavonoid biosynthesis. FR also promoted the accumulation of phenylalanine, sinapyl alcohol, methylchavicol, and anethole in the phenylpropanoid biosynthesis pathway, and increased the levels of luteolin and leucocyanidin in the flavonoid biosynthesis pathway, which may perhaps be applied in practical production to promote the natural antibacterial and antioxidant properties of mint. An appropriate increase in FR radiation might alter transcript reprogramming and redirect metabolic flux in mint, subsequently regulating its growth and secondary metabolism. Our study uncovered the regulation of FR and UVA treatments on mint in terms of growth, physiology, transcriptome, and metabolome, providing reference for the cultivation of mint and other horticultural plants. Full article
(This article belongs to the Section Plant Physiology and Metabolism)
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<p>Effects of different light qualities on mint growth morphology. CK: 70% red light and 30% blue light (7R3B), FR: 7R3B + far-red light, UVA: 7R3B + ultraviolet light A.</p>
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<p>Effects of different light qualities on mint growth. (<b>A</b>) Plant height. (<b>B</b>) Plant width. (<b>C</b>) Plant height (d34). (<b>D</b>) Plant width (d34). CK: 70% red light and 30% blue light (7R3B), FR: 7R3B + far-red light, UVA: 7R3B + ultraviolet light A. Treatments were replicated three times, and different letters indicated significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Effects of different light qualities on mint biomass. (<b>A</b>) Shoot fresh weight. (<b>B</b>) Root fresh weight. (<b>C</b>) Shoot dry weight. (<b>D</b>) Root dry weight. CK: 70% red light and 30% blue light (7R3B), FR: 7R3B + far-red light, UVA: 7R3B + ultraviolet light A. Treatments were replicated three times, and different letters indicated significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Effects of different light qualities on mint light response curves. CK: 70% red light and 30% blue light (7R3B), FR: 7R3B + far-red light, UVA: 7R3B + ultraviolet light A.</p>
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<p>Effects of different light qualities on mint gas exchange parameters. (<b>A</b>) Net photosynthetic rate (P<sub>n</sub>). (<b>B</b>) Intercellular CO<sub>2</sub> concentration (C<sub>i</sub>). (<b>C</b>) Stomatal conductance (G<sub>s</sub>). (<b>D</b>) Transpiration rate (T<sub>r</sub>). (<b>E</b>) Water use efficiency (WUE). CK: 70% red light and 30% blue light (7R3B), FR: 7R3B + far-red light, UVA: 7R3B + ultraviolet light A. Treatments were replicated three times, and different letters indicated significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Effects of different light qualities on chlorophyll fluorescence parameters. (<b>A</b>) Actual photochemical efficiency of PSII (ΦPSII). (<b>B</b>) Electron transport rate (ETR). (<b>C</b>) Photochemical quenching coefficient (qP). (<b>D</b>) Effective quantum yield of PSII photochemistry (F<sub>v</sub>’/F<sub>m</sub>’). (<b>E</b>) Maximal quantum yield of PSII (F<sub>v</sub>/F<sub>m</sub>). (<b>F</b>) Performance index (PI). CK: 70% red light and 30% blue light (7R3B), FR: 7R3B + far-red light, UVA: 7R3B + ultraviolet light A. Treatments were replicated three times, and different letters indicated significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Effects of different light qualities on gene expression. (<b>A</b>) PCA of transcriptome samples under different light qualities. (<b>B</b>) Number of DEGs detected in FR vs. CK and UVA vs. CK. (<b>C</b>) Venn diagram of DEGs in FR vs. CK and UVA vs. CK. CK: 70% red light and 30% blue light (7R3B), FR: 7R3B + far-red light, UVA: 7R3B + ultraviolet light A. Treatments were replicated three times.</p>
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<p>Effects of different light qualities on KEGG pathway enrichment of DEGs. (<b>A</b>) KEGG pathway enrichment of DEGs between FR and CK treatments. (<b>B</b>) KEGG pathway enrichment of DEGs between UVA and CK treatments. CK: 70% red light and 30% blue light (7R3B), FR: 7R3B + far-red light, UVA: 7R3B + ultraviolet light A. Treatments were replicated three times.</p>
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<p>Venn diagram of DAMs in FR vs. CK and UVA vs. CK. CK: 70% red light and 30% blue light (7R3B), FR: 7R3B + far-red light, UVA: 7R3B + ultraviolet light A. Treatments were replicated three times.</p>
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<p>Effects of different light qualities on KEGG pathway enrichment of DAMs. (<b>A</b>) KEGG annotation of DAMs. (<b>B</b>) The top ten up and down-regulated DAMs of fold change between FR and CK treatments. (<b>C</b>) The top ten up and down-regulated DAMs of fold change between UVA and CK treatments. (<b>D</b>) KEGG pathway enrichment of DAMs between FR and CK treatments. (<b>E</b>) KEGG pathway enrichment of DAMs between UVA and CK treatments. CK: 7R3B, FR: 7R3B + far-red light, UVA: 70% red light and 30% blue light (7R3B) + ultraviolet light A. Treatments were replicated three times.</p>
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<p>The DEGs and DAMs involved in plant hormone signal transduction pathway in response to different light qualities. The color in the rectangle represents the genes or metabolites that were regulated under different light qualities (red indicated up-regulation; yellow indicated non-significant; blue indicated down-regulation). CK: 70% red light and 30% blue light (7R3B), FR: 7R3B + far-red light, UVA: 7R3B + ultraviolet light A. Treatments were replicated three times.</p>
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<p>The DEGs and DAMs involved in phenylpropanoid biosynthesis pathway in response to different light qualities. The color in the rectangle represents the genes or metabolites that were regulated under different light qualities (red indicated up-regulation; yellow indicated non-significant; blue indicated down-regulation). CK: 70% red light and 30% blue light (7R3B), FR: 7R3B + far-red light, UVA: 7R3B + ultraviolet light A. Treatments were replicated three times.</p>
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<p>The DEGs and DAMs involved in flavonoid biosynthesis pathway in response to different light qualities. The color in the rectangle represents the genes or metabolites that were regulated under different light qualities (red indicated up-regulation; yellow indicated non-significant; blue indicated down-regulation). CK: 70% red light and 30% blue light (7R3B), FR: 7R3B + far-red light, UVA: 7R3B + ultraviolet light A. Treatments were replicated three times.</p>
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<p>(<b>A</b>) qRT-PCR analysis of the gene expression patterns and FPKM expression level in mint seedlings under different light qualities. (<b>B</b>) Log<sub>2</sub> Fold Change of RNA-seq and qRT-PCR analysis of <span class="html-italic">AUX/IAA</span>, <span class="html-italic">DELLA</span>, and <span class="html-italic">POD</span>. CK: 70% red light and 30% blue light (7R3B), FR: 7R3B + far-red light, UVA: 7R3B + ultraviolet light A. Treatments were replicated three times. “*” indicates a significant correlation at <span class="html-italic">p</span> ≤ 0.05 and “**” indicates a significant correlation at <span class="html-italic">p</span> ≤ 0.01.</p>
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<p>Correlation analysis between different results. The upper right ellipse represents the correlation between different parameters, and the lower left numbers represent the correlation coefficients, with red being a positive correlation and blue being a negative correlation. “*” indicates a significant correlation at <span class="html-italic">p</span> ≤ 0.05 and “**” indicates a significant correlation at <span class="html-italic">p</span> ≤ 0.01.</p>
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<p>PCA of different results. Arrow direction and length indicate correlation and strength, respectively.</p>
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<p>(<b>A</b>) Seedling rack for conducting experiments. (<b>B</b>) Spectral graphs of different treatments. CK: 70% red light and 30% blue light (7R3B), FR: 7R3B + far-red light, UVA: 7R3B + ultraviolet light A.</p>
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<p>The frame diagram depicting the addition of FR to red and blue light on the growth, photosynthesis, transcriptome, and metabolome of mint. The arrow “↑” indicates that the indicator was up-regulated under FR. FR: 70% red light and 30% blue light (7R3B) + far-red light.</p>
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18 pages, 4507 KiB  
Article
An Artificial Intelligence-Powered Environmental Control System for Resilient and Efficient Greenhouse Farming
by Meng-Hsin Lee, Ming-Hwi Yao, Pu-Yun Kow, Bo-Jein Kuo and Fi-John Chang
Sustainability 2024, 16(24), 10958; https://doi.org/10.3390/su162410958 - 13 Dec 2024
Viewed by 412
Abstract
The rise in extreme weather events due to climate change challenges the balance of supply and demand for high-quality agricultural products. In Taiwan, greenhouse cultivation, a key agricultural method, faces increasing summer temperatures and higher operational costs. This study presents the innovative AI-powered [...] Read more.
