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15 pages, 9746 KiB  
Article
Saussurea involucrata SiLEA5 Enhances Tolerance to Drought Stress in Solanum lycopersicum
by Xiaoyan Liu, Aowei Li, Guanghong Luo and Jianbo Zhu
Foods 2024, 13(22), 3641; https://doi.org/10.3390/foods13223641 - 15 Nov 2024
Viewed by 161
Abstract
Drought adversely affects plant growth, which leads to reduced crop yields and exacerbates food insecurity. Late embryogenesis abundant (LEA) proteins are crucial for plants’ responses to abiotic stresses. This research further investigates the role of SiLEA5 by utilizing transgenic tomatoes under drought stress. [...] Read more.
Drought adversely affects plant growth, which leads to reduced crop yields and exacerbates food insecurity. Late embryogenesis abundant (LEA) proteins are crucial for plants’ responses to abiotic stresses. This research further investigates the role of SiLEA5 by utilizing transgenic tomatoes under drought stress. The expression of SiLEA5 was upregulated under drought and abscisic acid (ABA) treatment, resulting in decreased electrolyte leakage and malondialdehyde content, alongside increased levels of osmotic regulators and antioxidant enzyme activity. These biochemical alterations reduce oxidative damage and enhance drought resistance. qRT-PCR analysis revealed the upregulation of ABA signaling genes and key enzymes involved in proline biosynthesis (P5CS) and dehydrin (DHN) synthesis under drought stress. Additionally, overexpression of SiLEA5 increased the net photosynthetic rate (Pn) and fruit yield of tomatoes by regulating stomatal density and aperture. These findings suggest that SiLEA5 may be a potential target for improving drought tolerance in tomatoes and other crops. Full article
(This article belongs to the Section Plant Foods)
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Figure 1

Figure 1
<p>Phenotypes of wild-type and transgenic tomato under normal growth. (<b>A</b>,<b>B</b>) Growth phenotypes of wild-type and transgenic tomato. (<b>C</b>) Relative growth rate (RGR). Data are means ± SD of three replicates. (* <span class="html-italic">p</span> &lt; 0.05, and ** <span class="html-italic">p</span> &lt; 0.01 for comparisons between the transgenic lines and wild-type plants by Student’s <span class="html-italic">t</span>-tests). Bar = 3 cm.</p>
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<p>Biochemical analysis of tomato under drought stress. (<b>A</b>–<b>D</b>) Growth phenotype of tomato. (<b>E</b>) Changes in leaf water loss rate. (<b>F</b>) Determination of root activity. (<b>G</b>) Biochemical changes under different drought stress levels. (<b>H</b>) Biochemical changes after rehydration. Data are means ± SD of three 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, and **** <span class="html-italic">p</span> &lt; 0.0001 for comparisons between the transgenic lines and wild-type plants by Student’s <span class="html-italic">t</span>-tests). Bar = 8 cm.</p>
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<p><span class="html-italic">SiLEA5</span> increases stomatal sensitivity to drought stress and ABA. (<b>A</b>) Stomatal changes in tomato under drought stress and ABA treatment. (<b>B</b>,<b>C</b>) Stomatal length and width (µm). (<b>D</b>) Stomatal aperture (µm). (<b>E</b>) Stomatal density (stoma·mm<sup>−2</sup>). Error bars, mean ± SD. The asterisks indicate a statistically significant difference (two-tailed Student’s <span class="html-italic">t</span>-test, * <span class="html-italic">p</span> &lt; 0.05, and ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, and **** <span class="html-italic">p</span> &lt; 0.0001).</p>
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<p>Analysis of photosynthetic capacity of wild-type and transgenic tomatoes under drought stress. (<b>A</b>) Leaf phenotype. (<b>B</b>) Leaf length. (<b>C</b>) Leaf width. (<b>D</b>) Ratio of leaf length to width. (<b>E</b>) Intercellular CO<sub>2</sub> concentration (Ci). (<b>F</b>) Net photosynthesis rate (Pn). (<b>G</b>) Stomatal conductance (Gs). (<b>H</b>) Transpiration rate (Tr). (<b>I</b>) Water Use Efficiency (WUE). (<b>J</b>) Maximum photochemical efficiency of Photosystem II (Fv/Fm). (<b>K</b>) Non-photochemical quenching (Npq). (<b>L</b>) Photochemical quenching coefficient (qp). Error bars, mean ± SD. Each red dot represents a sample. The asterisks indicate a statistically significant difference (two-tailed Student’s <span class="html-italic">t</span>-test, * <span class="html-italic">p</span> &lt; 0.05, and ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, and **** <span class="html-italic">p</span> &lt; 0.0001).</p>
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<p>Statistics of growth indicators, yield, and quality of wild-type and transgenic lines. (<b>A</b>–<b>D</b>) Measurement results of tomato growth indicators. (<b>E</b>–<b>G</b>) Tomato yield and quality comparison. (<b>H</b>) Fruit morphology of wild-type and transgenic tomato. Error bars, mean ± SD. The asterisks indicate a statistically significant difference (two-tailed Student’s <span class="html-italic">t</span>-test, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, and **** <span class="html-italic">p</span> &lt; 0.0001).</p>
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<p>Expression analysis of relevant genes in transgenic and wild-type tomato. (<b>A</b>) Relative expression levels of the <span class="html-italic">ABI2</span> gene. (<b>B</b>) Relative expression level of <span class="html-italic">PYL8</span> gene. (<b>C</b>) Relative expression level of <span class="html-italic">SRK2C</span> gene. (<b>D</b>) Relative expression level of <span class="html-italic">DHN</span> gene. (<b>E</b>) Relative expression level of <span class="html-italic">P5CS</span> gene. (<b>F</b>) Relative expression level of <span class="html-italic">DREB1A</span> gene. Three independent biological replicates were performed (<span class="html-italic">n</span> = 3). Error bars, mean ± SD. The asterisks indicate a statistically significant difference (two-tailed Student’s <span class="html-italic">t</span>-test, *** <span class="html-italic">p</span> &lt; 0.001, and **** <span class="html-italic">p</span> &lt; 0.0001).</p>
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<p>Schematic representation of the role of <span class="html-italic">SiLEA5</span> gene in drought stress regulation in tomatoes.</p>
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31 pages, 2280 KiB  
Review
Drought Tolerance in Plants: Physiological and Molecular Responses
by Mostafa Haghpanah, Seyyedhamidreza Hashemipetroudi, Ahmad Arzani and Fabrizio Araniti
Plants 2024, 13(21), 2962; https://doi.org/10.3390/plants13212962 - 23 Oct 2024
Viewed by 789
Abstract
Drought, a significant environmental challenge, presents a substantial risk to worldwide agriculture and the security of food supplies. In response, plants can perceive stimuli from their environment and activate defense pathways via various modulating networks to cope with stress. Drought tolerance, a multifaceted [...] Read more.
Drought, a significant environmental challenge, presents a substantial risk to worldwide agriculture and the security of food supplies. In response, plants can perceive stimuli from their environment and activate defense pathways via various modulating networks to cope with stress. Drought tolerance, a multifaceted attribute, can be dissected into distinct contributing mechanisms and factors. Osmotic stress, dehydration stress, dysfunction of plasma and endosome membranes, loss of cellular turgidity, inhibition of metabolite synthesis, cellular energy depletion, impaired chloroplast function, and oxidative stress are among the most critical consequences of drought on plant cells. Understanding the intricate interplay of these physiological and molecular responses provides insights into the adaptive strategies plants employ to navigate through drought stress. Plant cells express various mechanisms to withstand and reverse the cellular effects of drought stress. These mechanisms include osmotic adjustment to preserve cellular turgor, synthesis of protective proteins like dehydrins, and triggering antioxidant systems to counterbalance oxidative stress. A better understanding of drought tolerance is crucial for devising specific methods to improve crop resilience and promote sustainable agricultural practices in environments with limited water resources. This review explores the physiological and molecular responses employed by plants to address the challenges of drought stress. Full article
(This article belongs to the Special Issue Drought Responses and Adaptation Mechanisms in Plants)
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Figure 1
<p>Physiological effects of drought stress on plants and the outcomes that cause growth and yield reduction.</p>
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<p>Plants employ various strategies, such as tolerance, recovery, avoidance, and escape, to cope with drought.</p>
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<p>Molecular mechanisms of drought stress tolerance in plants. Abbreviations: ABA, abscisic acid; PYR1/PYL, pyrabactin resistance/PYR1-like; RCAR, Regulatory Component of ABA Receptor; PP2C, type 2C protein-phosphatase; SnRK2, sucrose non-fermenting 1-related protein kinase subfamily 2; ABF, ABA-responsive element binding factors; RAV1, Related to ABI3/VP1; DREB2, dehydration responsive element-binding protein 2; NAC, named based on its three domains: NAM, ATAF1,2 and CUC2; AREB, abscisic acid–responsive element binding protein; DRE/CRT, Dehydration Responsive Element/C-repeat Binding Factor; NACR, NAC recognition sequence; ABRE, ABA-responsive element; LEAs, Late embryogenesis-abundant; DHNs, Dehydrins, SMPs, seed maturation protein; TIPs, tonoplast intrinsic proteins; NIPs, nodulin 26-like intrinsic proteins, DSPs, desiccation-stress proteins, RAPs, resistance-associated proteins, Ca<sup>2+</sup>, calcium ion; ROS, reactive oxygen species; cGMP, 3’,5’-cyclic guanosine monophosphate; cAMP, cyclic adenosine monophosphate; MAPK, mitogen-activated protein kinase; CDPK, Calcium-dependent protein kinase; WRKY, WRKY transcription factors; MYB, MYB transcription factors; AP/EREAP, Activator protein/element of the apoptosis promoting; CAT, catalase; SOD, superoxide dismutase; APX, ascorbate peroxidase; POD, peroxidase; GR, glutathione reductase; GPX, Glutathione Peroxidase.</p>
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22 pages, 3096 KiB  
Article
Ascophyllum nodosum Extract Improves Olive Performance Under Water Deficit Through the Modulation of Molecular and Physiological Processes
by Maria Celeste Dias, Rui Figueiras, Marta Sousa, Márcia Araújo, José Miguel P. Ferreira de Oliveira, Diana C. G. A. Pinto, Artur M. S. Silva and Conceição Santos
Plants 2024, 13(20), 2908; https://doi.org/10.3390/plants13202908 - 17 Oct 2024
Viewed by 532
Abstract
The olive tree is well adapted to the Mediterranean climate, but how orchards based on intensive practices will respond to increasing drought is unknown. This study aimed to determine if the application of a commercial biostimulant improves olive tolerance to drought. Potted plants [...] Read more.
