Plant Responses to Environmental Stress

Topic Information

Dear Colleagues,

Plant growth and development are constantly exposed to biotic and abiotic stresses, such as drought, salinity, extreme temperature, UV radiation, high light, nutrient deficiency, insects, pathogens, and weeds. These the main reasons behind reductions in crop yields and food production worldwide. As a result, for example due to drought stress, remarkable changes occur in plant growth, photosynthesis, enzymatic activities, nutrient uptake, and biomass production. The decreased photosynthetic efficiency that is linked to both stomatal and nonstomatal effects is the result of a disruption of either biochemical or/and photochemical activity and increased oxidative damage by surplus reactive oxygen species (ROS) accumulation, which can harm the chloroplast and particularly photosystem II (PSII). Several studies have revealed that the concurrent action of many stresses, e.g., drought stress, high temperature, and high light, constantly cause deeper effects than when acting separately. Thus, there is a need for studies focusing on multiple stressors that occur at once. At the same time, plants have developed several energetic approaches at the morphological, physiological, and biochemical levels, allowing them to avoid and/or tolerate biotic and abiotic stresses. Environmental-stress-induced ROS creation is scavenged by enzymatic and nonenzymatic antioxidants. Plant responses to a disruption of homeostasis caused by a low environmental stress level display an overcompensation reaction that results in a hormetic stimulation. Understanding the way plants respond to biotic and abiotic stresses is an ongoing research topic. This Research Topic will highlight the mechanisms of plant responses to such stresses and, thus, can help in the development of realistic interventions for increasing agricultural productivity. Hence, detecting steps or mechanisms where plant response mechanisms are suboptimal under different environmental conditions, and then optimizing these steps for a better response, represents a key research target in the efforts to increase the ability of crop plants to face climate change which can detrimentally influence crop production. To meet global food and feed requirements, considering the current climate change crisis, it is essential to recognize how plants respond and adapt their metabolism to environmental stresses.

Dr. Julietta Moustaka
Prof. Dr. Michael Moustakas
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Agriculture agriculture	3.3	4.9	2011	20.2 Days	CHF 2600
Agronomy agronomy	3.3	6.2	2011	15.5 Days	CHF 2600
Plants plants	4.0	6.5	2012	18.2 Days	CHF 2700
Stresses stresses	-	4.7	2021	20.3 Days	CHF 1000

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14 pages, 724 KiB  

Open AccessReview

Effects of LED Light on Aromatic Medicinal Plants from Lavandula, Salvia, and Thymus Genera: A Systematic Review

by Gustavo J. Cáceres-Cevallos and María J. Jordán

Stresses 2024, 4(4), 627-640; https://doi.org/10.3390/stresses4040040 (registering DOI) - 30 Sep 2024

Abstract

LED light technology has been used in recent years in plant breeding due to its proven energy efficiency, low cost, and high quality for the enhancement of crops, including some aromatic medicinal plants (AMPs). Nonetheless, although several studies have shown that specific wavelengths [...] Read more.

LED light technology has been used in recent years in plant breeding due to its proven energy efficiency, low cost, and high quality for the enhancement of crops, including some aromatic medicinal plants (AMPs). Nonetheless, although several studies have shown that specific wavelengths can increase the content of bioactive compounds used by pharmaceutical, medical, and perfumery industries, there is limited information on this topic and the possible implications for plant stress in AMPs. The current systematic review focused on the effects of LED light on the physiological response, metabolite synthesis, and flowering induction in three important AMP genera: Lavandula, Salvia, and Thymus, belonging to the Lamiaceae family. A literature search was performed in the Web of Science and Scopus databases. This review is reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The bibliographic analysis highlights the significant variation in physiological responses to different light spectra between species, even within the same genera, implying a need to optimize light conditions in each species to achieve the best results. Finally, this review provides essential information for laying the groundwork for future research focused on enhancing AMPs using LED light to overcome various types of stress. Full article

(This article belongs to the Topic Plant Responses to Environmental Stress)

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<p>Preferred Reporting Items in Systematic Reviews and Meta-Analyses (PRISMA) flowchart showing the process of the literature search and publication selection.</p> Full article ">

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Supplementary material:
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13 pages, 1953 KiB  

Open AccessArticle

A Maize Mutant Impaired in SL Biosynthesis (zmccd8) Shows a Lower Growth, an Altered Response to Nitrogen Starvation, and a Potential Secondary Effect on Drought Tolerance

by Laura Ravazzolo, Andrea Chichi, Franco Meggio, Leonardo Buzzicotti, Benedetto Ruperti, Serena Varotto, Mario Malagoli and Silvia Quaggiotti

Stresses 2024, 4(4), 614-626; https://doi.org/10.3390/stresses4040039 - 25 Sep 2024

Abstract

Strigolactones (SLs) are essential phytohormones involved in plant development and interaction with the rhizosphere, regulating shoot branching, root architecture, and leaf senescence for nutrient reallocation. The Zea mays L. zmccd8 mutant, defective in SL biosynthesis, shows various architectural changes and reduced growth. This [...] Read more.

Strigolactones (SLs) are essential phytohormones involved in plant development and interaction with the rhizosphere, regulating shoot branching, root architecture, and leaf senescence for nutrient reallocation. The Zea mays L. zmccd8 mutant, defective in SL biosynthesis, shows various architectural changes and reduced growth. This study investigates zmccd8 and wild-type (WT) maize plants under two nutritional treatments (N-shortage vs. N-provision as urea). Morphometric analysis, chlorophyll and anthocyanin indexes, drought-related parameters, and gene expression were measured at specific time points. The zmccd8 mutant displayed reduced growth, such as shorter stems, fewer leaves, and lower kernel yield, regardless of the nutritional regime, confirming the crucial role of SLs. Additionally, zmccd8 plants exhibited lower chlorophyll content, particularly under N-deprivation, indicating SL necessity for proper senescence and nutrient mobilization. Increased anthocyanin accumulation in zmccd8 under N-shortage suggested a stress mitigation attempt, unlike WT plants. Furthermore, zmccd8 plants showed signs of increased water stress, likely due to impaired stomatal regulation, highlighting SLs role in drought tolerance. Molecular analysis confirmed higher expression of SL biosynthesis genes in WT under N-shortage, while zmccd8 lacked this response. These findings underscore SL importance in maize growth, stress responses, and nutrient allocation, suggesting potential agricultural applications for enhancing crop resilience. Full article

(This article belongs to the Topic Plant Responses to Environmental Stress)

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Figure 1
<p>Phenotypic analysis of stem height (<b>A</b>), leaf number (<b>B</b>), internode length (<b>C</b>), stem circumference (<b>D</b>), kernel weight (<b>E</b>), and ears length (<b>F</b>) for wild-type (WT) and <span class="html-italic">zmccd8</span> mutant plants at different days after sowing (DAS) under two N treatments. Error bars represent the mean ± SE (<span class="html-italic">n</span> = 24). At 58 DAS, urea was provided as the N source (dashed red line). Different letters indicate significant differences (at <span class="html-italic">p</span> &lt; 0.05 according to LSD test) at each DAS. Based on ANOVA, the significance of F values was reported as follows: ‘***’ <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; no asterisks <span class="html-italic">p</span> &gt; 0.05.</p> Full article ">Figure 2
<p>Profiles in chlorophyll content (<b>A</b>) and anthocyanin levels (<b>B</b>) in four different groups of maize leaves (L1-2; L3-4-5; L6-7-8; L9-10-11). Error bars represent the mean of six biological replicates ± SE. At 58 DAS, urea was provided as the N source (dashed red line). Different letters indicate significant differences (at <span class="html-italic">p</span> &lt; 0.05 according to LSD test) at each DAS. Based on ANOVA, the significance of F values was reported as follows: ‘***’ <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; no asterisks <span class="html-italic">p</span> &gt; 0.05.</p> Full article ">Figure 3
<p>Profiles of stomatal conductance (gsw, mol H₂O m⁻²s⁻¹) (<b>A</b>), leaf transpiration (E-app, mol H₂O m⁻²s⁻¹) (<b>B</b>), and photosystem II efficiency (PhiPS II) (<b>C</b>) in the group of leaves 9-10-11. Error bars represent the mean of six biological replicates ± SE. At 58 DAS, urea was provided as the N source (dashed red line). Different letters indicate significant differences (at <span class="html-italic">p</span> &lt; 0.05 according to LSD test) at each DAS. Based on ANOVA, the significance of F values was reported as follows: ‘***’ <span class="html-italic">p</span> &lt; 0.001; ‘*’ <span class="html-italic">p</span> &lt; 0.05; no asterisks <span class="html-italic">p</span> &gt; 0.05.</p> Full article ">Figure 4
<p>Relative gene expression of three genes involved in SL biosynthesis (<span class="html-italic">CCD7</span>, <span class="html-italic">CCD8</span>), signalling (<span class="html-italic">MAX2</span>), and drought stress (<span class="html-italic">SULTR6</span>) in leaf samples at three different days after sowing (DAS). Data are means ± SE for three biological replicates. Different letters indicate significant differences (at <span class="html-italic">p</span> &lt; 0.05 according to LSD test) at each DAS. Based on ANOVA, the significance of F values was reported as follows: ‘***’ <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; no asterisks <span class="html-italic">p</span> &gt; 0.05.</p> Full article ">