The rise in extreme weather events due to climate change challenges the balance of supply and demand for high-quality agricultural products. In Taiwan, greenhouse cultivation, a key agricultural method, faces increasing summer temperatures and higher operational costs. This study presents the innovative AI-powered greenhouse environmental control system (AI-GECS), which integrates customized gridded weather forecasts, microclimate forecasts, crop physiological indicators, and automated greenhouse operations. This system utilizes a Multi-Model Super Ensemble (MMSE) forecasting framework to generate accurate hourly gridded weather forecasts. Building upon these forecasts, combined with real-time in-greenhouse meteorological data, the AI-GECS employs a hybrid deep learning model, CLSTM-CNN-BP, to project the greenhouse’s microclimate on an hourly basis. This predictive capability allows for the assessment of crop physiological indicators within the anticipated microclimate, thereby enabling preemptive adjustments to cooling systems to mitigate adverse conditions. All processes run on a cloud-based platform, automating operations for enhanced environmental control. The AI-GECS was tested in an experimental greenhouse at the Taiwan Agricultural Research Institute, showing strong alignment with greenhouse management needs. This system offers a resource-efficient, labor-saving solution, fusing microclimate forecasts with crop models to support sustainable agriculture. This study represents critical advancements in greenhouse automation, addressing the agricultural challenges of climate variability. Full article
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<p>Illustration of the study area located in the Taiwan Agricultural Research Institute (TARI) in Central Taiwan. (<b>a</b>) TARI greenhouse. (<b>b</b>) Tomato cultivation. (<b>c</b>) Outdoor weather monitoring station.</p>
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<p>Conceptual flow of the proposed AI-powered greenhouse environmental control system (AI-GECS).</p>
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<p>Conceptual illustration of the time-delay method for multi-model super-ensemble forecasting. MF1-MF6 denote six forecast models.</p>
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<p>Architecture of the CLSTM-CNN-BP model.</p>
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<p>Illustration of the data flow for the AI-enabled environment control module.</p>
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<p>Control process of the AI-powered greenhouse environmental control system (AI-GECS).</p>
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<p>AI-enabled environmental control module. (<b>a</b>) Module size: 39 cm × 34 cm × 17.5 cm. (<b>b</b>) A: Relay control board; B: network sub-module; and C: backup battery (12 V).</p>
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<p>The performance of the proposed AI-GECS implemented in the TARI greenhouse during 9 October 2020 and 12 October 2020. Microclimate forecasts at T + 1 were generated from CLSTM-CNN-BP in consideration of the impact of environmental control equipment on microclimate and photosynthesis rate. (<b>a</b>) Internal Temp (°C); (<b>b</b>) internal RH (%); (<b>c</b>) internal PAR (μmol•m<sup>−2</sup>•s<sup>−1</sup>).</p>
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17 pages, 6646 KiB  
Article
Transcriptome Analysis Reveals Key Pathways and Genes Involved in Lodging Resistance of Upland Cotton
by Yuan Wang, Ao Feng, Caiwang Zhao, Xiaomei Ma, Xinyu Zhang, Yanjun Li and Jie Sun
Plants 2024, 13(24), 3493; https://doi.org/10.3390/plants13243493 (registering DOI) - 13 Dec 2024
Viewed by 346
Abstract
Lodging resistance is one of the most important traits of machine-picked cotton. Lodging directly affects the cotton yield, quality and mechanical harvesting effect. However, there are only a few reports on the lodging resistance of cotton. In this study, the morphological and physiological [...] Read more.