The olive tree is well adapted to the Mediterranean climate, but how orchards based on intensive practices will respond to increasing drought is unknown. This study aimed to determine if the application of a commercial biostimulant improves olive tolerance to drought. Potted plants (cultivars Arbequina and Galega) were pre-treated with an extract of Ascophyllum nodosum (four applications, 200 mL of 0.50 g/L extract per plant), and were then well irrigated (100% field capacity) or exposed to water deficit (50% field capacity) for 69 days. Plant height, photosynthesis, water status, pigments, lipophilic compounds, and the expression of stress protective genes (OeDHN1—protective proteins’ dehydrin; OePIP1.1—aquaporin; and OeHSP18.3—heat shock proteins) were analyzed. Water deficit negatively affected olive physiology, but the biostimulant mitigated these damages through the modulation of molecular and physiological processes according to the cultivar and irrigation. A. nodosum benefits were more expressive under water deficit, particularly in Galega, promoting height (increase of 15%) and photosynthesis (increase of 34%), modulating the stomatal aperture through the regulation of OePIP1.1 expression, and keeping OeDHN1 and OeHSP18.3 upregulated to strengthen stress protection. In both cultivars, biostimulant promoted carbohydrate accumulation and intrinsic water-use efficiency (iWUE). Under good irrigation, biostimulant increased energy availability and iWUE in Galega. These data highlight the potential of this biostimulant to improve olive performance, providing higher tolerance to overcome climate change scenarios. The use of this biostimulant can improve the establishment of younger olive trees in the field, strengthen the plant’s capacity to withstand field stresses, and lead to higher growth and crop productivity. Full article
(This article belongs to the Special Issue Drought Responses and Adaptation Mechanisms in Plants)
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Figure 1
<p>Leaf relative water content (RWC) (<b>A</b>,<b>B</b>) and plant height increment (<b>C</b>,<b>D</b>) in <span class="html-italic">O. europaea</span> plants of the treatments C (well-watered), BC (biostimulant + well-watered), S (water deficit), and BS (biostimulant + water deficit). Bars represent mean ± standard error (<span class="html-italic">n</span> = 5–10). The effect of the factor irrigation (I), factor biostimulant (B), and the interaction between the factor irrigation and biostimulant (I × B) are presented, and when the effect of each factor or the interaction is statistically significant (<span class="html-italic">p</span> ≤ 0.05), it appears in bold. Different letters indicate statistically significant differences (<span class="html-italic">p</span> ≤ 0.05). Significant differences among I × B refer to differences between C, BC, S, and BS treatments. Significant differences among the factor I refer to differences between 100% (treatments C and BC) and 50% irrigation (treatments S and BS). Significant differences among the factor B are shown in a chart at the top of the corresponding graph, and statistic letters refer to treatments without biostimulant (0: treatments C and S) and treatments with biostimulant (B: treatments BC and BS).</p>
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<p>Net CO<sub>2</sub>-assimilation rate (<b>A</b>,<b>B</b>), stomatal conductance (<b>C</b>,<b>D</b>), ratio of intercellular CO<sub>2</sub> and extracellular CO<sub>2</sub> concentration (Ci/Ca) (<b>E</b>,<b>F</b>), and intrinsic water-use efficiency (<b>G</b>,<b>H</b>) in <span class="html-italic">O. europaea</span> plants of the treatments C (well-watered), BC (biostimulant + well-watered), S (water deficit), and BS (biostimulant + water deficit). Bars represent mean ± standard error (<span class="html-italic">n</span> = 6–9). The effect of the factor irrigation (I), factor biostimulant (B), and the interaction between the factor irrigation and biostimulant (I × B) are presented, and when the effect of each factor or the interaction is statistically significant (<span class="html-italic">p</span> ≤ 0.05), it appears in bold. Different letters indicate statistically significant differences (<span class="html-italic">p</span> ≤ 0.05). Significant differences among I × B refer to differences between C, BC, S, and BS treatments. Significant differences among the factor I refer to differences between 100% (treatments C and BC) and 50% irrigation (treatments S and BS). Significant differences among the factor B are shown in a chart at the top of the corresponding graph, and statistic letters refer to treatments without biostimulant (0: treatments C and S) and treatments with biostimulant (B: treatments BC and BS).</p>
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<p>Maximum efficiency of PSII (F<sub>v</sub>/F<sub>m</sub>) (<b>A</b>,<b>B</b>), effective efficiency of PSII (Φ<sub>PSII</sub>) (<b>C</b>,<b>D</b>), efficiency of excitation energy capture by open PSII reaction centers (F<sub>v</sub>′/F<sub>m</sub>′) (<b>E</b>,<b>F</b>), photochemical quenching (qP) (<b>G</b>,<b>H</b>), and non-photochemical quenching (NPQ) (<b>I</b>,<b>J</b>) in <span class="html-italic">O. europaea</span> plants of the treatments C (well-watered), BC (biostimulant + well-watered), S (water deficit), and BS (biostimulant + water deficit). Bars represent mean ± standard error (<span class="html-italic">n</span> = 5–10). The effect of the factor irrigation (I), factor biostimulant (B), and the interaction between the factor irrigation and biostimulant (I × B) are presented, and when the effect of each factor or the interaction is statistically significant (<span class="html-italic">p</span> ≤ 0.05), it appears in bold. Different letters indicate statistically significant differences (<span class="html-italic">p</span> ≤ 0.05). Significant differences among I × B refer to differences between C, BC, S, and BS treatments. Significant differences among the factor I refer to differences between 100% (treatments C and BC) and 50% irrigation (treatments S and BS).</p>
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<p>Chlorophyll <span class="html-italic">a</span> (<b>A</b>,<b>B</b>) and <span class="html-italic">b</span> (<b>C</b>,<b>D</b>), and carotenoid (<b>E</b>,<b>F</b>) contents in <span class="html-italic">O. europaea</span> plants of the treatments C (well-watered), BC (biostimulant + well-watered), S (water deficit), and BS (biostimulant + water deficit). Bars represent mean ± standard error (<span class="html-italic">n</span> = 6–8). The effect of the factor irrigation (I), factor biostimulant (B), and the interaction between the factor irrigation and biostimulant (I × B) are presented, and when the effect of each factor or the interaction is statistically significant (<span class="html-italic">p</span> ≤ 0.05), it appears in bold. Different letters indicate statistically significant difference (<span class="html-italic">p</span> ≤ 0.05). Significant differences among I × B refer to differences between C, BC, S, and BS treatments. Significant differences among the factor I refer to differences between 100% (treatments C and BC) and 50% irrigation (treatments S and BS).</p>
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<p>Carbohydrate (<b>A</b>,<b>C</b>) and terpene (<b>B</b>,<b>D</b>) relative abundance (%) in <span class="html-italic">O. europaea</span> plants of the cultivar Arbequina (<b>A</b>,<b>B</b>) and Galega (<b>C</b>,<b>D</b>) in the treatments C (well-watered), BC (biostimulant + well-watered), S (water deficit), and BS (biostimulant + water deficit). Bars represent mean ± standard error (<span class="html-italic">n</span> = 3–4). The effect of the factor irrigation (I), factor biostimulant (B), and the interaction between the factor irrigation and biostimulant (I × B) are presented, and when the effect of each factor or the interaction is statistically significant (<span class="html-italic">p</span> ≤ 0.05), it appears in bold. Different letters indicate statistically significant difference (<span class="html-italic">p</span> ≤ 0.05). Significant differences among I × B refer to differences between C, BC, S, and BS treatments. Significant differences among the factor I refer to differences between 100% (treatments C and BC) and 50% (treatments S and BS) irrigation. For the case of <span class="html-small-caps">d</span>-(−)-tagatofuranose, gluconolactone, <span class="html-small-caps">d</span>-glucose, <span class="html-small-caps">d</span>-(+)-galactose, <span class="html-small-caps">d</span>-(+)-turanose, and <span class="html-small-caps">d</span>-erythrose in Arbequina and <span class="html-small-caps">d</span>-(−)-tagatofuranose, <span class="html-small-caps">d</span>-glucose, <span class="html-small-caps">d</span>-(+)-galactose, <span class="html-small-caps">d</span>-(+)-turanose, <span class="html-small-caps">d</span>-erythrose, <span class="html-small-caps">d</span>-mannitol, and myo-inositol in Galega, one-way ANOVA was performed and significant differences (<span class="html-italic">p</span> ≤ 0.05) are marked in bold and indicated by different letters. <span class="html-small-caps">d</span>-(−)-Tagato.: <span class="html-small-caps">d</span>-(−)-Tagatofuranose; Gluconolac.: Gluconolactone; <span class="html-small-caps">d</span>-(+)-Galac.—<span class="html-small-caps">d</span>-(+)-Galactose; <span class="html-small-caps">d</span>-(+)-Turan.: <span class="html-small-caps">d</span>-(+)-Turanose.</p>
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<p>Relative expression of dehydrins <span class="html-italic">OeDHN1I</span> (<b>A</b>,<b>D</b>), small heat shock proteins <span class="html-italic">OeHSP18.3</span> (<b>B</b>,<b>E</b>), and aquaporins <span class="html-italic">OePIP1.1</span> (<b>C</b>,<b>F</b>) in <span class="html-italic">O. europaea</span> plants of the treatments C (well-watered), BC (biostimulant + well-watered), S (water deficit), and BS (biostimulant + water deficit). Bars represents mean ± standard error (<span class="html-italic">n</span> = 4–8). The effect of the factor irrigation (I), factor biostimulant (B), and the interaction between the factor irrigation and biostimulant (I × B) are presented, and when the effect of each factor or the interaction is statistically significant (<span class="html-italic">p</span> ≤ 0.05), it appears in bold. Different letters indicate statistically significant difference (<span class="html-italic">p</span> ≤ 0.05). Significant differences among I × B refer to differences between C, BC, S, and BS treatments. Significant differences among the factor B refer to treatments without biostimulant (treatments C and S) and treatments with biostimulant (treatments BC and BS).</p>