18 pages, 2284 KiB  

Open AccessArticle

Foliar H₂O₂ Application Improve the Photochemical and Osmotic Adjustment of Tomato Plants Subjected to Drought

by Gustavo Ribeiro Barzotto, Caroline Pardine Cardoso, Letícia Galhardo Jorge, Felipe Girotto Campos and Carmen Sílvia Fernandes Boaro

Agriculture 2024, 14(9), 1572; https://doi.org/10.3390/agriculture14091572 - 10 Sep 2024

Abstract

Water limits may have a disastrous impact on agricultural productivity, and the current climate change scenario presents additional problems for crops that rely on regular rainfall. Reactive oxygen species, such as hydrogen peroxide (H₂O₂), are a recognized stress-sensing mechanism [...] Read more.

Water limits may have a disastrous impact on agricultural productivity, and the current climate change scenario presents additional problems for crops that rely on regular rainfall. Reactive oxygen species, such as hydrogen peroxide (H₂O₂), are a recognized stress-sensing mechanism in plants, and may be investigated as an approach for reducing stress impact via systemic acquired acclimation. Here, we looked at how H₂O₂ foliar application impacts tomato plants’ photosynthetic activity, antioxidant system, sugar chemical profile, and osmotic adjustment during drought and recovery. The experiment was in randomized blocks, 3 × 2 factorial design, with no, one, or two foliar application of 1 mM H₂O₂, on plants that were either continually watered or subjected to drought. The plants were tested both during the drought period and after they had resumed irrigation (recovered). Leaf water potential, chlorophyll a fluorescence, gas exchange, lipid peroxidation, H₂O₂ concentrations, phenols, proline, antioxidant enzyme activity, and sugar chemical profile were all measured. Our findings showed that H₂O₂ application generated metabolic alterations in tomato plants independent of water status, and that two applications in drought plants resulted in a 30% decrease in oxidative stress during drought and faster recovery following irrigation return, with greater production of defence-related molecules such as the APX enzyme, phenols, arabinose, and mannose. Continually watered plants also benefited from H₂O₂ application, which increased carbon assimilation by 35%. Full article

(This article belongs to the Topic Plant Responses to Environmental Stress)

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Figure 1

Figure 1
<p>(<b>a</b>) Leaf water potential of tomato plants grown under optimal water supply (watered) or subjected to drought, and (<b>b</b>) after irrigation return, plants continually watered and recovered plants. Values correspond to the mean ± confidence interval (n = 4). Different letters differentiate means using the Tukey test (&lt;0.05).</p> Full article ">Figure 2
<p>(<b>a</b>,<b>b</b>) Effective quantum yield of photosystem II (Φ_PSII), (<b>c</b>) non-photochemical fluorescence quenching coefficient (<span class="html-italic">qN</span>), (<b>d</b>) heat dissipation in the antenna complex (<span class="html-italic">D</span>) and (<b>e</b>) energy not dissipated and not used in the photochemical phase (<span class="html-italic">Ex</span>) in tomato plants grown under optimal water supply (watered) or subjected to drought and no, one, or two foliar applications of H₂O₂. After recovery due to irrigation return, (<b>f</b>) Φ_PSII, (<b>g</b>) qN, (<b>h</b>) <span class="html-italic">D</span> and (<b>i</b>) <span class="html-italic">Ex</span>. Values correspond to the mean ± confidence interval (n = 4). Different letters differentiate the means using the Tukey test (&lt;0.05), lowercase letters compare H₂O₂ application within each water condition and uppercase letters compare water condition within H₂O₂ application.</p> Full article ">Figure 3
<p>(<b>a</b>) Transpiration rate (<span class="html-italic">E</span>), (<b>b</b>) stomatal conductance (<span class="html-italic">Gs</span>), (<b>c</b>) net CO₂ assimilation (<span class="html-italic">A</span>) and (<b>d</b>) instantaneous water use efficiency (WUEi) in tomato plants grown under optimal water supply (watered) or subjected to drought and no, one or two foliar applications of H₂O₂. After recovery due irrigation return, (<b>e</b>) <span class="html-italic">E</span>, (<b>f</b>) <span class="html-italic">Gs</span>, (<b>g</b>) <span class="html-italic">A</span> and (<b>h</b>) WUEi. Values correspond to the mean ± confidence interval (n = 4). Different letters differentiate the means using the Tukey test (&lt;0.05), lowercase letters compare H₂O₂ application within each water condition, and uppercase letters compare water condition within H₂O₂ application.</p> Full article ">Figure 4
<p>(<b>a</b>) Lipid peroxidation (MDA) and (<b>b</b>,<b>c</b>) concentration of hydrogen peroxide (H₂O₂) in leaf tomato plants grown under optimal water supply (watered) or subjected to drought and no, one, or two foliar applications of H₂O₂. After recovery due to irrigation return, (<b>d</b>) MDA, (<b>e</b>,<b>f</b>) H₂O₂. Values correspond to the mean ± confidence interval (n = 4). Different letters differentiate the means using the Tukey test (&lt;0.05), lowercase letters compare H₂O₂ application within each water condition, and uppercase letters compare water condition within H₂O₂ application.</p> Full article ">Figure 5
<p>Antioxidant enzyme activity. (<b>a</b>) Superoxide dismutase (SOD), (<b>b</b>) catalase (CAT), (<b>c</b>) peroxidase (POD), and (<b>d</b>) ascorbate peroxidase (APX) in tomato plants grown under optimal water supply (watered) or subjected to drought and no, one, or two foliar applications of H₂O₂. After recovery due to irrigation return, (<b>e</b>) SOD, (<b>f</b>) CAT, (<b>g</b>) POD, and (<b>h</b>) APX. Values correspond to the mean ± confidence interval (n = 4). Different letters differentiate the means using the Tukey test (&lt;0.05), lowercase letters compare H₂O₂ application within each water condition, and uppercase letters compare water condition within H₂O₂ application.</p> Full article ">Figure 6
<p>(<b>a</b>) Concentration of total phenols and (<b>b</b>) concentration of proline in leaf tomato plants grown under optimal water supply (watered) or subjected to drought and no, one, or two foliar applications of H₂O₂. After recovery due to irrigation return, (<b>c</b>) total phenols, (<b>d</b>) proline. Values correspond to the mean ± confidence interval (n = 4). Different letters differentiate the means using the Tukey test (&lt;0.05), lowercase letters compare H₂O₂ application within each water condition, and uppercase letters compare water condition within H₂O₂ application.</p> Full article ">Figure 7
<p>Soluble sugars leaf concentrations. (<b>a</b>) Trehalose, (<b>b</b>) arabinose, (<b>c</b>) mannose, (<b>d</b>) glucose, (<b>e</b>) fructose, and (<b>f</b>) sucrose in tomato plants grown under optimal water supply (watered) or subjected to drought and no, one, or two foliar applications of H₂O₂. After recovery due to irrigation return, (<b>g</b>) trehalose, (<b>h</b>) mannose, (<b>i</b>) glucose, (<b>j</b>) fructose, and (<b>k</b>) sucrose. Values correspond to the mean ± confidence interval (n = 4). Different letters differentiate the means using the Tukey test (&lt;0.05), lowercase letters compare H₂O₂ application within each water condition, and uppercase letters compare water condition within H₂O₂ application.</p> Full article ">Figure 8
<p>Heatmap. (<b>a</b>) During the drought period and (<b>b</b>) after recovery by the return of irrigation. Water potential of leaf (WP4), effective quantum yield of photosystem II (Yield), non-photochemical quenching coefficient of fluorescence (qN), heat dissipation in the antenna complex (<span class="html-italic">D</span>), energy not dissipated and not used in the photochemical phase (<span class="html-italic">Ex</span>), transpiration rate (<span class="html-italic">E</span>), stomatal conductance (<span class="html-italic">Gs</span>), net CO₂ assimilation (<span class="html-italic">A)</span>, instantaneous water use efficiency (WUE), leaf lipid peroxidation (MDA), leaf concentration of hydrogen peroxide (H₂O₂), activity of superoxide dismutase (SOD), catalase (CAT), peroxidase (POD) and ascorbate peroxidase (APX), leaf concentration of total phenols (PHENOL), proline (PROL), trehalose (TREHA), arabinose (ARAB), mannose (MANNOS), glucose (GLUCO), fructose (FRUT), and sucrose (SUCRO) in tomato plants grown under optimal water supply (watered—WP/continually watered—CW) or subjected to drought (DP)/recovery (RP) and no, one, or two foliar applications of H₂O_2.</p> Full article ">