Lodging resistance is one of the most important traits of machine-picked cotton. Lodging directly affects the cotton yield, quality and mechanical harvesting effect. However, there are only a few reports on the lodging resistance of cotton. In this study, the morphological and physiological characteristics and transcriptome of two upland cotton varieties with different lodging resistance were compared. The results showed that the stem strength; the contents of lignin, soluble sugar and cellulose; and the activities of several lignin biosynthesis-related enzymes of the lodging-resistant variety M153 were significantly higher than those of the lodging-susceptible variety M5330. Transcriptomic analysis showed that the expression level of several genes related to lignin, cellulose, starch and sucrose synthesis, and photosynthesis were significantly up-regulated in the lodging-resistant variety M153, which was consistent with the content determination results of lignin, cellulose and soluble sugar. Silencing two lignin biosynthesis-related genes (GhPAL and Gh4CL) in cotton via VIGS (Virus-Induced Gene Silencing) resulted in reduced lignin content and decreased lodging resistance in cotton. These results suggested that lignin, cellulose and soluble sugar contents were positively correlated with the lodging resistance of cotton, and lignin, cellulose and soluble sugar biosynthesis-related genes can be used as potential targets for improving the lodging resistance of cotton. These findings provide a theoretical basis for the cultivation of cotton varieties with strong lodging resistance in the future. Full article
(This article belongs to the Section Plant Genetics, Genomics and Biotechnology)
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<p>Images and the morphological indices of two cotton varieties at three developmental stages. (<b>A</b>) An observation of stem lodging of M153 cotton at the boll stage. (<b>B</b>) An observation of stem lodging of M5330 cotton at the boll stage. (<b>C</b>) Plant height. (<b>D</b>) Gravity center height. (<b>E</b>) Stem diameter. (<b>F</b>) Stem fresh weight. (<b>G</b>) Puncture resistance of the stem. (<b>H</b>) Bending resistance of the stem. S1: the bud stage; S2: the boll stage; S3: the boll opening stage. SPSS 26.0 software was used for statistical analysis of the data. Each value is the mean of twenty biological replicates. Duncan‘s new multiple range method was used to test the significance of the differences. Different lowercase letters represent statistically significant differences among varieties in the same growing stage at the <span class="html-italic">p</span> &lt; 0.05 level from one-way ANOVA tests.</p>
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<p>Histochemical staining and physiological indices of the two cotton varieties at three developmental stages. (<b>A</b>) Lignin deposition of the stems of two cotton varieties at three developmental stages following phloroglucinol staining. (<b>B</b>) Lignin content. (<b>C</b>) PAL activity. (<b>D</b>) 4CL activity. (<b>E</b>) CAD activity. (<b>F</b>) Cellulose content. (<b>G</b>) Soluble sugar content. The positions indicated by the red arrows are the phloem (Ph) and xylem (Xyl). SPSS 26.0 software was used for statistical analysis of the data. Each value is the mean of ten biological replicates. Duncan‘s new multiple range method was used to test the significance of the differences. Different lowercase letters represent statistically significant differences among varieties in the same growing stage at the <span class="html-italic">p</span> &lt; 0.05 level from one-way ANOVA tests.</p>
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<p>Transcriptome analysis of two cotton varieties at the boll stage. (<b>A</b>) A heatmap of expression level correlation for pairwise samples. (<b>B</b>) Principal component analysis (PCA) of gene expression between sample groups. (<b>C</b>) A volcano plot of DEGs. Red and green points represent the up-regulated and down-regulated DEGs in the lodging resistance variety, respectively. (<b>D</b>) Go analysis of DEGs. (<b>E</b>) KEGG analysis of DEGs.</p>
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<p>Transcript profiling of genes in phenylpropanoid biosynthetic pathway in two cotton varieties. (<b>A</b>) Heatmap of DEGs involved in lignin biosynthesis pathway in two cotton varieties. (<b>B</b>) Diagram of phenylpropanoid biosynthesis pathway.</p>
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<p>A heatmap of DEGs related to the photosynthesis and starch and sucrose metabolism pathways and cellulose biosynthesis. (<b>A</b>) A heatmap of DEGs related to the photosynthesis pathway. (<b>B</b>) A heatmap of DEGs related to the starch and sucrose pathways. (<b>C</b>) A heatmap of DEGs related to cellulose biosynthesis.</p>
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<p>The expression levels and phylogenetic tree analysis of <span class="html-italic">GhPAL</span> and <span class="html-italic">Gh4CL</span>. (<b>A</b>) The expression level of the <span class="html-italic">GhPAL</span> gene in the two cotton varieties. (<b>B</b>) The expression level of the <span class="html-italic">Gh4CL</span> gene in the two cotton varieties. (<b>C</b>) A phylogenetic tree of GhPAL and PAL from other plants. (<b>D</b>) A phylogenetic tree of Gh4CL and PAL from other plants. GhPAL and Gh4CL marked with red are genes used for functional confirmation by HIGS. Gh: <span class="html-italic">Gossypium hirsutum</span>; Pb: <span class="html-italic">Pyrus bretschneideri</span>; At: <span class="html-italic">Arabidopsis thaliana</span>; Cs: <span class="html-italic">Cucumis sativus</span>; Gm: <span class="html-italic">Glycine max</span>; Os: <span class="html-italic">Oryza sativa.</span> The phylogenetic tree was constructed using MEGA11.0 via the N-J method. Each value is the mean of three biological replicates. Statistical analysis of the data was conducted using IBM SPSS Statistics version 26.0, with statistical significance being assessed via Student’s <span class="html-italic">t</span>-test. ** represents a significant difference at <span class="html-italic">p</span> &lt; 0.01 between the two cotton varieties.</p>
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<p>The morphological and physiological indices of cotton after silencing the of <span class="html-italic">GhPAL</span> and <span class="html-italic">Gh4CL</span> by VIGS. (<b>A</b>) The bleaching phenotype of pTRV2-<span class="html-italic">GhCHLI</span>-treated cotton plants. (<b>B</b>) The relative expression level of <span class="html-italic">GhPAL</span> in pTRV2:<span class="html-italic">GhPAL</span>-treated plants and <span class="html-italic">Gh4CL</span> genes in pTRV2:<span class="html-italic">Gh4CL</span>-treated plants. (<b>C</b>) The lignin deposition in stems of pTRV2-<span class="html-italic">GhPAL</span>- and pTRV2-<span class="html-italic">Gh4CL</span>-treated plants following histochemical staining. Ph: phloem; Xyl: xylem. (<b>D</b>) The lignin content in the pTRV2-<span class="html-italic">GhPAL</span>- and pTRV2-<span class="html-italic">Gh4CL</span>-treated cotton plants. (<b>E</b>) The expression levels of genes related to lignin biosynthesis in pTRV2-<span class="html-italic">00</span>- and pTRV2-<span class="html-italic">GhPAL</span>-treated cotton plants. (<b>F</b>) The expression levels of genes related to lignin biosynthesis in pTRV2-<span class="html-italic">00</span>- and pTRV2-<span class="html-italic">Gh4CL</span>-treated plants. (<b>G</b>) The stem diameter of VIGS-treated plants. (<b>H</b>) The puncture resistance strength of VIGS-treated plants. (<b>I</b>) The bending resistance strength of VIGS-treated plants. (<b>J</b>) The breaking resistance strength of VIGS-treated plants. Each value is the mean of three biological replicates in figure (<b>B</b>,<b>D</b>,<b>E</b>,<b>F</b>), and the mean of ten biological replicates in figure (<b>G</b>–<b>J</b>). Statistical analysis of the data was conducted using IBM SPSS Statistics version 26.0, with statistical significance being assessed via Student’s <span class="html-italic">t</span>-test. ** represents a significant difference at <span class="html-italic">p</span> &lt; 0.01 between VIGS-treated plants (pTRV2-<span class="html-italic">GhPAL</span>- and pTRV2-<span class="html-italic">Gh4CL</span>-treated plants) and pTRV2-<span class="html-italic">00</span>-treated plants and the ns represents no significant difference.</p>
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24 pages, 9193 KiB  
Article
Cycas Leaf:Seed Ratios Do Not Influence Seed Size, Gametophyte Carbohydrates, or Leaf Photosynthesis
by Thomas E. Marler
Agronomy 2024, 14(12), 2974; https://doi.org/10.3390/agronomy14122974 - 13 Dec 2024
Viewed by 269
Abstract
Experimental manipulations of the balance between leaves as source organs and reproductive structures as sink organs have contributed greatly to our understanding of the assimilate partitioning and regulation of leaf photosynthesis. In order to add cycads to this research agenda, the full range [...] Read more.