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<p>Principal component analysis plot (<span class="html-italic">x</span>-axis—first component PC1; and <span class="html-italic">y</span>-axis—second component PC2) of the physiological, molecular, and metabolomic data in olive leaves from both cultivars. PC1 explains 36% of the variance, while PC2 explain 23%. Circles with different colors depict sample scores of the different treatments. Ac.: acid; Carot.: carotenoids; DHN: <span class="html-italic">OeDHN1</span>; E: transpiration rate; Galacto.: <span class="html-small-caps">d</span>-galactose; Gluco.: <span class="html-small-caps">d</span>-Glucose; gs: stomatal conductance; Height: heigh increment; HSP: <span class="html-italic">OeHSP18.3</span>; LCA: long-chain alkane; Mio-In.: myo-inositol; Neophyt.: neophytadiene; PIP: <span class="html-italic">OePIP1.1</span>; P<sub>n</sub>: net CO<sub>2</sub>-assimilation rate; Tagato.: <span class="html-small-caps">d</span>-(−)- Tagatofuranose; WUE: intrinsic water-use efficiency.</p>
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<p>Schematization of the experiment. FC, field capacity.</p>
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23 pages, 6558 KiB  
Article
Unravelling Different Water Management Strategies in Three Olive Cultivars: The Role of Osmoprotectants, Proteins, and Wood Properties
by Sara Parri, Claudia Faleri, Marco Romi, José C. del Río, Jorge Rencoret, Maria Celeste Pereira Dias, Sara Anichini, Claudio Cantini and Giampiero Cai
Int. J. Mol. Sci. 2024, 25(20), 11059; https://doi.org/10.3390/ijms252011059 - 15 Oct 2024
Viewed by 632
Abstract
Understanding the responses of olive trees to drought stress is crucial for improving cultivation and developing drought-tolerant varieties. Water transport and storage within the plant is a key factor in drought-tolerance strategies. Water management can be based on a variety of factors such [...] Read more.
Understanding the responses of olive trees to drought stress is crucial for improving cultivation and developing drought-tolerant varieties. Water transport and storage within the plant is a key factor in drought-tolerance strategies. Water management can be based on a variety of factors such as stomatal control, osmoprotectant molecules, proteins and wood properties. The aim of the study was to evaluate the water management strategy under drought stress from an anatomical and biochemical point of view in three young Italian olive cultivars (Giarraffa, Leccino and Maurino) previously distinguished for their physiological and metabolomic responses. For each cultivar, 15 individuals in pots were exposed or not to 28 days of water withholding. Every 7 days, the content of sugars (including mannitol), proline, aquaporins, osmotins, and dehydrins, in leaves and stems, as well as the chemical and anatomical characteristics of the wood of the three cultivars, were analyzed. ‘Giarraffa’ reduced glucose levels and increased mannitol production, while ‘Leccino’ accumulated more proline. Both ‘Leccino’ and ‘Maurino’ increased sucrose and aquaporin levels, possibly due to their ability to remove embolisms. ‘Maurino’ and ‘Leccino’ accumulated more dehydrins and osmotins. While neither genotype nor stress affected wood chemistry, ‘Maurino’ had a higher vessel-to-xylem area ratio and a larger hydraulic diameter, which allows it to maintain a high transpiration rate but may make it more susceptible to cavitation. The results emphasized the need for an integrated approach, highlighting the importance of the relative timing and sequence of each parameter analyzed, allowing, overall, to define a “strategy” rather than a “response” to drought of each cultivar. Full article
(This article belongs to the Special Issue Molecular Advances in Olive and Its Derivatives)
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Figure 1

Figure 1
<p>Sugar levels identified by HPLC in Giarraffa (GIA), Leccino (LEC) and Maurino (MAU) under control (CTRL, black) and drought stress (DS, orange). (<b>A</b>) Glucose in leaf; (<b>B</b>) glucose in stem; (<b>C</b>) fructose in leaf; (<b>D</b>) fructose in stem; (<b>E</b>) sucrose in leaf; (<b>F</b>) sucrose in stem; (<b>G</b>) mannitol in leaf; (<b>H</b>) mannitol in stem, all expressed in mg g<sup>−1</sup> tissue dry weight (DW). Data in each column are presented as mean ± standard error. Within each time point, different letters denote statistical significance (<span class="html-italic">p</span>-value &lt; 0.05) according to Tukey’s multiple post hoc tests.</p>
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<p>Sugar levels identified by HPLC in Giarraffa (GIA), Leccino (LEC) and Maurino (MAU) under control (CTRL, black) and drought stress (DS, orange). (<b>A</b>) Glucose in leaf; (<b>B</b>) glucose in stem; (<b>C</b>) fructose in leaf; (<b>D</b>) fructose in stem; (<b>E</b>) sucrose in leaf; (<b>F</b>) sucrose in stem; (<b>G</b>) mannitol in leaf; (<b>H</b>) mannitol in stem, all expressed in mg g<sup>−1</sup> tissue dry weight (DW). Data in each column are presented as mean ± standard error. Within each time point, different letters denote statistical significance (<span class="html-italic">p</span>-value &lt; 0.05) according to Tukey’s multiple post hoc tests.</p>
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<p>Proline content in leaves (<b>A</b>) and stems (<b>B</b>) of Giarraffa (GIA), Leccino (LEC), and Maurino (MAU) cultivars under control (CTRL, black) and drought stress (DS, orange). Contents are expressed as μg g<sup>−1</sup> tissue dry weight (DW). Values in each column are presented as mean ± standard error. Within each time point, different letters denote statistical significance (<span class="html-italic">p</span>-value &lt; 0.05) according to Tukey’s multiple post hoc tests.</p>
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<p>PIP1 aquaporin levels in stems of Giarraffa (GIA), Leccino (LEC) and Maurino (MAU) cultivars under control (CTRL) and drought-stress (DS) conditions, at the beginning of stress (t0), two weeks later (t2) and four weeks later (t4). (<b>A</b>) Membranes immunoblotted with anti-aquaporin antibodies from the above experimental groups; (<b>B</b>) relative blot quantification expressed as integrated density (i.d.).</p>
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<p>Dehydrin levels in leaves of Giarraffa (GIA), Leccino (LEC) and Maurino (MAU) cultivars after two (t2) and four (t4) weeks of stress. (<b>A</b>) Membranes immunoblotted with anti-dehydrin antibodies from the above experimental groups; (<b>B</b>) relative quantification of the blots expressed as integrated density (i.d.).</p>
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<p>Dehydrin levels in stems of Giarraffa (GIA), Leccino (LEC) and Maurino (MAU) cultivars under control (CTRL) and drought-stress (DS) conditions, at the beginning of stress (t0), two weeks later (t2) and four weeks later (t4). (<b>A</b>) Membranes immunoblotted with anti-dehydrin antibodies from the above experimental groups; (<b>B</b>) relative blotting quantification expressed as integrated density (i.d.).</p>
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<p>Osmotin levels in leaves of Giarraffa (GIA), Leccino (LEC) and Maurino (MAU) cultivars after two (t2) and four (t4) weeks of stress. (<b>A</b>) Membranes immunoblotted with anti-osmotin antibodies from the above experimental groups; (<b>B</b>) relative quantification of blotting expressed as integrated density (i.d.).</p>
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<p>Osmotin levels in stems of Giarraffa (GIA), Leccino (LEC) and Maurino (MAU) cultivars under control (CTRL) and drought-stress (DS) conditions, at the beginning of stress (t0), two weeks (t2) and four weeks (t4). (<b>A</b>) Membranes immunoblotted with anti-osmotin antibodies from the above experimental groups; (<b>B</b>) relative blotting quantification expressed as integrated density (i.d.).</p>
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<p>2D-HSQC NMR spectra of stems from three olive cultivars (Giarraffa, Leccino, and Maurino) subjected to drought stress (DS) (bottom) and their corresponding stem controls (top). The primary lignin structures identified are also shown. A: β-<span class="html-italic">O</span>-4′ alkyl-aryl ethers; B: β-5′ phenylcoumarans; C: β-β′ resinols; F: β-1′-spirodienones Cinnamyl alcohol end-groups (I), cinnamaldehyde end-groups (J), <span class="html-italic">p</span>-hydroxyphenyl units (H), guaiacyl units (G), syringyl units (S), and Cα-oxidized syringyl units (Sʹ). The yellow boxes reflect semi-quantitative estimates of lignin units and compounds. Composition is expressed in molar percent (H + G + S = 100%), and end-groups are expressed as a fraction of the total lignin inter-unit linkage types A–F.</p>
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<p>Stem sections of <span class="html-italic">Olea europaea</span> cultivars Giarraffa (<b>A</b>), Leccino (<b>B</b>), and Maurino (<b>C</b>). ph: phloem, x: xylem vessels; cz: cambial zone; r: parenchyma ray; f: fibers; p: paratracheal parenchyma; bar corresponds to 20 µm. (<b>D</b>) Frequency distributions (number of vessels by 5 µm diameter) of vessel lumen diameters in the three olive cultivars.</p>
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23 pages, 15912 KiB  
Article
The Influence of Water Deficit on Dehydrin Content in Callus Culture Cells of Scots Pine
by Natalia Korotaeva, Vladimir Shmakov, Vadim Bel’kov, Daria Pyatrikas, Sofia Moldavskaya and Igor Gorbenko
Plants 2024, 13(19), 2752; https://doi.org/10.3390/plants13192752 - 30 Sep 2024
Viewed by 601
Abstract
Under a water deficit, the protective proteins known as dehydrins (DHNs) prevent nonspecific interactions in protein and membrane structures and their damage, in addition to playing an antioxidant role. The DHNs of a widespread xerophytic species Scots pine (Pinus sylvestris L.) have [...] Read more.