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23 pages, 6157 KiB  

Open AccessArticle

Stomatal and Non-Stomatal Leaf Responses during Two Sequential Water Stress Cycles in Young Coffea canephora Plants

by Danilo F. Baroni, Guilherme A. R. de Souza, Wallace de P. Bernado, Anne R. Santos, Larissa C. de S. Barcellos, Letícia F. T. Barcelos, Laísa Z. Correia, Claudio M. de Almeida, Abraão C. Verdin Filho, Weverton P. Rodrigues, José C. Ramalho, Miroslava Rakočević and Eliemar Campostrini

Stresses 2024, 4(3), 575-597; https://doi.org/10.3390/stresses4030037 - 9 Sep 2024

Abstract

Understanding the dynamics of physiological changes involved in the acclimation responses of plants after their exposure to repeated cycles of water stress is crucial to selecting resilient genotypes for regions with recurrent drought episodes. Under such background, we tried to respond to questions [...] Read more.

Understanding the dynamics of physiological changes involved in the acclimation responses of plants after their exposure to repeated cycles of water stress is crucial to selecting resilient genotypes for regions with recurrent drought episodes. Under such background, we tried to respond to questions as: (1) Are there differences in the stomatal-related and non-stomatal responses during water stress cycles in different clones of Coffea canephora Pierre ex A. Froehner? (2) Do these C. canephora clones show a different response in each of the two sequential water stress events? (3) Is one previous drought stress event sufficient to induce a kind of “memory” in C. canephora? Seven-month-old plants of two clones (’3V’ and ‘A1’, previously characterized as deeper and lesser deep root growth, respectively) were maintained well-watered (WW) or fully withholding the irrigation, inducing soil water stress (WS) until the soil matric water potential (Ψ_msoil) reached ≅ −0.5 MPa (−500 kPa) at a soil depth of 500 mm. Two sequential drought events (drought-1 and drought-2) attained this Ψ_msoil after 19 days and were followed by soil rewatering until a complete recovery of leaf net CO₂ assimilation rate (A_net) during the recovery-1 and recovery-2 events. The leaf gas exchange, chlorophyll a fluorescence, and leaf reflectance parameters were measured in six-day frequency, while the leaf anatomy was examined only at the end of the second drought cycle. In both drought events, the WS plants showed reduction in stomatal conductance and leaf transpiration. The reduction in internal CO₂ diffusion was observed in the second drought cycle, expressed by increased thickness of spongy parenchyma in both clones. Those stomatal and anatomical traits impacted decreasing the A_net in both drought events. The ‘3V’ was less influenced by water stress than the ‘A1’ genotype in A_net, effective quantum yield in PSII photochemistry, photochemical quenching, linear electron transport rate, and photochemical reflectance index during the drought-1, but during the drought-2 event such an advantage disappeared. Such physiological genotype differences were supported by the medium xylem vessel area diminished only in ‘3V’ under WS. In both drought cycles, the recovery of all observed stomatal and non-stomatal responses was usually complete after 12 days of rewatering. The absence of photochemical impacts, namely in the maximum quantum yield of primary photochemical reactions, photosynthetic performance index, and density of reaction centers capable of Q_A reduction during the drought-2 event, might result from an acclimation response of the clones to WS. In the second drought cycle, the plants showed some improved responses to stress, suggesting “memory” effects as drought acclimation at a recurrent drought. Full article

(This article belongs to the Topic Plant Responses to Environmental Stress)

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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Soil matric water potential (Ψ_msoil) at 100 mm cm and 500 mm from the soil surface in the pots of the <span class="html-italic">C. canephora</span> var. Robusta genotypes of (<b>A</b>) ‘3V’ and (<b>B</b>) ‘A1’ under well-watered (WW) and water stressed (WS) conditions. The water restriction was imposed during the drought-1 and drought-2 events, after which the soil was rewatered (and recovery-1 and recovery-2 events).</p> Full article ">Figure 2
<p>Leaf gas exchanges of two genotypes (Gen) of <span class="html-italic">C. canephora</span> var. Robusta (‘3V’ and ‘A1’) grown under two water availability conditions [Wat, well-watered (WW) and water stress (WS)], over 12 time-points of six-day intervals (Day) during drought-1 and drought-2 and respective recovery events: (<b>A</b>) net CO₂ assimilation rate (<span class="html-italic">A</span>_net), (<b>B</b>) stomatal conductance to water (<span class="html-italic">g</span>_s), (<b>C</b>) transpiration rate (<span class="html-italic">E</span>), and (<b>D</b>) leaf-to-air vapor pressure deficit (VPD_leaf-air). Inside the figures, the different lowercase letters indicate the significant difference among the time-points for each water regime (blue for WW and olive green for WS); different uppercase letters indicate the comparison between water availabilities for each time-point of observation (blue for WW and olive green for WS); and different superscript black ■ signs indicate that ‘3V’ was statistically superior to ‘A1’ at that time-point. Mean ± SE and ANOVA <span class="html-italic">p</span>-values (n = 7) for effects of three factors (water availability, genotype, and day of observation) and their interactions are shown. The significant <span class="html-italic">p</span>-values were marked in bold in the upper part of each graph.</p> Full article ">Figure 3
<p>Instantaneous water-use efficiency (WUE, <span class="html-italic">A</span>_net/<span class="html-italic">E</span>) of two genotypes (Gen) of <span class="html-italic">C. canephora</span> var. Robusta (‘3V’ and ‘A1’) grown under two water availability conditions [Wat, well-watered (WW) and water stress (WS)], over 12 time-points of six-day intervals (Day) during drought-1 and drought-2 and respective recovery events. Inside the figure, different lowercase letters indicate the significant difference among the day-time points for each water regime (blue for WW and olive green for WS); different uppercase letters indicate the comparison between water availabilities for each time-point of observation (blue for WW and olive green for WS). Mean ± SE and ANOVA <span class="html-italic">p</span>-values (n = 7) for effects of three factors (water availability, genotype, and day of observation) and their interactions are shown. The significant <span class="html-italic">p</span>-values were marked in bold in the upper part of each graph.</p> Full article ">Figure 4
<p>Variation of OJIP indexes of two genotypes (Gen) of <span class="html-italic">C. canephora</span> var. Robusta (‘3V’ and ‘A1’) grown under two water availability conditions [Wat, well-watered (WW) and water stress (WS)] over 12 time-points of six-day intervals (Day) during drought-1 and drought-2 and respective recovery events: (<b>A</b>) maximum quantum yield of primary photochemical reactions (ΦP₀), (<b>B</b>) probability of electron transfer from Q_A-to-electron transport chain beyond Q_A (ΨE₀), (<b>C</b>) photosynthetic performance index (PI_ABS), and (<b>D</b>) density of reaction centers capable of Q_A reduction (RC/CS₀). Inside the figures, the different lowercase letters indicate the significant difference among the time-points for each water regime (blue for WW and olive green for WS); different uppercase letters indicate the comparison between water availabilities for each day of observation (blue for WW and olive green for WS); superscript black ■ signs indicate that ‘3V’ was statistically superior to ‘A1’, while superscript black ● signs indicate that ‘A1’ clone was statistically superior to ‘3V’ clone at that time-point. Mean ± SE and ANOVA <span class="html-italic">p</span>-values (n = 7) for effects of three factors (water availability, genotype, and day of observation) and their interactions are shown. The significant <span class="html-italic">p</span>-values were marked in bold.</p> Full article ">Figure 5
<p>Variation of modulated chlorophyll <span class="html-italic">a</span> fluorescence indexes of two genotypes (Gen) of <span class="html-italic">C. canephora</span> var. Robusta (‘3V’ and ‘A1’) grown under two water availability conditions [Wat, well-watered (WW) and water stress (WS)] over 12 time-points of six-day intervals (Day) during drought-1 and drought-2 and respective recovery events: (<b>A</b>) effective quantum yield in PSII photochemistry (Φ_PSII), (<b>B</b>) photochemical quenching (qP), (<b>C</b>) non-photochemical quenching (NPQ), and (<b>D</b>) linear electron transport rate (ETR). Inside the figures, the different lowercase letters indicate the significant difference among the time-points for each water regime (blue for WW and olive green for WS); different uppercase letters indicate the comparison between water availabilities for each day of observation (blue for WW and olive green for WS); different superscript black ■ signs indicate that ‘3V’ was statistically superior to ‘A1’, while superscript black ● signs indicate that ‘A1’ clone was statistically superior to ‘3V’ clone at that time-point. Mean ± SE and ANOVA <span class="html-italic">P</span>-values (n = 7) for effects of three factors (water availability, genotype, and day of observation) and their interactions are shown. The significant <span class="html-italic">P</span>-values were marked in bold.</p> Full article ">Figure 6
<p>Variation of spectral reflectance indices of leaf adaxial surface of two genotypes (Gen) of C. canephora var. Robusta (‘3V’ and ‘A1’) grown under two water availability conditions [Wat, well-watered (WW) and water stress (WS)] over 12 time-points of six-day intervals (Day) during drought-1 and drought-2 and respective recovery events: (<b>A</b>) green chlorophyll index (GCI), (<b>B</b>) carotenoid reflectance index (CRI), (<b>C</b>) photochemical reflectance index (PRI), and (<b>D</b>) structure intensive reflectance index (SIPI). Inside the figures, the different lowercase letters indicate the significant difference among the time-points for each water regime (blue for WW and olive green for WS); different uppercase letters indicate the comparison between water availabilities for each day of observation (blue for WW and olive green for WS); different superscript black ■ signs indicate that ‘3V’ was statistically superior to ‘A1’, while superscript black ● signs indicate that ‘A1’ clone was statistically superior to ‘3V’ clone at that time-point. Mean ± SE and ANOVA <span class="html-italic">p</span>-values (n = 7) for effects of three factors (water availability, genotype, and day of observation) and their interactions are shown. The significant <span class="html-italic">p</span>-values were marked in bold.</p> Full article ">Figure 7
<p>Representative area of leaf xylem vessel (µm²) measured in <span class="html-italic">C. canephora</span> var. Robusta clones (‘3V’ and ‘A1’) under well-watered (WW) and water stress (WS) conditions: (<b>A</b>) A1-WW, (<b>B</b>) 3V-WW, (<b>C</b>) A1-WS, and (<b>D</b>) 3V-WS, evaluated at the end of the second drought cycle. A scale of 100 µm is shown.</p> Full article ">Figure 8
<p>Diagram of the two drought cycles. Transplant followed by drought-1 event last for 19 days (until −500 kPa of Ψ_msoil was reached), followed by a 31-day period for a whole plant recovery (including 12-day period of recovery-1 event). The 2nd drought cycle was then applied, similarly to the 1st drought cycle, by withholding irrigation until the −500 kPa of Ψ_msoil was reached (drought-2 event) and followed by another 12 days of recovery-2 event.</p> Full article ">