Experimental manipulations of the balance between leaves as source organs and reproductive structures as sink organs have contributed greatly to our understanding of the assimilate partitioning and regulation of leaf photosynthesis. In order to add cycads to this research agenda, the full range in natural variation in leaf:seed ratio and incident light level of in situ Cycas micronesica was augmented with the experimental manipulation of leaf:seed ratios of C. micronesica and Cycas edentata in Guam and the Philippines. In every study, individual seed size and concentrations of megagametophyte carbon, starch, and sugars were not influenced by leaf:seed ratio. The leaf net photosynthesis (Pn) and operational efficiency of photosystem II were also quantified for the in situ studies, and leaf:seed ratio did not influence these leaf physiology traits. The natural variation in incident light revealed increased net Pn for C. micronesica trees receiving greater levels of light, but the sink traits of seeds were not influenced by these differences in source strength. The findings indicated that the size and sink activity of individual cycad seeds are constitutive traits that are not influenced by the relative balance between leaf source and seed sink size at the individual plant level. The results also reveal that upregulation or downregulation of cycad leaf Pn is not influenced by sink size or source:sink ratio. The massive amounts of nonstructural carbohydrates in cycad stems and roots may explain these findings, as these organs may be the primary source for strobilus and seed growth independently from leaf Pn. Full article
(This article belongs to the Section Horticultural and Floricultural Crops)
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<p><span class="html-italic">Cycas micronesica</span> habitats in Guam: (<b>a</b>) southern Guam riparian habitat on volcanic acid substrates; (<b>b</b>) northern Guam upland habitat on karst alkaline substrates.</p>
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<p><span class="html-italic">Cycas</span> plants exploited for experimental manipulation of leaf and seed number: (<b>a</b>) <span class="html-italic">Cycas micronesica</span> in an eastern Guam karst forest; (<b>b</b>) reproductive <span class="html-italic">Cycas edentata</span> plant in ex situ germplasm garden in the Philippines.</p>
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<p>Leaf physiology traits of <span class="html-italic">Cycas micronesica</span> trees grown under various percentages of canopy openness. Net carbon dioxide assimilation (<span class="html-italic">Pn</span>, circles with solid lines) and operational efficiency of photosystem II (<span class="html-italic">Φ<sub>PSII</sub></span>, diamonds with dashed lines): (<b>a</b>) Riparian forest in southern Guam; (<b>b</b>) Karst forest in northern Guam. Markers are mean ± SE, <span class="html-italic">n</span> = 6.</p>
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<p>The influence of two leaf:seed ratio categories on <span class="html-italic">Cycas micronesica</span> seed tissue weights: open bars = fresh weight (g); shaded bars = dry weight (g); (<b>a</b>) 10:30 sclerotesta; (<b>b</b>) 10:30 sarcotesta; (<b>c</b>) 10:30 megagametophyte; (<b>d</b>) 30:10 sclerotesta; (<b>e</b>) 30:10 sarcotesta; (<b>f</b>) 30:10 megagametophyte. Statistics above each column compare 10:30 and 30:10 results for each of the six weights. Bars are mean ± SE, <span class="html-italic">n</span> = 6.</p>
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<p>The influence of three leaf:seed ratio categories on <span class="html-italic">Cycas edentata</span> seed tissue weights: open bars = fresh weight (g); shaded bars = dry weight (g); (<b>a</b>) 10:40 sclerotesta; (<b>b</b>) 10:40 sarcotesta; (<b>c</b>) 10:40 megagametophyte; (<b>d</b>) 25:25 sclerotesta; (<b>e</b>) 25:25 sarcotesta; (<b>f</b>) 25:25 megagametophyte; (<b>g</b>) 40:10 sclerotesta; (<b>h</b>) 40:10 sarcotesta; (<b>i</b>) 40:10 megagametophyte. Statistics above each column compare the three leaf:seed categories for each of the six weights. Bars are mean ± SE, <span class="html-italic">n</span> = 5.</p>
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<p>Heat map and coefficients for Pearson correlations among traits of <span class="html-italic">Cycas micronesica</span> plants growing in southern Guam and sorted by leaf:seed ratio categories.</p>
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<p>Heat map and coefficients for Pearson correlations among traits of <span class="html-italic">Cycas micronesica</span> plants growing in northern Guam and sorted by leaf:seed ratio categories.</p>
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<p>Heat map and coefficients for Pearson correlations among traits of <span class="html-italic">Cycas micronesica</span> plants growing in southern Guam and sorted by canopy openness categories.</p>
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<p>Heat map and coefficients for Pearson correlations among traits of <span class="html-italic">Cycas micronesica</span> plants growing in northern Guam and sorted by canopy openness categories.</p>
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<p>Heat map and coefficients for Pearson correlations among traits of <span class="html-italic">Cycas micronesica</span> plants growing in eastern Guam and sorted by manipulated leaf:seed ratio categories. Leaf <span class="html-italic">Pn</span> 1 and PSII 1 data from linear growth phase of seeds; leaf <span class="html-italic">Pn</span> 2 and PSII 2 data from linear gametophyte starch concentration accumulation phase.</p>
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<p>Heat map and coefficients for Pearson correlations among traits of <span class="html-italic">Cycas micronesica</span> plants growing in off-site garden in the Philippines and sorted by manipulated leaf:seed ratio categories. DW = dry weight, FW = fresh weight, DMC = dry matter content.</p>
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<p>Heat map and coefficients for Pearson correlations among traits of <span class="html-italic">Cycas edentata</span> plants growing in off-site garden in the Philippines and sorted by manipulated leaf:seed ratio categories. DW = dry weight, FW = fresh weight, DMC = dry matter content.</p>
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18 pages, 3388 KiB  
Article
The Molecular Mechanism Regulating Flavonoid Production in Rhododendron chrysanthum Pall. Against UV-B Damage Is Mediated by RcTRP5
by Fushuai Gong, Jinhao Meng, Hongwei Xu and Xiaofu Zhou
Int. J. Mol. Sci. 2024, 25(24), 13383; https://doi.org/10.3390/ijms252413383 - 13 Dec 2024
Viewed by 249
Abstract
Elevated levels of reactive oxygen species (ROS) are caused by ultraviolet B radiation (UV-B) stress. In response, plants strengthen their cell membranes, impeding photosynthesis. Additionally, UV-B stress initiates oxidative stress within the antioxidant defense system and alters secondary metabolism, particularly by increasing the [...] Read more.