Under a water deficit, the protective proteins known as dehydrins (DHNs) prevent nonspecific interactions in protein and membrane structures and their damage, in addition to playing an antioxidant role. The DHNs of a widespread xerophytic species Scots pine (Pinus sylvestris L.) have been poorly studied, and their role in resistance to water deficits has not been revealed. In this paper, we have expanded the list of DHNs that accumulate in the cells of Scots pine under the conditions of water deficits and revealed their relationship with the effects of water deficits. In this investigation, callus cultures of branches and buds of Scots pine were used. A weak water deficit was created by adding polyethylene glycol to the culture medium. Under the conditions of a water deficit, the activity of catalase and peroxidase enzymes increased in the callus cultures. A moderate decrease in the total water content was correlated with a decrease in the growth rate of the callus cultures, as well as with an increase in the activity of lipid peroxidation. The accumulation of Mr 72, 38, and 27 kDa DHNs occurred in the callus cultures of buds, and the accumulation of Mr 72 and 27 kDa DHNs positively correlated with the lipid peroxidation activity. An increase in the content of DHNs was observed in cultures that differed in origin, growth indicators, and biochemical parameters, indicating the universality of this reaction. Thus, previously undescribed DHNs were identified, the accumulation of which is caused by water deficiency and is associated with manifestations of oxidative stress in the kidney cells of Scots pine. Full article
(This article belongs to the Section Plant Response to Abiotic Stress and Climate Change)
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<p>The appearance of PEG-treated (5 and 8%) and control callus samples. A typical appearance is presented. C—control (untreated); t1, t2, t3, t4, and t5 represent the numbers of individual trees.</p>
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<p>The influence of PEG concentrations on total water content in cells of callus cultures. The digits represent numbers of the trees that were used as sources of plant material for callus induction. The means and standard errors of the mean are shown (n = 3–6). * indicates significance differences among control (c) and treatment (5 or 8% PEG); ** indicates significance differences among treatments (<span class="html-italic">p</span> &lt; 0.05). Different letters indicate significance differences among calli obtained from different trees at <span class="html-italic">p</span> &lt; 0.05 (the regular font corresponds to control; the underlined italic font corresponds to 5% PEG; the bold font corresponds to 8% PEG). fw—fresh weight.</p>
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<p>The influence of water deficit (WD) on viability of callus cell cultures. The digits represent numbers of the trees that were used as sources of plant material for callus induction. The means and standard errors of the mean are shown (n = 3). * indicates significance differences among control (c) and treatment (5 or 8% PEG); ** indicates significance differences among treatments (<span class="html-italic">p</span> &lt; 0.05). Different letters indicate significance differences among calli obtained from different trees at <span class="html-italic">p</span> &lt; 0.05 (the regular font corresponds to control; the underlined italic font corresponds to 5% PEG; the bold font corresponds to 8% PEG). FW—fresh weight.</p>
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<p>The influence of water deficit (WD) on hydrogen peroxide content and superoxide anion content in callus culture cells. The digits represent numbers of the trees that were used as sources of plant material for callus induction. The means and standard errors of the mean are shown. * indicates significance differences among control (c) and treatment (5 or 8% PEG); ** indicates significance differences among treatments (<span class="html-italic">p</span> &lt; 0.05). Different letters indicate significance differences among calli obtained from different trees at <span class="html-italic">p</span> &lt; 0.05 (the regular font corresponds to control; the underlined italic font corresponds to 5% PEG; the bold font corresponds to 8% PEG). FW—fresh weight.</p>
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<p>The influence of water deficit (WD) on CAT, POD, and LP activities in cells of callus cultures. The digits represent numerical indexes of the trees that were used as sources of plant material for callus induction. The means and standard errors of the mean are shown. n = 3. * indicates significance differences among control (c) and treatment (5 or 8% PEG); ** indicates significance differences among treatments (<span class="html-italic">p</span> &lt; 0.05). Different letters indicate significance differences among calli obtained from different trees at <span class="html-italic">p</span> &lt; 0.05 (the regular font corresponds to control; the underlined italic font corresponds to 5% PEG; the bold font corresponds to 8% PEG). FW—fresh weight.</p>
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<p>Heatmap of DHN gene expression log<sub>2</sub>-fold changes (LogFC) in calli under PEG treatment in relation to control conditions. Br—sample calli initiated from branch tissues; Bu—sample calli initiated from bud tissue; 5% and 8% are percentages of PEG added to growth media for WD induction. t1–t5 represent different trees used for callus culture initiation. Three independent biological replicates were used, and only results with significant expression differences (<span class="html-italic">P</span><sub>t-test</sub> &lt; 0.05) from control conditions are shown. <span class="html-italic">ACT-1</span> expression is used for normalization.</p>
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<p>The influence of water deficit (WD) on DHN accumulation in cells of callus cultures. The digits represent numerical indexes of the trees that were used as sources of plant material for callus induction. c—control; 5%—5% PEG; 8%—8% PEG. The digits on the right represent the molecular weights of the detected DHNs. Typical membranes are presented.</p>
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<p>The influence of water deficit (WD) on the content of 75, 38, and 27 kDa DHNs in cells of callus cultures. The digits represent numbers of the trees that were used as sources of plant material for callus induction. The means and standard errors of the mean are shown. n = 3–4. * indicates significance differences among control (c) and treatment (5 or 8% PEG) (<span class="html-italic">p</span> &lt; 0.05). Different letters indicate significance differences among calli obtained from different trees at <span class="html-italic">p</span> &lt; 0.05 (the regular font corresponds to control; the underlined italic font corresponds to 5% PEG; the bold font corresponds to 8% PEG). The data on the DHN content of cells of t2 and t5 branches were obtained in duplicate and are not shown in this diagram.</p>
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17 pages, 5171 KiB  
Article
Transcription Factor and Protein Regulatory Network of PmACRE1 in Pinus massoniana Response to Pine Wilt Nematode Infection
by Wanfeng Xie, Xiaolin Lai, Yuxiao Wu, Zheyu Li, Jingwen Zhu, Yu Huang and Feiping Zhang
Plants 2024, 13(19), 2672; https://doi.org/10.3390/plants13192672 - 24 Sep 2024
Viewed by 3238
Abstract
Pine wilt disease, caused by Bursaphelenchus xylophilus, is a highly destructive and contagious forest affliction. Often termed the “cancer” of pine trees, it severely impacts the growth of Masson pine (Pinus massoniana). Previous studies have demonstrated that ectopic expression of [...] Read more.