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11 pages, 7191 KiB  

Open AccessArticle

The Small Auxin-Up RNA 50 (SAUR50) Gene from Ammopiptanthus nanus Negatively Regulates Drought Tolerance

by Yuanyuan Zhang, Qi Li, Mengyang Jiang, Hui Tian, Muhammad Hayder Bin Khalid, Yingge Wang and Haoqiang Yu

Plants 2024, 13(17), 2512; https://doi.org/10.3390/plants13172512 - 7 Sep 2024

Abstract

Drought stress is a primary abiotic stress that causes significant losses to forestry and agricultural production. Therefore, exploring drought-responsive genes and their regulatory mechanism is crucial for plant molecular breeding for forestry and agriculture production safety. Small auxin-up RNA (SAUR) proteins are essential [...] Read more.

Drought stress is a primary abiotic stress that causes significant losses to forestry and agricultural production. Therefore, exploring drought-responsive genes and their regulatory mechanism is crucial for plant molecular breeding for forestry and agriculture production safety. Small auxin-up RNA (SAUR) proteins are essential in plant growth and development but show functional diversity in stress response. In this study, the transcriptome sequencing data of Ammopiptanthus nanus seedlings revealed that the expression of AnSAUR50 was continuously downregulated under drought stress. Hence, the AnSAUR50 gene was cloned and functionally analyzed in drought response. The results showed that the coding sequence of AnSAUR50 was 315 bp in length and encoded 104 amino acids. The AnSAUR50 protein showed high conservation, possessed a SAUR-specific domain, and localized in the nucleus and cell membrane. The heterologous expression of the AnSAUR50 gene enhanced the drought sensitivity of the transgenic Arabidopsis with a lower survival rate, biomass, and higher malondialdehyde content and relative electrolyte leakage. Moreover, transgenic plants showed shorter root lengths and bigger stomatal apertures, resulting in facilitating water loss under drought stress. The study indicates that AnSAUR50 negatively regulates drought tolerance by inhibiting root growth and stomatal closure, which provides insights into the underlying function and regulatory mechanism of SAURs in plant stress response. Full article

(This article belongs to the Topic Plant Responses to Environmental Stress)

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Figure 1

Figure 1
<p>Identification of AnSAUR50. (<b>A</b>) The phylogenetic tree of AnSAUR50 and other SAURs. (<b>B</b>) Multiple alignments of AnSAUR50 and other SAURs, indicating that the SAUR-specific domain is highly conserved. Red, blue and gray backgrounds mean 100%, 75% and 50% conservation, respectively. (<b>C</b>) The predicted structure of AnSAUR50 and CcSAUR50 protein. (<b>D</b>) Expression pattern of <span class="html-italic">AnSAUR50</span> gene under polyethylene glycol (PEG) treatment for simulating drought conditions. The RNA-seq data were retrieved from our previous project (PRJNA684798). Box indicates the SAUR-specific domain. The black dot means AnSAUR50. ** indicates statistical significance at <span class="html-italic">p</span> &lt; 0.01 level.</p> Full article ">Figure 2
<p>Subcellular localization of AnSAUR50. (<b>A</b>) The diagram of expression vector. <span class="html-italic">35S</span> promoter was used to drive <span class="html-italic">AnSAUR50</span>-<span class="html-italic">eGFP</span>, and <span class="html-italic">eGFP</span> gene. <span class="html-italic">OCS</span>, the 3’-flanking region of octopine synthase gene, was used as terminator. (<b>B</b>) The photos of eGFP fluorescence signal in the cells transformed by <span class="html-italic">35S</span>-<span class="html-italic">AnSAUR50</span>-<span class="html-italic">eGFP</span> and <span class="html-italic">35S</span>-<span class="html-italic">eGFP</span>. Scar bar was 50 μm.</p> Full article ">Figure 3
<p>Identification of transgenic lines. (<b>A</b>) The diagram of expression vectors. The <span class="html-italic">35S</span> and <span class="html-italic">OCS</span> sequences were used to drive and terminate <span class="html-italic">AnSAUR50</span> gene. <span class="html-italic">MASp</span> and <span class="html-italic">MASt</span> mean promoter and terminator of mannopine synthase. <span class="html-italic">BlpR</span>, encoding phosphinothricin acetyltransferase confers resistance to bialophos or phosphinothricin. (<b>B</b>) Phenotype of basta screening for transgenic plants. (<b>C</b>) PCR detection of transgenic lines. M, DNA marker DL2000. L1 to L6 were T₁ transgenic lines overexpressing <span class="html-italic">AnSAUR50</span>. (<b>D</b>) RT-PCR analysis. The <span class="html-italic">AtUBQ5</span> was used as a reference gene. WT, wild type. L1 and L3 were homozygous lines expressing <span class="html-italic">AnSAUR50</span> gene.</p> Full article ">Figure 4
<p>Overexpression of <span class="html-italic">AnSAUR50</span> enhanced drought sensitivity of transgenic <span class="html-italic">Arabidopsis</span>. (<b>A</b>) Phenotype of <span class="html-italic">A. thaliana</span> transformed by <span class="html-italic">AnSAUR50</span> under drought stress. (<b>B</b>) Survival rate of each line after drought stress. (<b>C</b>) Biomass of every line after drought stress. (<b>D</b>) Malondialdehyde (MDA) content. (<b>E</b>). Relative electrolyte leakage (REL). WT, wild type. L1 and L3 were homozygous lines expressing the <span class="html-italic">AnSAUR50</span> gene. ** indicates statistical significance at <span class="html-italic">p</span> &lt; 0.01 level.</p> Full article ">Figure 5
<p>Overexpression of <span class="html-italic">AnSAUR50</span> resulted in root growth retardation of transgenic <span class="html-italic">Arabidopsis</span> under simulated drought conditions. (<b>A</b>) Root phenotype. (<b>B</b>) The statistics of root length. WT, wild type. L1 and L3 were homozygous lines expressing <span class="html-italic">AnSAUR50</span> gene. ** indicates statistical significance at <span class="html-italic">p</span> &lt; 0.01 level.</p> Full article ">Figure 6
<p>Overexpression of <span class="html-italic">AnSAUR50</span> altered stomatal aperture of transgenic <span class="html-italic">Arabidopsis</span>. (<b>A</b>) Phenotype of stoma of each line. The leaf samples of every line were excised for 90 min of dehydration and used to detect stomatal aperture. Scar bar = 20 µm. (<b>B</b>) The statistical data of stomatal aperture. (<b>C</b>) Water loss rate of detached leaves. WT, wild type. L1 and L3 were homozygous lines expressing <span class="html-italic">AnSAUR50</span> gene. * and ** indicate statistical significance at <span class="html-italic">p</span> &lt; 0.05 and <span class="html-italic">p</span> &lt; 0.01 level.</p> Full article ">