Elevated levels of reactive oxygen species (ROS) are caused by ultraviolet B radiation (UV-B) stress. In response, plants strengthen their cell membranes, impeding photosynthesis. Additionally, UV-B stress initiates oxidative stress within the antioxidant defense system and alters secondary metabolism, particularly by increasing the quantity of UV-absorbing compounds such as flavonoids. The v-myb avian myeloblastosis viral oncogene homolog (MYB) transcription factor (TF) may participate in a plant’s response to UV-B damage through its regulation of flavonoid biosynthesis. In this study, we discovered that the photosynthetic activity of Rhododendron chrysanthum Pall. (R. chrysanthum) decreased when assessing parameters of chlorophyll (PSII) fluorescence parameters under UV-B stress. Concurrently, antioxidant system enzyme expression increased under UV-B exposure. A multi-omics data analysis revealed that acetylation at the K68 site of the RcTRP5 (telomeric repeat binding protein of Rhododendron chrysanthum Pall.) transcription factor was upregulated. This acetylation modification of RcTRP5 activates the antioxidant enzyme system, leading to elevated expression levels of peroxidase (POD), superoxide dismutase (SOD), and catalase (CAT). Upregulation is also observed at the K95 site of the chalcone isomerase (CHI) enzyme and the K178 site of the anthocyanidin synthase (ANS) enzyme. We hypothesize that RcTRP5 influences acetylation modifications of CHI and ANS in flavonoid biosynthesis, thereby indirectly regulating flavonoid production. This study demonstrates that R. chrysanthum can be protected from UV-B stress by accumulating flavonoids. This could serve as a useful strategy for enhancing the plant’s flavonoid content and provide a valuable reference for research on the metabolic regulation mechanisms of other secondary substances. Full article
(This article belongs to the Special Issue Abiotic Stress in Plant)
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<p>Trends in <span class="html-italic">R. chrysanthum’s</span> photosynthetic characteristics under UV-B stress: (<b>a</b>–<b>d</b>) real-time fluorescence actual, quantum yield of modulatable quenching in PSII, quantum yield of non-modulatable quenching in PSII, and photosynthetic efficiency of PSII, respectively. The data represent the mean ± SD for <span class="html-italic">n</span> = 3. A significant difference among treatments at <span class="html-italic">p</span> &lt; 0.05 is indicated by different letters (a, b).</p>
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<p>Flavonoid trends in six <span class="html-italic">R. chrysanthum</span> species in response to UV-B exposure: (<b>a</b>–<b>f</b>) gallocatechin, 6-methoxyflavone, kaempferol-3-O-arabinoside, naringenin chalcone, butin, and quercetin-3-O-arabinoside, respectively. The data represent the mean ± SD for <span class="html-italic">n</span> = 3. A significant difference among treatments at <span class="html-italic">p</span> &lt; 0.05 is indicated by different letters (a, b).</p>
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<p>Enrichment analysis of MYB transcription factors significantly altered by UV-B stress in the <span class="html-italic">R. chrysanthum</span>: (<b>a</b>) there were notable variations in the expression levels of eight MYB transcription factors in rhododendron that respond to UV-B stress; red indicates higher expression levels and green lower expression levels; (<b>b</b>) eight MYB transcription factors in the <span class="html-italic">R. chrysanthum</span> were analyzed for enrichment.</p>
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<p>Response of antioxidant enzyme system of <span class="html-italic">R. chrysanthum</span> to UV-B stress and correlation analysis with <span class="html-italic">RcTRP5</span>: (<b>a</b>–<b>c</b>) POD: peroxidase; CAT1: catalase isozyme 1; SODCC: superoxide dismutase; SODCP: superoxide dismutase; (<b>d</b>) the more pinkish the color, the stronger the positive correlation; the more bluish the color, the stronger the negative correlation. The data represent the mean ± SD for <span class="html-italic">n</span> = 3. A significant difference among treatments at <span class="html-italic">p</span> &lt; 0.05 is indicated by different letters (a, b). Asterisks denote treatments with significant changes (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p><span class="html-italic">R. chrysanthum</span> regulates the production of flavonoids: (<b>a</b>) data on metabolite content and enzyme gene expression were shown on a heat map after being normalized using the formula (Xi − min(x))/(max(x) − min(x)). Heatmaps with dark-red and dark-blue hues show changes in metabolite expression, with redder hues denoting higher expression and bluer hues denoting lower expression. Red and green heatmaps show changes in the expression of enzyme genes; redder hues denote higher expression, while greener hues denote lower expression; (<b>b</b>,<b>c</b>) the more pinkish the color, the stronger the positive correlation; the more bluish the color, the stronger the negative correlation. For <span class="html-italic">n</span> = 3, the data are the mean ± SD. Asterisks denote treatments with significant changes (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Examination of two important enzymes’ acetylation changes in the <span class="html-italic">R. chrysanthum</span> flavonoid biosynthesis pathway: (<b>a</b>) from left to right: the three-dimensional architectures of the CHI’s hydrophobic clusters, salt bridges, and acetylation modification sites; (<b>b</b>) from left to right: the three-dimensional architectures of the ANS’s hydrophobic clusters, salt bridges, and acetylation modification sites.</p>
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<p>Correlation analysis of <span class="html-italic">R. chrysanthum’s</span> antioxidant enzyme systems and photosynthetic parameters under UV-B stress. The stronger the association, the more pinkish the color, and the stronger the correlation, the more bluish the color. For <span class="html-italic">n</span> = 3, the data are the mean ± SD. Asterisks denote treatments with significant changes (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Diagram illustrating the defense mechanisms that <span class="html-italic">R. chrysanthum</span> uses against UV-B rays. <span class="html-italic">R. chrysanthum’s</span> enzyme systems and flavonoid biosynthesis pathways under normal light and UV-B stress are depicted in the left and right leaves, respectively. The damaging injuries and reactions to UV-B stress in <span class="html-italic">R. chrysanthum</span> are shown by the red lines. Acetylation modification sites and their upregulation are indicated by pink arrows. Inhibitory effects are indicated by blue lines.</p>
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18 pages, 33302 KiB  
Article
Comparative Transcriptomic Analysis and Candidate Gene Identification for Wild Rice (GZW) and Cultivated Rice (R998) Under Low-Temperature Stress
by Yongmei Yu, Dilin Liu, Feng Wang, Le Kong, Yanhui Lin, Leiqing Chen, Wenjing Jiang, Xueru Hou, Yanxia Xiao, Gongzhen Fu, Wuge Liu and Xing Huo
Int. J. Mol. Sci. 2024, 25(24), 13380; https://doi.org/10.3390/ijms252413380 - 13 Dec 2024
Viewed by 274
Abstract
Rice is a short-day thermophilic crop that originated from the low latitudes of the tropics and subtropics; it requires high temperatures for growth but is sensitive to low temperatures. Therefore, it is highly important to explore and analyze the molecular mechanism of cold [...] Read more.