Pine wilt disease, caused by Bursaphelenchus xylophilus, is a highly destructive and contagious forest affliction. Often termed the “cancer” of pine trees, it severely impacts the growth of Masson pine (Pinus massoniana). Previous studies have demonstrated that ectopic expression of the PmACRE1 gene from P. massoniana in Arabidopsis thaliana notably enhances resistance to pine wilt nematode infection. To further elucidate the transcriptional regulation and protein interactions of the PmACRE1 in P. massoniana in response to pine wilt nematode infection, we cloned a 1984 bp promoter fragment of the PmACRE1 gene, a transient expression vector was constructed by fusing this promoter with the reporter GFP gene, which successfully activated the GFP expression. DNA pull-down assays identified PmMYB8 as a trans-acting factor regulating PmACRE1 gene expression. Subsequently, we found that the PmACRE1 protein interacts with several proteins, including the ATP synthase CF1 α subunit, ATP synthase CF1 β subunit, extracellular calcium-sensing receptor (PmCAS), caffeoyl-CoA 3-O-methyltransferase (PmCCoAOMT), glutathione peroxidase, NAD+-dependent glyceraldehyde-3-phosphate dehydrogenase, phosphoglycerate kinase 1, cinnamyl alcohol dehydrogenase, auxin response factor 16, and dehydrin 1 protein. Bimolecular fluorescence complementation (BiFC) assays confirmed the interactions between PmACRE1 and PmCCoAOMT, as well as PmCAS proteins in vitro. These findings provide preliminary insights into the regulatory role of PmACRE1 in P. massoniana’s defense against pine wilt nematode infection. Full article
(This article belongs to the Special Issue Molecular Biology and Bioinformatics of Forest Trees)
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<p>Transient expression of <span class="html-italic">PmACRE1</span> gene promoter-driven GFP expression in <span class="html-italic">N. benthamiana</span> leaves.</p>
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<p>Subcellular localization of PmMYB8 transcription factor in <span class="html-italic">N. benthamiana</span> leaves.</p>
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<p>Detection of Proteins Interacting with PmACRE1. M, Protein ladder; Lanes 1–8, Proteins binding on the <span class="html-italic">PmACRE1</span> gene promoter.</p>
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<p>KEGG enrichment analysis of PmACRE1-interacting proteins in <span class="html-italic">P. massoniana.</span></p>
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<p>BiFC Validation of Protein Interaction between PmACRE1 and PmCCoAOMT.</p>
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<p>BiFC Validation of Protein Interaction between PmACRE1 and PmCAS.</p>
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26 pages, 4308 KiB  
Review
Drought Stress Effects and Ways for Improving Drought Tolerance in Impatiens walleriana Hook.f.—A Review
by Marija Milovančević, Milana Trifunović-Momčilov, Olga Radulović, Snežana Milošević and Angelina Subotić
Horticulturae 2024, 10(9), 903; https://doi.org/10.3390/horticulturae10090903 - 26 Aug 2024
Viewed by 1093
Abstract
Drought is one of the main abiotic stresses affecting plant growth and development. Reduced plant yield and quality are primarily caused by the reductions in photosynthesis, mineral uptake, metabolic disorders, damages from the increased production of reactive oxygen species, and many other disruptions. [...] Read more.
Drought is one of the main abiotic stresses affecting plant growth and development. Reduced plant yield and quality are primarily caused by the reductions in photosynthesis, mineral uptake, metabolic disorders, damages from the increased production of reactive oxygen species, and many other disruptions. Plants utilize drought resistance mechanisms as a defense strategy, and the systems’ activation is dependent upon several factors, including plant genotype, onthogenesis phase, drought intensity and duration, and the season in which the drought occurs. Impatiens walleriana is a worldwide popular flowering plant recognized for its vibrant flower colors, and is an indispensable plant in pots, gardens and other public areas. It prefers well-draining, moisturized soil, and does not perform well in overly dry or waterlogged conditions. Consequently, inadequate water supply is a common problem for this plant during production, transportation, and market placement, which has a substantial impact on plant performance overall. This review article outlines certain features of morphological, physiological, and molecular alterations induced by drought in ornamental, drought-sensitive plant species I. walleriana, as well as research carried out to date with the aim to improve the drought tolerance. Stress proteins aquaporins and dehydrins, whose molecular structure was described for the first time in this plant species, are highlighted specifically for their role in drought stress. Furthermore, the effective improvement of drought tolerance in I. walleriana by exogenous application of Plant Growth Regulators and Plant Growth-Promoting Bacteria is discussed in detail. Finally, this review can provide valuable insights for improving plant resilience and productivity in the face of water scarcity, which is critical for sustainable agriculture and horticulture. Full article
(This article belongs to the Special Issue Horticultural Production under Drought Stress)
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<p>Drought effects on plant growth and development (<b>left</b> side), and plant resistance mechanisms to drought (<b>right</b> side).</p>
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<p><span class="html-italic">I. walleriana</span> with different color of flowers.</p>
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<p>Morphological differences between well-watered and drought-stressed <span class="html-italic">I. walleriana</span>. (<b>a</b>,<b>b</b>) well-watered shoots and roots; (<b>c</b>,<b>d</b>) drought-stressed shoots and roots.</p>
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<p>3D structures of <span class="html-italic">I. walleriana</span> aquaporins (IwPIP1;4, IwPIP2;2, IwPIP2;7 and IwTIP4;1), and dehydrins (IwDhn1, IwDhn2.1 and IwDhn2.2), obtained by using the software SWISS-MODEL (<a href="https://swissmodel.expasy.org/" target="_blank">https://swissmodel.expasy.org/</a>) and PHYRE2 (<a href="http://www.sbg.bio.ic.ac.uk/phyre2/html/page.cgi?id=index" target="_blank">http://www.sbg.bio.ic.ac.uk/phyre2/html/page.cgi?id=index</a>).</p>
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<p>Summarized effects of exogenously applied elicitors on <span class="html-italic">I. walleriana</span> drought-tolerance improvement.</p>
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18 pages, 716 KiB  
Review
New Insights into Involvement of Low Molecular Weight Proteins in Complex Defense Mechanisms in Higher Plants
by Magdalena Ruszczyńska and Hubert Sytykiewicz
Int. J. Mol. Sci. 2024, 25(15), 8531; https://doi.org/10.3390/ijms25158531 - 5 Aug 2024
Viewed by 883
Abstract
Dynamic climate changes pose a significant challenge for plants to cope with numerous abiotic and biotic stressors of increasing intensity. Plants have evolved a variety of biochemical and molecular defense mechanisms involved in overcoming stressful conditions. Under environmental stress, plants generate elevated amounts [...] Read more.
Dynamic climate changes pose a significant challenge for plants to cope with numerous abiotic and biotic stressors of increasing intensity. Plants have evolved a variety of biochemical and molecular defense mechanisms involved in overcoming stressful conditions. Under environmental stress, plants generate elevated amounts of reactive oxygen species (ROS) and, subsequently, modulate the activity of the antioxidative enzymes. In addition, an increase in the biosynthesis of important plant compounds such as anthocyanins, lignin, isoflavonoids, as well as a wide range of low molecular weight stress-related proteins (e.g., dehydrins, cyclotides, heat shock proteins and pathogenesis-related proteins), was evidenced. The induced expression of these proteins improves the survival rate of plants under unfavorable environmental stimuli and enhances their adaptation to sequentially interacting stressors. Importantly, the plant defense proteins may also have potential for use in medical applications and agriculture (e.g., biopesticides). Therefore, it is important to gain a more thorough understanding of the complex biological functions of the plant defense proteins. It will help to devise new cultivation strategies, including the development of genotypes characterized by better adaptations to adverse environmental conditions. The review presents the latest research findings on selected plant defense proteins. Full article
(This article belongs to the Section Molecular Plant Sciences)
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<p>Scheme of stress-related induction of biosynthesis of the low molecular weight (LMW) defensive proteins in plants. ROS—reactive oxygen species.</p>
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<p>Biotic activity of the low molecular weight plant defensive proteins.</p>
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27 pages, 2669 KiB  
Article
Transcriptomic Analyses Reveal That Coffea arabica and Coffea canephora Have More Complex Responses under Combined Heat and Drought than under Individual Stressors
by Isabel Marques, Isabel Fernandes, Octávio S. Paulo, Dora Batista, Fernando C. Lidon, Ana P. Rodrigues, Fábio L. Partelli, Fábio M. DaMatta, Ana I. Ribeiro-Barros and José C. Ramalho
Int. J. Mol. Sci. 2024, 25(14), 7995; https://doi.org/10.3390/ijms25147995 - 22 Jul 2024
Cited by 1 | Viewed by 916
Abstract
Increasing exposure to unfavorable temperatures and water deficit imposes major constraints on most crops worldwide. Despite several studies regarding coffee responses to abiotic stresses, transcriptome modulation due to simultaneous stresses remains poorly understood. This study unravels transcriptomic responses under the combined action of [...] Read more.