17 pages, 3993 KiB  

Open AccessArticle

Genome-Wide Identification of the Shaker Potassium Channel Family in Chinese Cabbage and Functional Studies of BrKAT1 in Yeast

by Jin-Yan Zhou, Ze-Chen Gu and Dong-Li Hao

Agronomy 2024, 14(9), 1954; https://doi.org/10.3390/agronomy14091954 - 29 Aug 2024

Viewed by 239

Abstract

Shaker potassium channels play a crucial role in potassium (K+) nutrition and stress resistance in plants. However, systematic research on Shaker K+ channels in Chinese cabbage [Brassica rapa var. chinensis (L.) Kitamura] remains scarce. This study identified 13 Shaker K+ channel members [...] Read more.

Shaker potassium channels play a crucial role in potassium (K+) nutrition and stress resistance in plants. However, systematic research on Shaker K+ channels in Chinese cabbage [Brassica rapa var. chinensis (L.) Kitamura] remains scarce. This study identified 13 Shaker K+ channel members within the cabbage genome, which are unevenly distributed across eight chromosomes. Notably, the number of Shaker K+ channel members in Chinese cabbage exceeds that found in the model plants Arabidopsis (9) and rice (10). This discrepancy is attributed to a higher number of homologous proteins in Groups II and V of Chinese cabbage, with gene segmental duplication in these two subgroups being a significant factor contributing to the expansion of the Shaker K+ channel gene family. Interspecies collinearity analysis revealed that the whole genome and the Shaker K+ channel family of Chinese cabbage show greater similarity to those of Arabidopsis than to those of rice, indicating that Shaker K+ channels from the Brassicaceae family have a closer relationship than that from the Poaceae family. Given that gene expansion occurs in Group II, we investigated whether a functional difference exists between BrKAT1.1 and BrKAT1.2 using yeast assays and promoter analysis. The expression of two BrKAT1 genes in the potassium uptake-deficient yeast mutant R5421 can restore growth under low potassium conditions, indicating their role in potassium absorption. Truncation of the N-terminal 63 amino acids of BrKAT1.2 resulted in the loss of potassium absorption capability, suggesting that the N-terminus is essential for maintaining the potassium absorption function of BrKAT1.2. Furthermore, the expression of the two BrKAT1 genes in the salt-sensitive yeast G19 enhances yeast tolerance to salt stress. These results demonstrate that BrKAT1.1 and BrKAT1.2 exhibit similar abilities in potassium uptake and salt tolerance. The difference between BrKAT1.1 and BrKAT1.2 lay in their promoter regulatory elements, suggesting that differences in transcriptional regulation contributed to the functional differentiation of BrKAT1.1 and BrKAT1.2. These findings provide a foundation for understanding the evolution and functional mechanisms of the Shaker K+ channel family in Chinese cabbage and for improving potassium nutrition and salt tolerance in this species through the manipulation of BrKAT1. Full article

(This article belongs to the Topic Plant Responses to Environmental Stress)

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Figure 1

Figure 1
<p>Phylogenetic tree of the relationships among the Shaker K+ channel proteins of Chinese cabbage, Arabidopsis, and rice. Shaker K+ channels from Chinese cabbage are indicated by red dots. Shaker K+ channels from Arabidopsis are indicated by blue squares. Shaker K+ channels from rice are represented by green stars.</p> Full article ">Figure 2
<p>Phylogenetic tree and protein domains among the Chinese cabbage Shaker K+ channel family. The left panel shows a phylogenetic tree, and the right panel shows the protein domain results. Both are constructed on the basis of the Shaker K+ channel amino acid sequence.</p> Full article ">Figure 3
<p>Position of Shaker K+ channel members on the chromosomes. The name and gene ID of the Shaker K+ channel are indicated.</p> Full article ">Figure 4
<p>Intraspecies collinearity analysis. The gray line represents the collinear gene pairs, whereas the red line represents the collinear Shaker K+ channel gene pairs in the genome of Chinese cabbage. The gene name (blue) and gene ID number are marked at the corresponding positions.</p> Full article ">Figure 5
<p>Collinearity analyses of Shaker K+ channel genes among Chinese cabbage, rice, and Arabidopsis. The gray lines among the three plants represent collinear blocks in wide regions of the genome, whereas the red lines represent the orthologous relationships of the Shaker K+ channel genes.</p> Full article ">Figure 6
<p>Promoter analysis of Shaker K+ channels. The various regulatory elements were represented using distinct colors.</p> Full article ">Figure 7
<p>Yeast functional complementation. The growth performance of a yeast mutant (deficient in K+ uptake) harboring <span class="html-italic">BrKAT1.1</span>, <span class="html-italic">BrKAT1.2</span>, or one <span class="html-italic">BrKAT1</span> gene with a truncated N-terminus is presented. The K+ concentrations used were 0.02, 0.2, 2, and 100 mM. The dilution factor of the cell suspension is indicated.</p> Full article ">Figure 8
<p>The expression of <span class="html-italic">BrKAT1</span> increases salt tolerance in yeast. The growth performance of a <span class="html-italic">BrKAT1.1</span>-, <span class="html-italic">BrKAT1.2-</span>, and <span class="html-italic">SsKAT1.1</span>-expressing yeast mutant (G19, a salt-sensitive yeast strain) on AP media supplemented with different concentrations of Na+ is presented. The dilution factor of the cell suspension is indicated.</p> Full article ">

14 pages, 14360 KiB  

Open AccessArticle

LzSCL9, a Novel GRAS Transcription Factor in Lanzhou Lily (Lilium davidii var. unicolor), Participates in Regulation of Trichokonins-Primed Heat Stress Tolerance

by Xing Cao, Liping Ding, Jiahui Liang, Yanrong Zhou, Xiulan Chen, Haiyan Li, Tao Liu, Wenxiu Yue, Juanjuan Sui, Liangbao Jiang, Yulian Qian, Dongdong Yang, Bo Wang, Hailing Zhang, Ze Wu and Xiaoyan Song

Plants 2024, 13(16), 2330; https://doi.org/10.3390/plants13162330 - 21 Aug 2024

Viewed by 420

Abstract

In our previous research, we found that trichokonins’ (TKs) employment improved the thermotolerance of the Lanzhou lily, a renowned edible crop species endemic to China that is relatively susceptible to high temperatures (HTs). Here, a novel Lanzhou lily GRAS gene, LzSCL9, was [...] Read more.

In our previous research, we found that trichokonins’ (TKs) employment improved the thermotolerance of the Lanzhou lily, a renowned edible crop species endemic to China that is relatively susceptible to high temperatures (HTs). Here, a novel Lanzhou lily GRAS gene, LzSCL9, was identified to respond to heat stress (HS) and HS+TKs treatment based on transcriptome and RT-qPCR analysis. TKs could improve the upregulation of LzSCL9 during long-term HS. The expression profile of LzSCL9 in response to HS with or without TKs treatment showed a significant positive correlation with LzHsfA2a-1, which was previously identified as a key regulator in TKs’ conferred resilience to HT. More importantly, overexpression of LzSCL9 in the lily enhanced its tolerance to HTs and silencing LzSCL9 in the lily reduced heat resistance. Taken together, this study identified the positive role of LzSCL9 in TK-induced thermotolerance, thereby preliminarily establishing a molecular mechanism on TKs regulating the thermostability of the Lanzhou lily and providing a new candidate regulator for plant heat-resistant breeding. Full article

(This article belongs to the Topic Plant Responses to Environmental Stress)