Rice is a short-day thermophilic crop that originated from the low latitudes of the tropics and subtropics; it requires high temperatures for growth but is sensitive to low temperatures. Therefore, it is highly important to explore and analyze the molecular mechanism of cold tolerance in rice to expand rice planting areas. Here, we report a phenotypic evaluation based on low-temperature stress in indica rice (R998) and wild rice (GZW) and a comparative transcriptomic study conducted at six time points. After 7 days of low-temperature treatment at 10 °C, R998 exhibited obvious yellowing and greening of the leaves, while GZW exhibited high low-temperature resistance, and the leaves maintained their normal morphology and exhibited no yellowing; GZW has a higher survival rate. Principal component analysis (PCA) and cluster analysis of the RNA-seq data revealed that the difference in low-temperature resistance between the two cultivars was caused mainly by the difference in low-temperature treatment after 6 h. Differential expression analysis revealed 2615 unique differentially expressed genes (DEGs) in the R998 material, 1578 unique DEGs in the GZW material, 1874 unique DEGs between R998 and GZW, and 2699 DEGs that were differentially expressed not only between cultivars but also at different time points in the same material under low-temperature treatment. A total of 15,712 DEGs were detected and were significantly enriched in the phenylalanine metabolism, photosynthesis, plant hormone signal transduction, and starch and sucrose metabolism pathways. These 15,712 DEGs included 1937 genes encoding transcription factors (TFs), of which 10 have been identified with functional validation in previous studies. In addition, a gene regulatory network was constructed via weighted gene correlation network analysis (WGCNA), and 12 key genes related to low-temperature tolerance in rice were identified, including five genes encoding TFs, one of which was identified and verified in previous studies. These results provide a theoretical basis for an in-depth understanding of the molecular mechanism of low-temperature tolerance in rice and provide new genetic resources for the study of low-temperature tolerance in rice. Full article
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<p>Phenotypic and physiological indices for cold tolerance in R998 and GZW rice. (<b>a</b>) Phenotypes of rice treated at 25 °C or 10 °C for 7 days followed by 25 °C for an additional 7 days. (<b>b</b>) Changes in the physiological indices of R998 and GZW under low-temperature stress; the results are presented as the means ± SDs (n = 3, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Correlation analysis and PCA of 26 RNA-seq samples. (<b>a</b>) Correlation cluster analysis of 36 RNA-seq samples. (<b>b</b>) PCA of 36 RNA-seq samples.</p>
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<p>Differential expression and enrichment analysis of GZW. (<b>a</b>) Number of genes whose expression was upregulated or downregulated at different time points under low-temperature stress in GZW. (<b>b</b>) Venn diagram of the number of common and specific DEGs at different time points of low-temperature stress in GZW. (<b>c</b>) GO enrichment analysis of all DEGs at different time points under low-temperature stress in GZW. (<b>d</b>) KEGG enrichment analysis of all DEGs at different time points under low-temperature stress in GZW. (<b>e</b>) Line graph of the cluster analysis of all DEGs at different time points under low-temperature stress in GZW. The red numbers represent the numbers of DEGs and TFs in each cluster.</p>
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<p>Differential expression and enrichment analysis of R998. (<b>a</b>) The number of genes whose expression increased or decreased at different time points under low-temperature stress in R998. (<b>b</b>) Venn diagram of the numbers of common and specific DEGs at different time points under low-temperature stress in R998. (<b>c</b>) GO enrichment analysis of all DEGs at different time points of low-temperature stress in R998. (<b>d</b>) KEGG enrichment analysis of all DEGs at different time points under low-temperature stress in R998. (<b>e</b>) Line graph of the cluster analysis of all DEGs at different time points under low-temperature stress in R998. The red numbers represent the numbers of DEGs and TFs in each cluster.</p>
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<p>Analysis of differential expression and enrichment between GZW and R998. (<b>a</b>) Number of genes whose expression differed between GZW and R998. (<b>b</b>) Venn diagram of the numbers of common and specific DEGs between GZW and R998. (<b>c</b>) GO enrichment analysis of all DEGs between GZW and R998. (<b>d</b>) KEGG enrichment analysis of all DEGs between GZW and R998.</p>
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<p>Heatmap of the numbers and expression patterns of common and unique DEGs between GZW and R998 and at different time points of low-temperature stress in the same material. (<b>a</b>) Venn diagram of common and specific DEGs between the GZW and R998 cultivars. (<b>b</b>) Heatmap of the expression patterns of DEGs uniquely expressed by R998. (<b>c</b>) Heatmap of the unique DEG expression patterns of GZW. (<b>d</b>) Heatmap of the unique DEG expression patterns between R998 and GZW. (<b>e</b>) Heatmap of DEG expression patterns common to R998 and GZW.</p>
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<p>Differential expression of TFs and expression profile analysis. (<b>a</b>) Heatmap of the proportions of the top 10 TFs; the area represents the number of TFs, and different colors represent different TFs. (<b>b</b>) Clustering heatmap of TFs, with identified and validated TFs on the right.</p>
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<p>WGCNA and candidate gene mining. (<b>a</b>) Cluster dendrogram of all DEGs via WGCNA. (<b>b</b>) Correlation heatmap of the module with GZW and R998 at different time points of low-temperature stress. (<b>c</b>) Gene coexpression networks of significantly related modules.</p>
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<p>Analysis of the expression patterns of 12 candidate genes after low-temperature stress. The error bars represent the means of triplicates ± SEs (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Scatter plot of the correlation between the qRT–PCR and RNA-seq data.</p>
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19 pages, 2742 KiB  
Article
NgLst8 Coactivates TOR Signaling to Activate Photosynthetic Growth in Nannochloropsis gaditana
by Zhengying Zhang, Shu Yang, Yanyan Li, Dian Xie, Guobin Chen, Jiaxu Ren, Hongmei Zhu and Hantao Zhou
Microorganisms 2024, 12(12), 2574; https://doi.org/10.3390/microorganisms12122574 - 13 Dec 2024
Viewed by 257
Abstract
The target of rapamycin (TOR) serves as a central regulator of cell growth, coordinating anabolic and catabolic processes in response to nutrient availability, growth factors, and energy supply. Activation of TOR has been shown to promote photosynthesis, growth, and development in yeast, animals, [...] Read more.
The target of rapamycin (TOR) serves as a central regulator of cell growth, coordinating anabolic and catabolic processes in response to nutrient availability, growth factors, and energy supply. Activation of TOR has been shown to promote photosynthesis, growth, and development in yeast, animals, and plants. In this study, the complete cDNA sequence of the Lst8 gene was obtained from Nannochloropsis gaditana. The structure of N. gaditana LST8 comprises a typical WD40 repeat sequence, exhibiting high sequence similarity to several known LST8 proteins. By overexpressing the Lst8 gene in N. gaditana, we constructed the NgLst8 transgenic algal strain and measured its photosynthetic activity and growth. We observed that an increase in LST8 abundance promotes the expression of TOR-related kinase, thereby enhancing photosynthetic growth. Transcriptome analysis further elucidated the response mechanism of elevated Lst8 abundance in relation to photosynthesis. Our findings indicate that increased Lst8 expression activates ABC transporter proteins and the MAPK signaling pathway, which regulate the transmembrane transport of sugars and other metabolites, integrate photosynthesis, sugar metabolism, and energy signaling, and modulate energy metabolism in algal cells through interactions with the TOR signaling pathway. Full article
(This article belongs to the Section Microbial Biotechnology)
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Figure 1