Increasing exposure to unfavorable temperatures and water deficit imposes major constraints on most crops worldwide. Despite several studies regarding coffee responses to abiotic stresses, transcriptome modulation due to simultaneous stresses remains poorly understood. This study unravels transcriptomic responses under the combined action of drought and temperature in leaves from the two most traded species: Coffea canephora cv. Conilon Clone 153 (CL153) and C. arabica cv. Icatu. Substantial transcriptomic changes were found, especially in response to the combination of stresses that cannot be explained by an additive effect. A large number of genes were involved in stress responses, with photosynthesis and other physiologically related genes usually being negatively affected. In both genotypes, genes encoding for protective proteins, such as dehydrins and heat shock proteins, were positively regulated. Transcription factors (TFs), including MADS-box genes, were down-regulated, although responses were genotype-dependent. In contrast to Icatu, only a few drought- and heat-responsive DEGs were recorded in CL153, which also reacted more significantly in terms of the number of DEGs and enriched GO terms, suggesting a high ability to cope with stresses. This research provides novel insights into the molecular mechanisms underlying leaf Coffea responses to drought and heat, revealing their influence on gene expression. Full article
(This article belongs to the Special Issue Plants Responses to Climate Change)
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<p>Total number of expressed genes in <span class="html-italic">Coffea arabica</span> cv. Icatu and <span class="html-italic">C. canephora</span> cv. Conilon Clone 153 (CL153) plants grown under well-watered (WW; light colors) and control temperature (25 °C; blue) conditions before gradual exposure to severe water deficit (SWD; dark colors). Afterward, WW and SWD plants were additionally exposed to increased temperatures of 37 °C (green) and 42 °C (orange), followed by a 2-week recovery period (REC14, yellow) with full rewatering and a temperature of 25 °C.</p>
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<p>Differentially expressed genes (DEGs) relative to initial control conditions (25 °C, WW, light colors) in <span class="html-italic">Coffea arabica</span> cv. Icatu and <span class="html-italic">C. canephora</span> cv. Conilon Clone 153 (CL153) plants after gradual exposure to severe water deficit (SWD, dark colors) and to increased temperatures of 37 °C (green) and 42 °C (red), followed by a 2-week recovery period recovery (REC14, yellow) with full rewatering and a temperature of 25 °C. Intersections between 37 °C and 42 °C (gray), 42 °C and REC14 (pink), 37 °C and REC14 (ashy), and 42 °C and REC14 (violet) are shown.</p>
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<p>Proportion of significantly up- (red) and down-regulated (blue) DEGs associated with antioxidant activities, lipid metabolism, photosynthesis, and respiration in Icatu (<b>A</b>) and CL153 (<b>B</b>). Treatments are as explained in <a href="#ijms-25-07995-f002" class="html-fig">Figure 2</a>.</p>
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<p>Over-representation analysis of Gene Ontology (GO) terms performed with gProfiler against functional annotations in Icatu (<b>A</b>) and CL153 (<b>B</b>). GO terms are grouped by main category—Biological Process (BP), Molecular Function (MF), and Cellular Component (CC). Counts (size) indicate the number of DEGs annotated with each GO term, and dots are colored by the adjusted <span class="html-italic">p</span>-value (red: up-regulated DEGs; blue: down-regulated DEG). Treatments are as explained in <a href="#ijms-25-07995-f002" class="html-fig">Figure 2</a>.</p>
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<p>Correlation results between RNA-seq data and qRT–PCR expression levels considering the following selected genes: <span class="html-italic">PP2C51</span>, protein phosphatase 2C 51-like; <span class="html-italic">LEADC3</span>, late embryogenesis abundant protein Dc3-like; <span class="html-italic">DH1a</span>, dehydrin DH1a; <span class="html-italic">SUS2</span>, sucrose synthase 2-like; <span class="html-italic">PIP2-2</span>, aquaporin PIP2-2-like; <span class="html-italic">XTH6</span>, xyloglucan endotransglucosylase/hydrolase protein 6; <span class="html-italic">GOLS2</span>, galactinol synthase 2-like; <span class="html-italic">CuSOD1</span>, superoxide dismutase [Cu-Zn]; <span class="html-italic">APXChl</span>, chloroplast ascorbate peroxidase.</p>
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16 pages, 5151 KiB  
Article
Transcriptome Analysis Reveals Genes and Pathways Associated with Drought Tolerance of Early Stages in Sweet Potato (Ipomoea batatas (L.) Lam.)
by Peng Cheng, Fanna Kong, Yang Han, Xiaoping Liu and Jiaping Xia
Genes 2024, 15(7), 948; https://doi.org/10.3390/genes15070948 - 19 Jul 2024
Viewed by 987
Abstract
The yield of sweet potato [Ipomoea batatas (L.) Lam] can be easily threatened by drought stress. Typically, early stages like the seedling stage and tuber-root expansion stage are more vulnerable to drought stress. In this study, a highly drought-tolerant sweet potato cultivar [...] Read more.
The yield of sweet potato [Ipomoea batatas (L.) Lam] can be easily threatened by drought stress. Typically, early stages like the seedling stage and tuber-root expansion stage are more vulnerable to drought stress. In this study, a highly drought-tolerant sweet potato cultivar “WanSu 63” was subjected to drought stress at both the seedling stage (15 days after transplanting, 15 DAT) and the tuber-root expansion stage (45 DAT). Twenty-four cDNA libraries were constructed from leaf segments and root tissues at 15 and 45 DAT for Next-Generation Sequencing. A total of 663, 063, and 218 clean reads were obtained and then aligned to the reference genome with a total mapped ratio greater than 82.73%. A sum of 7119, 8811, 5463, and 930 differentially expressed genes were identified from leaves in 15 days (L15), roots in 15 days (R15), leaves in 45 days (L45), and roots in 45 days (R45), respectively, in drought stress versus control. It was found that genes encoding heat shock proteins, sporamin, LEA protein dehydrin, ABA signaling pathway protein gene NCED1, as well as a group of receptor-like protein kinases genes were enriched in differentially expressed genes. ABA content was significantly higher in drought-treated tissues than in the control. The sweet potato biomass declined sharply to nearly one-quarter after drought stress. In conclusion, this study is the first to identify the differentially expressed drought-responsive genes and signaling pathways in the leaves and roots of sweet potato at the seedling and root expansion stages. The results provide potential resources for drought resistance breeding of sweet potato. Full article
(This article belongs to the Special Issue Advances in Genetic Breeding of Sweetpotato)
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<p>Biochemical analysis of sweet potato samples under drought stress. (<b>A</b>) Water content. (<b>B</b>) Total protein content. (<b>C</b>) Abscisic acid content. (<b>D</b>) Proline content. (<b>E</b>) Superoxide dismutase activity. (<b>F</b>) Content of total antioxidant capacity. CK: control; DR: drought stress treatment. Bars mean SD (<span class="html-italic">n</span> = 3). <span class="html-italic">p</span> represents the result of Student’s <span class="html-italic">t</span>-test.</p>
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<p>Venn diagram of DEGs. (<b>A</b>) DEGs in CK_L15-vs-DR_L15 versus CK_L45-vs-DR_L45. (<b>B</b>) DEGs in CK_R15-vs-DR_R15 versus CK_R45-vs-DR_R45. (<b>C</b>) Up-regulated DEGs in CK_L15-vs-DR_L15 versus CK_L45-vs-DR_L45. (<b>D</b>) Up-regulated DEGs in CK_R15-vs-DR_R15 versus CK_R45-vs-DR_R45. (<b>E</b>) Down-regulated DEGs in CK_L15-vs-DR_L15 versus CK_L45-vs-DR_L45. (<b>F</b>) Down-regulated DEGs in CK_R15-vs-DR_R15 versus CK_R45-vs-DR_R45. (<b>G</b>) Up-regulated DEGs in all four stages and tissues. (<b>H</b>) Down-regulated DEGs in all four stages and tissues.</p>
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<p>Most significant differentially expressed genes in all four stages and tissues. (<b>A</b>) DEGs in CK_L15-vs-DR_L15. (<b>B</b>) DEGs in CK_L45-vs-DR_L45. (<b>C</b>) DEGs in CK_R15-vs-DR_R15. (<b>D</b>) DEGs in CK_R45-vs-DR_R45. Red indicates up-regulated genes and blue indicates down-regulated genes. The genes were screened by <span class="html-italic">p</span>-value.</p>
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<p>GO and KEGG enrichment in all four stages and tissues. (<b>A</b>) GO term enrichment of down-regulated DEGs. (<b>B</b>) GO term enrichment of up-regulated DEGs. (<b>C</b>) KEGG term enrichment of down-regulated DEGs. (<b>D</b>) KEGG term enrichment of up-regulated DEGs.</p>
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<p>Receptor-like kinases enriched in (<b>A</b>) CK_L15 vs. DR_L15, (<b>B</b>) CK_L45 vs. DR_L45, (<b>C</b>) CK_R15 vs. DR_R15, and (<b>D</b>) CK_R45 vs. DR_R45.</p>
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21 pages, 1382 KiB  
Article
Differential Gene Expression in Contrasting Common Bean Cultivars for Drought Tolerance during an Extended Dry Period
by Talita Pijus Ponce, Michely da Silva Bugança, Victória Stern da Silva, Rogério Fernandes de Souza, Vânia Moda-Cirino and Juarez Pires Tomaz
Genes 2024, 15(7), 935; https://doi.org/10.3390/genes15070935 - 17 Jul 2024
Viewed by 930
Abstract
Common beans (Phaseolus vulgaris L.), besides being an important source of nutrients such as iron, magnesium, and protein, are crucial for food security, especially in developing countries. Common bean cultivation areas commonly face production challenges due to drought occurrences, mainly during the [...] Read more.