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Figure 1

Figure 1
<p>The analysis of <span class="html-italic">GRASs</span> expression in reaction to HS or HS + TKs treatment based on transcriptome data. (<b>A</b>) <span class="html-italic">GRASs</span> expression in response to HS or HS + TKs treatment. Eight genes (highlighted with pink rectangular box) were upregulated under 12 h of HS in the presence of TKs. (<b>B</b>) Heat map of correlation between <span class="html-italic">LzHsfA2a-1</span> and 75 <span class="html-italic">GRAS</span> gene expression levels. Pearson’s correlation (r) analyses (<span class="html-italic">p</span> &lt; 0.05) were used. CL5991.Contig3_All showed the highest correlation with <span class="html-italic">LzHsfA2a-1</span> in gene expression. (<b>C</b>) Expression analysis of 6 differentially expressed <span class="html-italic">GRAS</span> genes at different time points under HS treatment using RNAseq. CL5991.Contig3_All (highlighted with red asterisk) was selected for further study.</p> Full article ">Figure 2
<p>Phylogenetic relationship and sequences alignment of SCL9 in various species. (<b>A</b>) Multiple alignments of LzSCL9 with other plant SCL9. LHR I, VHIID, LHR II, PFYRE, and SAW motifs are indicated by horizontal lines. (<b>B</b>) Phylogenetic tree analysis of LzSCL9, LlSCL9, and GRASs from Arabidopsis was fulfilled using the neighbor-joining method with 1000 bootstrap replicates. LzSCL9 was highlighted with a rectangular box. Ac: <span class="html-italic">Ananas comosus</span>, Os: <span class="html-italic">Oryza sativa</span>, Eg: <span class="html-italic">Elaeis guineensis</span>, Pd: <span class="html-italic">Phoenix dactylifera</span>, Vv: <span class="html-italic">Vitis vinifera</span>, At: <span class="html-italic">Arabidopsis thaliana</span>, Ll: <span class="html-italic">Lilium longiflorum</span>.</p> Full article ">Figure 2 Cont.
<p>Phylogenetic relationship and sequences alignment of SCL9 in various species. (<b>A</b>) Multiple alignments of LzSCL9 with other plant SCL9. LHR I, VHIID, LHR II, PFYRE, and SAW motifs are indicated by horizontal lines. (<b>B</b>) Phylogenetic tree analysis of LzSCL9, LlSCL9, and GRASs from Arabidopsis was fulfilled using the neighbor-joining method with 1000 bootstrap replicates. LzSCL9 was highlighted with a rectangular box. Ac: <span class="html-italic">Ananas comosus</span>, Os: <span class="html-italic">Oryza sativa</span>, Eg: <span class="html-italic">Elaeis guineensis</span>, Pd: <span class="html-italic">Phoenix dactylifera</span>, Vv: <span class="html-italic">Vitis vinifera</span>, At: <span class="html-italic">Arabidopsis thaliana</span>, Ll: <span class="html-italic">Lilium longiflorum</span>.</p> Full article ">Figure 3
<p>qRT-PCR analysis of <span class="html-italic">LzSCL9</span> expression and validation of the DEG. (<b>A</b>) Relative expression of <span class="html-italic">LzSCL9</span> in leaves under 2 mg/L TKs treatment or distilled water at 40 °C HS for different durations (0, 3, 6, and 12 h). (<b>B</b>) FPKM of <span class="html-italic">LzSCL9</span> (CL5991.Contig3_All) from RNA-seq. Different lowercase letters represent significant differences at a 0.05 level with Duncan’s multiple range test.</p> Full article ">Figure 4
<p>Thermotolerance assessment in lily petal discs overexpressing <span class="html-italic">LzSCL9</span>. (<b>A</b>) Detection of <span class="html-italic">LzSCL9</span> expression in <span class="html-italic">LzSCL9</span> overexpression petal discs. (<b>B</b>) Petal disc color phenotypes observed at 22 °C (RT) and after 12 h exposure to 40 °C (HS). (<b>C</b>) Relative ion leakage of petal discs at RT (22 °C) and following 12 h HS (40 °C). Data were analyzed using 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).</p> Full article ">Figure 5
<p>Evaluation of thermotolerance in <span class="html-italic">LzSCL9</span>-silenced lily petal discs. (<b>A</b>) <span class="html-italic">LzSCL9</span> expression level in TRV-VIGS petals. (<b>B</b>) The color phenotypes of petal discs at 22 °C (RT) and subjected to 40 °C for 12 h (HS). (<b>C</b>) Relative ion leakage of petal discs at RT and following HS. Data were processed using Student’s <span class="html-italic">t</span>-test (* <span class="html-italic">p</span> &lt; 0.05).</p> Full article ">

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16 pages, 3672 KiB  

Open AccessArticle

Genome-Wide Identification, Bioinformatic Characterization, and Expression Profiling of Starch Synthase (SS) Genes in Foxtail Millet under Drought Condition

by Joseph N. Amoah, Monica Ode Adu-Gyamfi and Albert Owusu Kwarteng

Stresses 2024, 4(3), 518-533; https://doi.org/10.3390/stresses4030033 - 16 Aug 2024

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Abstract

Millet, a vital and nutritionally dense cereal extensively cultivated in Sub-Saharan Africa, plays a key role in ensuring food security. This study investigates the starch synthase (SS) gene family, which is crucial for starch biosynthesis and influences various plant functions and [...] Read more.

Millet, a vital and nutritionally dense cereal extensively cultivated in Sub-Saharan Africa, plays a key role in ensuring food security. This study investigates the starch synthase (SS) gene family, which is crucial for starch biosynthesis and influences various plant functions and stress responses. While the specific roles of SS genes in millet under drought conditions are not fully elucidated, this research provides a thorough analysis of the SS gene family in millet. A total of twelve millet SS genes (SiSSs) were identified and classified into four subfamilies (I–IV) through gene structure and phylogenetic analysis. The SiSS genes were unevenly distributed across millet chromosomes, with cis-acting elements associated with plant growth and stress defense being identified. Quantitative PCR (qPCR) revealed dynamic and varied expression patterns of SiSSs in different tissues under drought stress. Millet plants subjected to drought conditions showed higher tissue starch content and increased starch synthase activity compared to controls. Importantly, the expression levels of the twelve SiSSs were positively correlated with both starch content and synthase activity, suggesting their significant role in drought tolerance. This study enhances our understanding of the millet SS gene family and highlights the potential of these genes in breeding programs aimed at developing drought-resistant millet varieties. Further research is recommended to validate these findings and delve deeper into the mechanisms underlying drought tolerance. Full article

(This article belongs to the Topic Plant Responses to Environmental Stress)

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Figure 1

Figure 1
<p>The phylogenetic relationships among SS proteins in five plant species are depicted through a phylogenetic tree. SS proteins from millet (SiSS1-SiSS12), Arabidopsis thaliana (AtSS1-AtSS7), wheat (TaSS1-TaSS7), rice (OsSS1-OsSS14), and cassava (MeSS1-MeSS4) are highlighted in red, blue, yellow, pink, and cyan-blue colors, respectively. The neighbor-joining phylogenetic tree of SS protein sequences was constructed with 1000 bootstrap replicates using MEGA v10.0 (Pennsylvania State University, Philadelphia, PA, USA).</p> Full article ">Figure 2
<p>(<b>A</b>) Gene structure of <span class="html-italic">SiSS</span> genes generated using the GSDS 2.0 (Gene Structure Display Server) website (<a href="http://gsds.cbi.pku.edu.cn" target="_blank">http://gsds.cbi.pku.edu.cn</a> (accessed on 20 March 2024)). (<b>B</b>) Exon numbers of p-tative <span class="html-italic">SiSSs</span>. (<b>C</b>) Conserved motifs of the <span class="html-italic">SiSSs</span> identified by MEME (Multiple EM for Motif Elicitation). Each motif is represented by a colored box numbered at the bottom, and the length of the motifs in each protein is proportional. The phylogenetic tree was constructed using the maximum likelihood method with 1000 bootstrap replicates by MEGA v10.0 (Pennsylvania State University, Philadelphia, PA, USA).</p> Full article ">Figure 3
<p>(<b>A</b>) Distribution of <span class="html-italic">SiSSs</span> on the millet chromosome, and (<b>B</b>) gene duplication analysis of <span class="html-italic">SiSSs</span>. The putative whole genome duplicated (WGD) genes are connected by various lines. Numbers 1-9 indicate the positions of SSs on chromosomes 1 through 9 in millet.</p> Full article ">Figure 4
<p>Synteny analysis of <span class="html-italic">SiSSs</span> among millet, Arabidopsis, rice, tomato, and barley was conducted using the one-step MCScanX on TBtools for gene duplication analysis. Grey lines in the background represent collinear blocks within the genomes of different plant species, and blue lines indicate syntenic SS gene pairs. Numbers 1-9 indicate the positions of <span class="html-italic">SSs</span> on chromosomes 1 through 9 across various plant species, while the triangles mark the exact locations of the <span class="html-italic">SSs</span> genes on each chromosome.</p> Full article ">Figure 5
<p>Cis-acting regulatory element (CARE) analysis identified in the putative starch synthesis genes. The CAREs were analyzed from the upstream (2000 bp) promoter sequence of each gene. Identified CAREs were classified based on their function into (<b>A</b>) growth and development, (<b>B</b>) phytohormones, (<b>C</b>) stress and defense, and (<b>D</b>) light-responsive elements.</p> Full article ">Figure 6
<p>Three-dimensional (3D) modeling of starch synthase (<span class="html-italic">SS</span>) Models were predicted and displayed at a confidence level &gt; 90%. Green and blue helix structures denote alpha helix (%) and beta strand (%), respectively.</p> Full article ">Figure 7
<p>Transmembrane topology prediction of <span class="html-italic">SS</span> proteins in millet. Models were predicted and displayed at a confidence level &gt; 90%.</p> Full article ">Figure 8
<p>Quantitative polymerase chain reaction (qPCR) expression analysis of putative <span class="html-italic">SiSSs</span> under different drought conditions. (<b>A</b>–<b>L</b>) represent the relative tissue expression (leaf and root) of putative <span class="html-italic">SiSSs</span> (<span class="html-italic">SiSS1</span>-<span class="html-italic">SiSS12</span>) after a 15-day drought stress treatment. The data represent the mean and standard errors of biological triplicates. Significance (<span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">p</span> &lt; 0.001) was determined using <span class="html-italic">t</span>-test.</p> Full article ">Figure 9
<p>Heat map depicting the expression profiles of <span class="html-italic">SiSS</span> genes after a 15-day drought treatment in (<b>A</b>) leaf and (<b>B</b>) root tissues of the ‘PI 662292’ millet genotype. The heat map was computed using the mean expression values of each putative gene. I–III denotes the three different groups of classification for <span class="html-italic">SiSSs</span>.</p> Full article ">