Figure 1
<p>Construction and validation of the NgLst8 transformants. (<b>A</b>) Modeling of the NgLst8 transformants. (<b>B</b>) Electrophoretic verification results of the <span class="html-italic">Lst8</span> and <span class="html-italic">zeocin</span> fragments. NC: negative control (A vector lacking the target gene LST8, containing only the zeocin resistance gene, to serve as a negative control). (<b>C</b>) The RT-qPCR of NgLst8 and WT strains. (<b>D</b>) The Western Blotting of NgLst8 and WT strains. **** <span class="html-italic">p</span> &lt; 0.0001, *** <span class="html-italic">p</span> &lt; 0.001, ** <span class="html-italic">p</span> &lt; 0.01, * <span class="html-italic">p</span> &lt; 0.05. The error bars represent the standard deviation calculated from three replicate measurements.</p>
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<p>Growth characteristics of NgLst8 strains. (<b>A</b>) Growth profile of the WT and the NgLst8 strains. (<b>B</b>) Growth ratios from day 3 to day 4. (<b>C</b>) Biomass on day 12 for NgLst8 and WT. The error bars represent the standard deviations derived from three biological replicates. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Chlorophyll fluorescence parameters of PSII of NgLst8 and WT strains. (<b>A</b>) Fv/Fm of NgLst8 and WT strains. (<b>B</b>) Y(II) of NgLst8 and WT strains. (<b>C</b>) Relative photosynthetic electron transfer efficiency (rETR), rETRmax, and IK of WT and NgLst8 strains. (<b>D</b>) Photosynthetic oxygen release rate of WT and NgLst8 strains. *** <span class="html-italic">p</span> &lt; 0.001, ** <span class="html-italic">p</span> &lt; 0.01, * <span class="html-italic">p</span> &lt; 0.05; ns indicates <span class="html-italic">p</span> &gt; 0.05. The error bars indicate the standard error of three biological replicates.</p>
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<p>Chloroplast morphology and cell grain size of NgLst8. (<b>A</b>) Transmission electron microscopy of WT and NgLst8 strains on day 3. White arrows point to lipid droplets and black arrows to chloroplast. (<b>B</b>) FSC of WT and NgLst8 strains on day 3. (<b>C</b>) Diameter of algal cells after three days of WT and NgLst8 strains. **** <span class="html-italic">p</span> &lt; 0.0001, *** <span class="html-italic">p</span> &lt; 0.001, ** <span class="html-italic">p</span> &lt; 0.01. The error bars indicate the standard error of three biological replicates.</p>
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<p>Transcription of WT and NgLst8 strains’ related genes. (<b>A</b>) Transcription of WT and NgLst8 strains’ photosynthesis-related genes. (<b>B</b>) Transcription of WT and NgLst8 strains’ phosphatidylinositol pathways. *** <span class="html-italic">p</span> &lt; 0.001, ** <span class="html-italic">p</span> &lt; 0.01, * <span class="html-italic">p</span> &lt; 0.05. The error bars indicate the standard error of three biological replicates.</p>
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<p>The transcriptomic analysis of WT and NgLst8 strains. (<b>A</b>) KEGG enrichment analysis of WT and NgLst8 strains. (<b>B</b>) Heatmap of differential gene enrichment pathways.</p>
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<p>The transcriptome reveals a model in which overexpression of the <span class="html-italic">Lst8</span> gene regulates photosynthetic growth in <span class="html-italic">N. gaditana.</span> The red arrows indicate upregulation of expression. Black arrows represent direct effects, gray arrows indicate interactions, and blue arrows denote growth-related effects.</p>
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7 pages, 157 KiB  
Perspective
Emerging Alternatives to Mitigate Agricultural Fresh Water and Climate/Ecosystem Issues: Agricultural Revolutions
by Dennis M. Bushnell
Water 2024, 16(24), 3589; https://doi.org/10.3390/w16243589 - 13 Dec 2024
Viewed by 374
Abstract
Fresh-water food production/agriculture for both plants and animals utilizes some 70% of the planets’ fresh water, produces some 26% of greenhouse gas emissions and has a longish list of other societal-related issues. Given the developing and extant shortages of arable land, fresh water [...] Read more.
Fresh-water food production/agriculture for both plants and animals utilizes some 70% of the planets’ fresh water, produces some 26% of greenhouse gas emissions and has a longish list of other societal-related issues. Given the developing and extant shortages of arable land, fresh water and food, along with climate/ecosystem issues, there is a need to greatly reduce these adverse effects of fresh-water agriculture. There are, especially since the advent of the 4th Agricultural Revolution, a number of major frontier technologies and functionality changes along with prospective alternatives which could, when combined and collectivized in various ways, massively improve the practices, adverse impacts and outlook of food production. These include cellular/factory agriculture; photosynthesis alternatives; a shift to off-grids and roads/back-to-the-future, do-it-yourself living (aka de-urbanization); cultivation of halophytes on wastelands using saline water; insects; frontier energetics; health-related market changes; and vertical farms/hydroponics/aeroponics. Shifting to these and other prospective alternatives would utilize far less arable land and fresh water, produce far less greenhouse gases and reduce food costs and pollution while increasing food production. Full article
17 pages, 3424 KiB  
Article
Role of the Foliar Endophyte Colletotrichum in the Resistance of Invasive Ageratina adenophora to Disease and Abiotic Stress
by Ailing Yang, Yuxuan Li, Zhaoying Zeng and Hanbo Zhang
Microorganisms 2024, 12(12), 2565; https://doi.org/10.3390/microorganisms12122565 - 12 Dec 2024
Viewed by 293
Abstract
Plant-associated fungi often drive plant invasion success by increasing host growth, disease resistance, and tolerance to environmental stress. A high abundance of Colletotrichum asymptomatically accumulated in the leaves of Ageratina adenophora. In this study, we aimed to clarify whether three genetically distinct [...] Read more.
Plant-associated fungi often drive plant invasion success by increasing host growth, disease resistance, and tolerance to environmental stress. A high abundance of Colletotrichum asymptomatically accumulated in the leaves of Ageratina adenophora. In this study, we aimed to clarify whether three genetically distinct endophytic Colletotrichum isolates (AX39, AX115, and AX198) activate invasive plant defenses against disease and environmental stress. We observed that, in the absence of pathogen attack and environmental stress, the foliar endophyte Colletotrichum reduced photosynthesis-related physiological indicators (i.e., chlorophyll content and soluble sugar content), increased resistance-related indicators (i.e., total phenolic (TP) and peroxidase (POD) activity), and decreased the biomass of A. adenophora. However, endophytic Colletotrichum strains exhibit positive effects on resistance to certain foliar pathogen attacks. Strains AX39 and AX115 promoted but AX198 attenuated the pathogenic effects of pathogen strains G56 and Y122 (members of Mesophoma ageratinae). In contrast, AX39 and AX115 weakened, but AX198 had no effect on, the pathogenic effect of the pathogen strain S188 (Mesophoma speciosa; Didymellaceae family). We also found that endophytes increase the biomass of A. adenophora under drought or nutrient stress. Strain AX198 significantly increased stem length and chlorophyll content under drought stress. Strain AX198 significantly increased the aboveground dry weight, AX115 increased the stem length, and AX39 significantly increased the chlorophyll content under nutrient stress. Our results revealed that there are certain positive effects of foliar Colletotrichum endophytes on A. adenophora in response to biotic and abiotic stresses, which may be beneficial for its invasion. Full article
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Figure 1