Common beans (Phaseolus vulgaris L.), besides being an important source of nutrients such as iron, magnesium, and protein, are crucial for food security, especially in developing countries. Common bean cultivation areas commonly face production challenges due to drought occurrences, mainly during the reproductive period. Dry spells last approximately 20 days, enough time to compromise production. Hence, it is crucial to understand the genetic and molecular mechanisms that confer drought tolerance to improve common bean cultivars’ adaptation to drought. Sixty six RNASeq libraries, generated from tolerant and sensitive cultivars in drought time sourced from the R5 phenological stage at 0 to 20 days of water deficit were sequenced, generated over 1.5 billion reads, that aligned to 62,524 transcripts originating from a reference transcriptome, as well as 6673 transcripts obtained via de novo assembly. Differentially expressed transcripts were functionally annotated, revealing a variety of genes associated with molecular functions such as oxidoreductase and transferase activity, as well as biological processes related to stress response and signaling. The presence of regulatory genes involved in signaling cascades and transcriptional control was also highlighted, for example, LEA proteins and dehydrins associated with dehydration protection, and transcription factors such as WRKY, MYB, and NAC, which modulate plant response to water deficit. Additionally, genes related to membrane and protein protection, as well as water and ion uptake and transport, were identified, including aquaporins, RING-type E3 ubiquitin transferases, antioxidant enzymes such as GSTs and CYPs, and thioredoxins. This study highlights the complexity of plant response to water scarcity, focusing on the functional diversity of the genes involved and their participation in the biological processes essential for plant adaptation to water stress. The identification of regulatory and cell protection genes offers promising prospects for genetic improvement aiming at the production of common bean varieties more resistant to drought. These findings have the potential to drive sustainable agriculture, providing valuable insights to ensure food security in a context of climate change. Full article
(This article belongs to the Special Issue Molecular Biology of Crop Abiotic Stress Resistance)
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<p>Venn diagram demonstrating the intersection DEGs at 4, 8, 12, 16, and 20 days of water deficit in common bean. (<b>A</b>) DEGs upregulated in BRS-Pontal. (<b>B</b>) DEGs downregulated in BRS-Pontal. (<b>C</b>) DEGs upregulated in IAPAR 81. (<b>D</b>) DEGs downregulated in IAPAR 81. (<b>E</b>) DEGs upregulated comparing BRS-Pontal and IAPAR 81 under water deficit. (<b>F</b>) DEGs downregulated comparing BRS-Pontal and IAPAR 81 under water deficit.</p>
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<p>Molecular function (MF), biological processes (BP) and cellular components (CC) identified by pathways and gene ontology (GO) analyses using g:Profiler of the significant DEGs (Log<sub>2</sub> fold change &gt; 2 and padj &lt; 0.05) when comparing BRS-Pontal and IAPAR 81 under water deficit, in all periods of evaluation (4, 8, 12, 16, and 20 days of drought). Numbers indicate the main identification GO.</p>
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<p>Relative gene quantification (RQ) of the <span class="html-italic">CaM</span> (<b>A</b>), <span class="html-italic">NAC</span> (<b>B</b>), <span class="html-italic">LEA5</span> (<b>C</b>) <span class="html-italic">GST</span> (<b>D</b>), and <span class="html-italic">Trx</span> (<b>E</b>) in cultivars IAPAR 81 (tolerant) and BRS (sensitive) demonstrated according to RQ = 2<sup>−ΔΔCt</sup>. Comparison between treatments subjected to drought (<b>D</b>) and controls (<b>C</b>) within each cultivar/collection period. The comparison of means was carried out using the Student’s <span class="html-italic">t</span> test at 5% (*) and 1% (**) of significance.</p>
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12 pages, 2697 KiB  
Article
ZmD11 Gene Regulates Tobacco Plant Floral Development under Drought Stress
by Zhanfeng Li, Fuchao Jiao, Zhiyi Sun, Enying Zhang, Xiyun Song, Yuhe Pei, Jun Li, Nicola Cannon, Xianmin Chang and Xinmei Guo
Agronomy 2024, 14(7), 1381; https://doi.org/10.3390/agronomy14071381 - 27 Jun 2024
Viewed by 566
Abstract
Maize is most sensitive to drought stress at the floral stage by reducing tassel and silk quality, and thus improving drought tolerance at this stage may help preserve yield. It has been reported that BRs (brassinosteroids) promote floral development under drought stress. However, [...] Read more.
Maize is most sensitive to drought stress at the floral stage by reducing tassel and silk quality, and thus improving drought tolerance at this stage may help preserve yield. It has been reported that BRs (brassinosteroids) promote floral development under drought stress. However, the function of the brassinosteroid biosynthesis gene ZmDWARF11 (ZmD11) on floral growth under drought stress has not been elucidated. This study found that under normal growth conditions, the heterologous over-expression of ZmD11 significantly enhanced both the vegetative growth and floral development of tobacco. Under drought stress, overexpressing ZmD11 reduced stress-induced tobacco flower size reduction, while it did not affect vegetative growth. After drought treatment, the activities of protective enzymes, including CAT (Catalase), SOD (Superoxide Dismutase), and POD (Peroxidase), were higher, while the content of MDA (Malondialdehyde) was lower in ZmD11 over-expression tobacco lines than that in the wild type control. The relative expression of dehydrin-related genes NtLeat5 and NtERD10 was increased in ZmD11 over-expression tobacco lines compared to that in the control. In summary, we reported that ZmD11 plays a role in tobacco floral development under drought stress. Our data are valuable in understanding the functions of BRs in regulating plant floral development under drought stress. Full article
(This article belongs to the Section Crop Breeding and Genetics)
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<p>The expression of <span class="html-italic">ZmD11</span> and endogenous BR content. (<b>A</b>) The expression of <span class="html-italic">ZmD11</span> is inhibited by high concentrations of exogenous BRs. (<b>B</b>) The expression level <span class="html-italic">ZmD11</span> in over-expression lines. (<b>C</b>) The endogenous BR content in transgenic lines. WT indicates wild type; OX indicates <span class="html-italic">ZmD11</span> over-expression of transgenic lines. At least three biological replicates were used. * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Over-expression of ZmD11 promotes seedling growth. (<b>A</b>) Photos of tobacco root after 7 days of growth. (<b>B</b>) Root length after 7 days of growth. (<b>C</b>) Photos of tobacco root and shoot after 37 days of growth. (<b>D</b>) Root fresh weight after 37 days of growth. (<b>E</b>) Shoot fresh weight after 37 days of growth. WT indicates wild type; OX indicates <span class="html-italic">ZmD11</span> over-expression of transgenic lines. At least three biological replicates were used. * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Over-expression of ZmD11 promotes tobacco floral development. (<b>A</b>) Photos of flower after 52 days of growth. (<b>B</b>) Floral organ weight. WT indicates wild type; OX indicates ZmD11 over-expression of transgenic lines. Three replicates were used. * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>The activity of protective enzymes and the transcription level of dehydrin genes. (<b>A</b>) Photos of floral organs during drought stress. (<b>B</b>) Proline content in tobacco leaves. (<b>C</b>) Relative water content in tobacco leaves. Drought 7 d is the 7th day of drought stress treatment, and Drought 15 d is the 15th day of drought stress treatment. WT indicates wild type; OX indicates ZmD11 over-expression of transgenic lines. Three replicates were used. * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>The activity or content of protective enzymes. (<b>A</b>) CAT activity, (<b>B</b>) SOD Activity, (<b>C</b>) POD activity, (<b>D</b>) MDA content. Drought 7 d is the 7th day of drought stress treatment, and Drought 15 d is the 15th day of drought stress treatment. WT indicates wild type; OX indicates ZmD11 over-expression of transgenic lines. Three replicates were used. * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>The transcription level of dehydrin genes. (<b>A</b>) The relative expression of tobacco dehydrin gene <span class="html-italic">NtLeat5</span>. (<b>B</b>) The relative expression of tobacco dehydrin gene <span class="html-italic">NtERD10</span>. Drought 7 d is the 7th day of drought stress treatment, and Drought 15 d is the 15th day of drought stress treatment. WT indicates wild type; OX indicates ZmD11 over-expression of transgenic tobacco lines. Three replicates were used. * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Hormone content in tobacco. (<b>A</b>) The content of BR. (<b>B</b>) The content of SA. (<b>C</b>) The content of ABA. WT indicates wild type; OX indicates over-expression of transgenic tobacco lines. Three biological replicates were used. * <span class="html-italic">p</span> &lt; 0.05.</p>
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17 pages, 5117 KiB  
Article
Combined Pretreatment with Bioequivalent Doses of Plant Growth Regulators Alleviates Dehydration Stress in Lactuca sativa
by Irina I. Vaseva, Iskren Sergiev, Dessislava Todorova, Martynas Urbutis, Giedrė Samuolienė and Lyudmila Simova-Stoilova
Horticulturae 2024, 10(6), 544; https://doi.org/10.3390/horticulturae10060544 - 23 May 2024
Viewed by 1168
Abstract
Plant hormones regulate adaptive responses to various biotic and abiotic stress factors. Applied exogenously, they trigger the natural plant defense mechanisms, a feature that could be implemented in strategies for supporting crop resilience. The potential of the exogenous cytokinin-like acting compound (kinetin), the [...] Read more.