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21 pages, 1921 KiB  

Open AccessArticle

Enhancing Solanum lycopersicum Resilience: Bacterial Cellulose Alleviates Low Irrigation Stress and Boosts Nutrient Uptake

by Noelia De la Cruz Gómez, César Poza-Carrión, Lucía Del Castillo-González, Ángel Isidro Martínez Sánchez, Ana Moliner, Inmaculada Aranaz and Marta Berrocal-Lobo

Plants 2024, 13(15), 2158; https://doi.org/10.3390/plants13152158 - 4 Aug 2024

Viewed by 560

Abstract

The use of natural-origin biomaterials in bioengineering has led to innovative approaches in agroforestry. Bacterial cellulose (BC), sharing the same chemical formula as plant-origin cellulose (PC), exhibits significantly different biochemical properties, including a high degree of crystallinity and superior water retention capacity. Previous [...] Read more.

The use of natural-origin biomaterials in bioengineering has led to innovative approaches in agroforestry. Bacterial cellulose (BC), sharing the same chemical formula as plant-origin cellulose (PC), exhibits significantly different biochemical properties, including a high degree of crystallinity and superior water retention capacity. Previous research showed that natural-origin glucose-based chitin enhanced plant growth in both herbaceous and non-herbaceous plants. In this study, we produced BC in the laboratory and investigated its effects on the substrate and on Solanum lycopersicum seedlings. Soil amended with BC increased root growth compared with untreated seedlings. Additionally, under limited irrigation conditions, BC increased global developmental parameters including fresh and dry weight, as well as total carbon and nitrogen content. Under non-irrigation conditions, BC contributed substantially to plant survival. RNA sequencing (Illumina^®) on BC-treated seedlings revealed that BC, despite its bacterial origin, did not stress the plants, confirming its innocuous nature, and it lightly induced genes related to root development and cell division as well as inhibition of stress responses and defense. The presence of BC in the organic substrate increased soil availability of phosphorus (P), iron (Fe), and potassium (K), correlating with enhanced nutrient uptake in plants. Our results demonstrate the potential of BC for improving soil nutrient availability and plant tolerance to low irrigation, making it valuable for agricultural and forestry purposes in the context of global warming. Full article

(This article belongs to the Topic Plant Responses to Environmental Stress)

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Figure 1

Figure 1
<p>ATR-FTIR spectrum of bacterial cellulose (BC). (<b>A</b>) Whole spectrum. (<b>B</b>) Detail of the spectra between 650 and 1000 cm⁻¹ showing two bacterial cellulose bands around 750 and around 710 cm⁻¹, corresponding to the presence of the crystalline phases Iα and Iβ of BC, respectively. The presence of stars in FTIR spectra denotes the beta linkage of cellulose monomers. (<b>C</b>) XRD diffractogram recorded for the isolated bacterial cellulose, showing the crystalline structure of cellulose I with three main peaks hightlighted with stars located at 14.7, 17.3, and 23.2 corresponding to crystallographic planes 100, 010, and 110, respectively.</p> Full article ">Figure 2
<p>Physiological effects of bacterial cellulose (BC) on <span class="html-italic">Solanum lycopersicum</span> (Sl). (<b>A</b>) Seed germination rate on substrate containing 0.01% (<span class="html-italic">w</span>:<span class="html-italic">w</span>), BC (triangle), or watered control (circle). Data were collected up to 14 days. (<b>B</b>) Seed germination rate under hydroponic conditions in the presence of 0.01% (<span class="html-italic">w</span>:<span class="html-italic">v</span>), BC (triangle), or watered control (circle). Data were collected up to 14 days. (<b>C</b>) Total fresh and dry weight (g) of seedlings, growth under optimal irrigation conditions (I), or under regulated non-irrigation conditions (NI), in the absence (white) or presence of BC (grey). (<b>D</b>) Representative photos of 14-day-old seedlings growing at the same plate under I or NI conditions, in the absence (−) or presence (+) of BC. Bars: 1 cm. (<b>E</b>) Total shoot (left figure) and root (right figure) lengths (cm), measured in the absence (white) or presence of BC (grey) growth under I or NI conditions. (<b>F</b>) Detail of shoots and roots photos corresponding to (<b>E</b>), obtained for measuring seedling growth using ImageJ^® tool (1.53 version) (see <a href="#sec4-plants-13-02158" class="html-sec">Section 4</a>) under I or NI conditions. Assays were performed at least three times with similar results using ten seedlings per pot and twelve pots per plate and tree plates per treatment (n = 120 per plate). Data were analyzed with the Stat-graphics Centurion 19 program, using a Variance check (<span class="html-italic">p</span> &gt; 0.05) and a non-parametric Kruskal–Wallis test. * Significant statistical differences.</p> Full article ">Figure 3
<p>Effect of bacterial cellulose (BC) on Fe, K, and P content in plants and substrate. (<b>A</b>) Effect of BC (0.01%, <span class="html-italic">w</span>:<span class="html-italic">w</span>) on total P content on substrate (S, mg/kg), plants (P, g/100 g), or planted substrate (PS, mg/kg) in the absence (C) or presence of BC (0.01%, <span class="html-italic">w</span>:<span class="html-italic">w</span>). (<b>B</b>) Effect of BC on total Fe content on S (mg/kg), P (mg/Kg × 10²), or PS (mg/kg) in the absence (C) or presence of BC. (<b>C</b>) Effect of BC on total K content on S (mg/kg), P (g/100 g), or PS (mg/kg) in the absence (C) or presence of BC. Substrate and seedling samples were collected after fourteen days, making the treatment at time zero. At least three assays were performed analyzing ten seedlings per pot, three pots per treatment, with three plates (n = 120). Data were analyzed with the Stat-graphics Centurion 19 program, using a Variance check (<span class="html-italic">p</span> &gt; 0.05) and a non-parametric Kruskal–Wallis’s test. White is control (C), while the grey bars correspond to bacterial cellulose (BC) treatment. * Significant statistical differences.</p> Full article ">Figure 4
<p>RNA sequencing data analysis of <span class="html-italic">Solanum lycopersicum</span> seedlings responding to bacterial cellulose (BC). (<b>A</b>) Volcano scatter plots obtained with BioJupies^® software, displaying the log2-fold changes calculated by performing a differential gene expression analysis (see <a href="#sec4-plants-13-02158" class="html-sec">Section 4</a>). Red points indicate significantly up-regulated genes, while blue points indicate down-regulated genes. The upper plot shows results of seedlings responding to BC (0.01%, <span class="html-italic">w</span>:<span class="html-italic">v</span>) after one hour, related to watered non-treated seedlings. The lower plot shows a positive control for molecular plant response to chitin (Qq, 0.01%, <span class="html-italic">w</span>:<span class="html-italic">v</span>) (see <a href="#sec4-plants-13-02158" class="html-sec">Section 4</a>). (<b>B</b>) Heatmap obtained with BioJupies^® software (see <a href="#sec4-plants-13-02158" class="html-sec">Section 4</a>), displaying significant gene expression on selected genes in the RNA-seq dataset. Induced (red) or repressed genes (blue) (FDR&lt; or &gt;0.05) are shown after 1 h of treatment with BC. Every row of the heatmap represents a gene, and every column represents triplicates of controls (C1 to C3) and BC treatments (BC1 to BC3), correspondingly. Every cell displays normalized gene expression values. <span class="html-italic">Solanum lycopersicum</span> gene IDs are shown in the left column obtained by using the ITAG 3.2 genome annotation version. Black dots mean significant diferences on BC treated plants related to untreated plants lower than 1.5.</p> Full article ">

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Open AccessArticle

Photosynthetic Limitations and Growth Traits of Four Arabica Coffee (Coffea arabica L.) Genotypes under Water Deficit

by Wilmer Tezara, Daniel W. Loyaga, Víctor H. Reynel Chila and Ana Herrera

Agronomy 2024, 14(8), 1713; https://doi.org/10.3390/agronomy14081713 - 4 Aug 2024

Viewed by 541

Abstract

Climate change increases the risk of coffee yield due to the genotype-dependent effects of water deficit on coffee physiology. The goal of this research was to evaluate how water deficit altered the physiological and growth characteristics of arabica coffee (Coffea arabica L.). [...] Read more.