<p>Physiological indices of <span class="html-italic">A. adenophora</span> inoculated with endophytic <span class="html-italic">Colletotrichum</span> strains. (<b>a</b>) Chlorophyll content, (<b>b</b>) soluble sugar content, (<b>c</b>) total phenol content, and (<b>d</b>) peroxidase activity. Dots with different colors represent the raw data of each sample inoculated with the <span class="html-italic">Colletotrichum</span> AX39, AX115, and AX198 strains. The RI represents the response index, where the negative RI in panels (<b>a</b>,<b>b</b>) indicates reduced chlorophyll content and soluble sugar content in the treatment with <span class="html-italic">Colletotrichum</span> spp. infection compared with the control without <span class="html-italic">Colletotrichum</span> spp. infection. The positive RIs in panels (<b>c</b>,<b>d</b>) indicate increased total phenol content and peroxidase POD activity in the treatment with <span class="html-italic">Colletotrichum</span> spp. infection compared with the control without <span class="html-italic">Colletotrichum</span> spp. infection. Nonparametric Mann–Whitney U tests or independent sample T tests were used to identify the differences between each treatment group and the control group (* &lt;0.05, *** &lt;0.001). Post hoc comparisons were performed via Duncan’s test for equal variance and Dunnett’s test for unequal variance (T3 test) to determine whether the difference in the RI was significant among the treatments inoculated with AX39, AX115, and AX198, with different letters indicating significant differences. The error bars are the standard errors.</p>
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<p>Biomass of <span class="html-italic">A. adenophora</span> inoculated with endophytic <span class="html-italic">Colletotrichum</span> strains. (<b>a</b>) Aboveground parts, (<b>b</b>) underground parts, (<b>c</b>) branch number, (<b>d</b>) stem length, (<b>e</b>) root length, and (<b>f</b>) root–to-shoot ratio. Dots with different colors represent the raw data of each sample inoculated with the <span class="html-italic">Colletotrichum</span> AX39, AX115, and AX198 strains. A negative RI indicates a reduced biomass of <span class="html-italic">A. adenophora</span> in the experimental treatment with <span class="html-italic">Colletotrichum</span> spp. infection compared with that in the control without <span class="html-italic">Colletotrichum</span> spp. infection. Nonparametric Mann–Whitney U tests or independent sample T tests were used to identify the differences between each treatment group and the control group (* &lt;0.05, ** &lt;0.01, *** &lt;0.001). Post hoc comparisons were performed via Duncan’s test for equal variance and Dunnett’s test for unequal variance (T3 test) to determine whether the difference in the RI was significant among the treatments inoculated with AX39, AX115, and AX198, with different letters indicating significant differences. The error bars are the standard errors.</p>
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<p>LMA (dry weight per unit area) of <span class="html-italic">A. adenophora</span> inoculated with endophytic <span class="html-italic">Colletotrichum</span> strains. (<b>a</b>) The second pair of leaves, (<b>b</b>) the fifth pair of leaves. Dots with different colors represent the raw data of each sample inoculated with the <span class="html-italic">Colletotrichum</span> AX39, AX115, and AX198 strains. A negative RI indicates a reduced LMA of <span class="html-italic">A. adenophora</span> in the experimental treatment with <span class="html-italic">Colletotrichum</span> spp. infection compared with that in the control without <span class="html-italic">Colletotrichum</span> spp. infection. Nonparametric Mann–Whitney U tests or independent sample T tests were used to identify the differences between each treatment group and the control group (* &lt;0.05). Post hoc comparisons were performed via Duncan’s test for equal variance and Dunnett’s test for unequal variance (T3 test) to determine whether the difference in the RI was significant among the different treatments inoculated with AX39, AX115, and AX198, with different letters indicating significant differences. The error bars are the standard errors.</p>
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<p>The asymptomatic leaves of <span class="html-italic">A. adenophora</span> plants inoculated with a <span class="html-italic">Colletotrichum</span> spore mixture (<b>a</b>) and wounded and inoculated with agar discs of <span class="html-italic">Colletotrichum</span> (<b>b</b>). “CK” represents the control group without <span class="html-italic">Colletotrichum</span> inoculation.</p>
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<p>Pathogenicity effects of inoculating endophyte <span class="html-italic">Colletotrichum</span> strains on <span class="html-italic">A. adenophora</span> after challenge with the (<b>a</b>) pathogen G56, (<b>b</b>) pathogen Y122, and (<b>c</b>) pathogen S188. Dots with different colors represent the raw data of each sample inoculated with <span class="html-italic">the Colletotrichum</span> AX39, AX115, and AX198 strains. The specific leaf spot area and morphology are shown in (<b>d</b>); scale bar = 10 mm, and “CK” represents the control group without <span class="html-italic">Colletotrichum</span> inoculation. Nonparametric Mann–Whitney U tests or independent sample T tests were used to identify the differences between each treatment group and the control group (* &lt;0.05, *** &lt;0.001). Post hoc comparisons were performed via Duncan’s test for equal variance and Dunnett’s test for unequal variance (T3 test) to determine whether the difference in the RI was significant among the different treatments inoculated with AX39, AX115, and AX198, with different letters indicating significant differences. The error bars are the standard errors.</p>
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<p>Biomass and chlorophyll content of <span class="html-italic">A. adenophora</span> inoculated with endophyte <span class="html-italic">Colletotrichum</span> strains under normal conditions and drought stress. (<b>a</b>) Aboveground parts, (<b>b</b>) underground parts, (<b>c</b>) root/shoot ratio, (<b>d</b>) stem length, (<b>e</b>) root length, (<b>f</b>) branch number, and (<b>g</b>) chlorophyll content. A positive RI indicates an increased biomass of <span class="html-italic">A. adenophora</span> in the drought stress (−W) treatment with <span class="html-italic">Colletotrichum</span> strain (AX39, AX115, or AX198) inoculation compared with that without <span class="html-italic">Colletotrichum</span> inoculation. The formula is as follows: (treatment_Wcontrol_W)/control_W. Nonparametric Mann–Whitney U tests or independent sample T tests were used to identify the differences between each drought stress treatment group and the normal treatment group (** &lt;0.01). Post hoc comparisons were performed via Duncan’s test for equal variance and Dunnett’s test for unequal variance (T3 test) to show that the differences in the RIs were significant among the treatments inoculated with strains AX39, AX115, and AX198, with different letters indicating significant differences. The error bars are the standard errors.</p>
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<p>Growth effects of <span class="html-italic">A. adenophora</span> inoculated with endophyte <span class="html-italic">Colletotrichum</span> strains under nutrient stress and drought stress. Individuals of <span class="html-italic">A. adenophora</span> were inoculated with AX39, AX115, or AX198 and grown for one month in a plant growth chamber under nutrient stress (−N) and drought (−W).</p>
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<p>Biomass and chlorophyll content of <span class="html-italic">A. adenophora</span> plants inoculated with endophytic <span class="html-italic">Colletotrichum</span> strains under normal conditions and nutrient stress conditions. (<b>a</b>) Aboveground parts, (<b>b</b>) underground parts, (<b>c</b>) root/shoot ratio, (<b>d</b>) stem length, (<b>e</b>) root length, (<b>f</b>) branch number, and (<b>g</b>) chlorophyll content. A positive RI indicates an increased biomass of <span class="html-italic">A. adenophora</span> in the nutrient stress (−N) treatment with <span class="html-italic">Colletotrichum</span> strain (AX39, AX115, or AX198) inoculation compared with that without <span class="html-italic">Colletotrichum</span> inoculation. The formula is as follows: (treatment_Ncontrol_N)/control_N. Nonparametric Mann–Whitney U tests or independent sample T tests were used to identify the differences between each nutrient stress treatment and the normal treatment (* &lt;0.05,*** &lt;0.001). Post hoc comparisons were performed via Duncan’s test for equal variance and Dunnett’s test for unequal variance (T3 test) to show that the differences in the RIs were significant among the treatments inoculated with strains AX39, AX115, and AX198, with different letters indicating significant differences. The error bars are the standard errors.</p>
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