Plant hormones regulate adaptive responses to various biotic and abiotic stress factors. Applied exogenously, they trigger the natural plant defense mechanisms, a feature that could be implemented in strategies for supporting crop resilience. The potential of the exogenous cytokinin-like acting compound (kinetin), the auxin analogue 1-naphtyl acetic acid (NAA), abscisic acid (ABA) and the ethyleneprecursor 1-aminocyclopropane-1-carboxylic acid (ACC) to mitigate dehydration was tested on Lactuca sativa (lettuce) grown on 12% polyethylene glycol (PEG). Priming with different blends containing these plant growth regulators (PGRs) applied in bioequivalent concentrations was evaluated through biometric measurements and biochemical analyses. The combined treatment with the four compounds exhibited the best dehydration protective effect. The antioxidative enzyme profiling of the PGR-primed individuals revealed increased superoxide dismutase (SOD), catalase and peroxidase activity in the leaves. Immunodetection of higher levels of the rate-limiting enzyme for proline biosynthesis (delta-pyroline-5-carboxylate synthase) in the primed plants coincided with a significantly higher content of the amino acid measured in the leaves. These plants also accumulated particular dehydrin types, which may have contributed to the observed stress-relieving effect. The four-component mix applied by spraying or through the roots exerted similar stress-mitigating properties on soil-grown lettuce subjected to moderate drought. Full article
(This article belongs to the Special Issue Horticultural Production under Drought Stress)
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Graphical abstract

Graphical abstract
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<p>Representative phenotype (<b>a</b>), shoot area (<b>b</b>), and fresh (<b>c</b>) and dry weight (<b>d</b>) of the aboveground part of <span class="html-italic">Lactuca sativa</span> plants subjected to different PGR pretreatments and subsequent 10-day dehydration stress provoked by 12% PEG. The graphs show the absolute values of the parameters and the error bars indicate the standard error (SE, <span class="html-italic">n</span> = 20). The asterisk marks statistically significant differences with the PEG-affected “Mock”-treated group (one-way ANOVA with Student’s <span class="html-italic">t</span>-test).</p>
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<p>(<b>a</b>) Malondialdehyde (MDA); (<b>b</b>) free sulfhydryl groups (SH-groups); (<b>c</b>) total phenolic compounds in differently pretreated <span class="html-italic">Lactuca sativa</span> plants grown on nutrient media −/+12% PEG for 10 days. The graphs show the absolute values of the parameters and the error bars indicate the standard error (SE, <span class="html-italic">n</span> = 6). The lowercase letters designate statistically different results (one-way ANOVA with Duncan’s multiple range test at <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Mix 7’s priming effect on antioxidant enzymes in the second true leaf of <span class="html-italic">Lactuca sativa</span> plants grown on nutrient media −/+ 12% PEG for 10 days. (<b>a</b>) Hydrogen peroxide level in the second true leaves of the differently treated plants; (<b>b</b>) <span class="html-italic">Cu/Zn-SOD</span>, <span class="html-italic">Fe-SOD</span> and <span class="html-italic">Mn-SOD</span> transcript accumulation and total SOD activity; (<b>c</b>) <span class="html-italic">CAT</span> transcript accumulation and total CAT activity; (<b>d</b>) <span class="html-italic">POX N1</span>, <span class="html-italic">POX5</span> and <span class="html-italic">APX</span> transcript accumulation and total guaiacol POX activity. The error bars indicate the standard error (SE, <span class="html-italic">n</span> = 3). The lowercase letters designate statistically different results (one-way ANOVA with Duncan’s multiple range test at <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Mix 7 priming effect on L-Proline biosynthesis in <span class="html-italic">Lactuca sativa</span> grown on nutrient media −/+ 12% PEG for 10 days. (<b>a</b>) Immunodetection of P5CS signal (marked with arrow) in leaves of control and PEG-stressed plants that have received “Mock” or “Mix 7” pretreatments. Ponceao-S staining of membrane is shown below the immunoblot. (<b>b</b>) Leaf L-Pro content measured in samples derived from the same individuals analyzed in the immunoblot. Error bars indicate standard error (SE, <span class="html-italic">n</span> = 6). Lowercase letters designate statistically different results (one-way ANOVA with Duncan’s multiple range test at <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Mix 7 priming effect on dehydrin profiles in <span class="html-italic">Lactuca sativa</span> grown on nutrient media −/+ 12% PEG for 10 days. Immunodetection with K-, Y- and S primary antibodies is presented. Mix 7-induced “KYS” signals are marked with “&lt;”, and “KS” with “*”. Ponceao-S staining of the same membranes is shown below the immunoblots to visualize equal protein loading.</p>
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<p>Representative phenotype, fresh weight, dry weight and shoot area of <span class="html-italic">Lactuca sativa</span> plants pretreated with Mix 7 by spraying (<b>a</b>) or through the roots (<b>b</b>) and subjected to moderate soil drought. The error bars indicate the standard deviation (SD, <span class="html-italic">n</span> = 10). The lowercase letters designate statistically different results (one-way ANOVA with Duncan’s multiple range test at <span class="html-italic">p</span> &lt; 0.05).</p>
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15 pages, 1875 KiB  
Article
Comparative Analysis of Dehydrins from Woody Plant Species
by Milan Karas, Dominika Vešelényiová, Eva Boszorádová, Peter Nemeček, Zuzana Gerši and Jana Moravčíková
Biomolecules 2024, 14(3), 250; https://doi.org/10.3390/biom14030250 - 20 Feb 2024
Viewed by 1266
Abstract
We conducted analyses on 253 protein sequences (Pfam00257) derived from 25 woody plant species, including trees, shrubs, and vines. Our goal was to gain insights into their architectural types, biochemical characteristics, and potential involvement in mitigating abiotic stresses, such as drought, cold, or [...] Read more.
We conducted analyses on 253 protein sequences (Pfam00257) derived from 25 woody plant species, including trees, shrubs, and vines. Our goal was to gain insights into their architectural types, biochemical characteristics, and potential involvement in mitigating abiotic stresses, such as drought, cold, or salinity. The investigated protein sequences (253) comprised 221 angiosperms (85 trees/shrubs and 36 vines) and 32 gymnosperms. Our sequence analyses revealed the presence of seven architectural types: Kn, KnS, SKn, YnKn, YnSKn, FSKn, and FnKn. The FSKn type predominated in tree and shrub dehydrins of both gymnosperms and angiosperms, while the YnSKn type was more prevalent in vine dehydrins. The YnSKn and YnKn types were absent in gymnosperms. Gymnosperm dehydrins exhibited a shift towards more negative GRAVY scores and Fold Indexes. Additionally, they demonstrated a higher Lys content and lower His content. By analyzing promoter sequences in the angiosperm species, including trees, shrubs, and vines, we found that these dehydrins are induced by the ABA-dependent and light-responsive pathways. The presence of stress- and hormone-related cis-elements suggests a protective effect against dehydration, cold, or salinity. These findings could serve as a foundation for future studies on woody dehydrins, especially in the context of biotechnological applications. Full article
(This article belongs to the Section Biomacromolecules: Proteins)
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<p>MEME LOGO of K, Y, S, and F segments in dehydrins of woody plants (<b>a</b>). Distribution of dehydrin sequences based on the presence of the K, S, F, and Y segments (<b>b</b>). Distribution of dehydrin sequences based on their structural types (<b>c</b>).</p>
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<p>Phylogenetic tree analysis of dehydrin proteins. The tree was built from 247 dehydrin amino acid sequences (<a href="#app1-biomolecules-14-00250" class="html-app">Table S1</a>). Sequences were clustered into 4 distinct groups (group 1 (<span class="html-italic">n</span> = 94, FSK<sub>n</sub> 84.0%), group 2 (<span class="html-italic">n</span> = 32, FSK<sub>n</sub> 56.3%, F<sub>n</sub>K<sub>n</sub> 21.9%), group 3 (<span class="html-italic">n</span> = 17, K<sub>n</sub>S 88.2%), group 4 (<span class="html-italic">n</span> = 104, Y<sub>n</sub>SK<sub>n</sub> 57.7%, Y<sub>n</sub>K<sub>n</sub> 24.0%)). The bootstrap value was set to 1000. The phylogenetic tree was visualized and graphically edited using the online program ITOL version 5.</p>
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<p>Biochemical properties of dehydrins in woody plants categorized by the gymnosperm (trees) and angiosperm (trees/shrubs and vines) dehydrins. Molecular weight (Mw) (<b>a</b>), isoelectric point (pI) (<b>b</b>), GRAVY score (<b>c</b>), fold index (<b>d</b>), Lys content (<b>e</b>), His content (<b>f</b>).</p>
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<p>The frequency of occurrence of cis-acting regulatory elements in the promoter sequences of dehydrin genes with the <span class="html-italic">Y</span>-axis representing the percentage of promoter sequences containing the corresponding cis element at least once.</p>
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23 pages, 2783 KiB  
Review
Stone Pine (Pinus pinea L.) High-Added-Value Genetics: An Overview
by Ana Sofia B. Simões, Margarida Machado Borges, Liliana Grazina and João Nunes
Genes 2024, 15(1), 84; https://doi.org/10.3390/genes15010084 - 10 Jan 2024
Viewed by 1920
Abstract
Stone pine (Pinus pinea L.) has received limited attention in terms of genetic research. However, genomic techniques hold promise for decoding the stone pine genome and contributing to developing a more resilient bioeconomy. Retrotransposon and specific genetic markers are effective tools for [...] Read more.
Stone pine (Pinus pinea L.) has received limited attention in terms of genetic research. However, genomic techniques hold promise for decoding the stone pine genome and contributing to developing a more resilient bioeconomy. Retrotransposon and specific genetic markers are effective tools for determining population-specific genomic diversity. Studies on the transcriptome and proteome have identified differentially expressed genes PAS1, CLV1, ATAF1, and ACBF involved in shoot bud formation. The stone pine proteome shows variation among populations and shows the industrial potential of the enzyme pinosylvin. Microsatellite studies have revealed low levels of polymorphism and a unique genetic diversity in stone pine, which may contribute to its environmental adaptation. Transcriptomic and proteomic analyses uncover the genetic and molecular responses of stone pine to fungal infections and nematode infestations, elucidating the defense activation, gene regulation, and the potential role of terpenes in pathogen resistance. Transcriptomics associated with carbohydrate metabolism, dehydrins, and transcription factors show promise as targets for improving stone pine’s drought stress response and water retention capabilities. Stone pine presents itself as an important model tree for studying climate change adaptation due to its characteristics. While knowledge gaps exist, stone pine’s genetic resources hold significant potential, and ongoing advancements in techniques offer prospects for future exploration. Full article
(This article belongs to the Special Issue Genetic Research and Plant Breeding 2.0)
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<p>Results of the bibliographic research divided by theme and frequency of occurrence. The graph represents a total of 51 references.</p>
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<p>Chronology of studies on Stone pine’s genome [<a href="#B21-genes-15-00084" class="html-bibr">21</a>,<a href="#B22-genes-15-00084" class="html-bibr">22</a>,<a href="#B23-genes-15-00084" class="html-bibr">23</a>,<a href="#B24-genes-15-00084" class="html-bibr">24</a>,<a href="#B25-genes-15-00084" class="html-bibr">25</a>,<a href="#B26-genes-15-00084" class="html-bibr">26</a>,<a href="#B27-genes-15-00084" class="html-bibr">27</a>].</p>
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<p>Disease resistance mechanisms identified for stone pine.</p>
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<p>Enzymes and proteins involved in stone pine’s <span class="html-italic">B. xylophilus</span> infection resistance.</p>
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<p>Drought-response-related parameters in stone pine and associated genetic processes (population genetics, hereditary traits, or species evolution).</p>
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<p>Characteristics involved in phenotypic plasticity in stone pine.</p>
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