Climate change increases the risk of coffee yield due to the genotype-dependent effects of water deficit on coffee physiology. The goal of this research was to evaluate how water deficit altered the physiological and growth characteristics of arabica coffee (Coffea arabica L.). Water status, photosynthetic response to CO₂ intercellular concentration (A/C_i curves) and growth parameters were evaluated in seedlings of four genotypes (Catimor ECU 02, Cavimor ECU, red Caturra and Sarchimor 4260). Most of the physiological traits evaluated differed significantly among genotypes. Between control and water deficit plants, significant variations occurred in the A/C_i parameters, showing a wide range of values for net photosynthetic rate, stomatal conductance, and water use efficiency, with decreases ranging from 4 to 74%. Maximum electron transport rate through photosystem II, highest rate of RuBisCO carboxylation, and triose phosphate utilization rate were all strongly decreased by water deficit 61% (red Caturra and Sarchimor 4260), followed by Cavimor ECU (35%) and Catimor ECU 02 (24%). Differences in response to water deficit among genotypes suggest possible genotypic differences in tolerance. The results indicated that Catimor ECU 02 and Cavimor ECU were less sensitive to water deficit, while red Caturra and Sarchimor 4260 were the most susceptible. Full article

(This article belongs to the Topic Plant Responses to Environmental Stress)

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Figure 1

Figure 1
<p>Gas exchange parameters of four arabica coffee genotypes. In the left column the graphs (<b>A</b>–<b>D</b>) show the mean values for each genotype per month, February (black), March (red), April (green) and May (yellow) 2017. In the right column the graphs (<b>E</b>–<b>H</b>) show the means of all sampling time points per genotype. Bars represent the mean of five plants ± SE. Distinct letters indicate significant variations in genotypes and/or sampling time (<span class="html-italic">p</span> &lt; 0.05). (<b>A</b>,<b>E</b>) Net photosynthetic rate; (<b>B</b>,<b>F</b>) transpiration rate; (<b>C</b>,<b>G</b>) stomatal conductance; (<b>D</b>,<b>H</b>) water use efficiency.</p> Full article ">Figure 2
<p>Effects of water deficit duration on four coffee genotype plants’ leaves: (<b>A</b>–<b>D</b>), soil water content (SWC); (<b>B</b>–<b>H</b>), leaf water content (LWC); (<b>I</b>–<b>L</b>), relative water content (RWC); and (<b>M</b>–<b>P</b>), specific leaf area (SLA), in both WD (open circles) and control (closed circles) plants. Genotype name is shown above the uppermost panels. The data represent means (6 ≤ <span class="html-italic">n</span> ≤ 10) ± SE. For each parameter at <span class="html-italic">p</span> &lt; 0.05, distinct letters denote significant variations between WD and control seedlings. Measurements taken a day after re-irrigation are indicated by red arrows.</p> Full article ">Figure 3
<p>Effects of water deficit duration on four coffee genotype plants’ leaves: control (closed circles) and WD (open circles) plants. Net photosynthetic rate (<b>A</b>–<b>D</b>), transpiration rate (<b>E</b>–<b>H</b>), stomatal conductance (<b>I</b>–<b>L</b>), intercellular CO₂ concentration (<b>M</b>–<b>P</b>), and water use efficiency (<b>Q</b>–<b>T</b>). Genotype name shown in the uppermost panels. The data are means (<span class="html-italic">n =</span> 5) ± SE. For each parameter at <span class="html-italic">p</span> &lt; 0.05, distinct letters denote significant variations between WD and control seedlings. Measurements taken a day after re-irrigation are indicated by red arrows.</p> Full article ">Figure 4
<p>The net photosynthetic rate (A) and stomata conductance (g_s) of four genotypes of arabica coffee in the leaves of plants experiencing a water deficit (open circles) and control plants (closed circles) are correlated. The genotypes examined were Sarchimor 4260 (●), Red Caturra (■), Catimor ECU 02 (▲) and Cavimor ECU (♦). The data are means (<span class="html-italic">n</span> = 5) ± SE. The regression was significant at <span class="html-italic">p</span> &lt; 0.0001.</p> Full article ">Figure 5
<p>Effects of water deficit duration in parameters calculated from A/C_i curves on four coffee genotype plants’ leaves: (<b>A</b>), C_i-saturated photosynthetic rate; (<b>B</b>), carboxylation efficiency; (<b>C</b>), relative stomatal limitation; (<b>D</b>), relative mesophyll limitation; (<b>E</b>), maximum rate of electron transport through PSII; (<b>F</b>), maximum rate of RuBisCO carboxylation and (<b>G</b>), triose phosphates utilization rate. The genotypes examined were Sarchimor 4260 (●), Red Caturra (■), Catimor ECU 02 (▲) and Cavimor ECU (♦). When a value exceeds the symbol size, standard errors are presented. The data are means (<span class="html-italic">n</span> = 4) ± SE.</p> Full article ">Figure 6
<p>Changes over time of growth variables in plants of four arabica coffee genotypes in: (<b>A</b>) height; (<b>B</b>) flowering index; (<b>C</b>) stem diameter; (<b>D</b>) crown diameter; (<b>E</b>) number of branches; (<b>F</b>) branch length; (<b>G</b>) total number of nodes per branch, and (<b>H</b>) distance between internodes. Control plants measured in February (black bars), March (red bars), April (green bars), May (yellow bars), June (blue bars) and July (pink bars); plants under water deficit for 15 d (turquoise) and 29 d (grey). Cultivar names are indicated on the abscissa. An asterisk indicates significant differences between control plants in July and those subjected to water deficit (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 7
<p>Principal component analysis for the 24 physiological and morphological characteristics evaluated in the 4 genotypes of arabica coffee. The ellipses represent the coffee genotypes grouped in the different groups, in blue control plants (C) and red in plants water deficit (WD). Longer broken lines indicate more weight in each component, and also indicate the variables that are significant in each of the main components.</p> Full article ">

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Open AccessArticle

The Identification of Kabatiella zeae as a Causal Agent of Northern Anthracnose of Sorghum in China and Estimation of Host Resistance

by Wenbo Yu, Yu Wang, Lan Hu, Jing Xu, Jichen Yan, Peng Cao, Chunjuan Liu, Xiaolong Shi, Chang Liu, Yu Jiang and Yufei Zhou

Plants 2024, 13(13), 1857; https://doi.org/10.3390/plants13131857 - 5 Jul 2024

Viewed by 537

Abstract

Sorghum northern anthracnose is a leaf disease affecting sorghum, which results in plant death and substantial yield loss. This study aimed to effectively understand the disease, clarify its biological characteristics, and evaluate the resistance of germplasm resources. A field sample was collected to [...] Read more.

Sorghum northern anthracnose is a leaf disease affecting sorghum, which results in plant death and substantial yield loss. This study aimed to effectively understand the disease, clarify its biological characteristics, and evaluate the resistance of germplasm resources. A field sample was collected to isolate and purify the pathogen. The pathogen, identified as Kabatiella zeae Narita et Hiratsuka using both morphological and molecular techniques, was further confirmed as the causative agent of northern anthracnose of sorghum following Robert Koch’s principles. The results revealed the optimal culture temperature to be 25 °C, preferred dark culture conditions, and the best growth on potato glucose agar medium with sucrose and L-leucine as the optimal carbon and nitrogen sources, respectively. A total of 138 sorghum germplasm resources were inoculated and evaluated using the isolated pathogen, with 20 lines (14.49%) exhibiting high resistance, 18 lines (13.04%) showing disease resistance, 27 lines (19.57%) demonstrating medium resistance, 37 lines (26.81%) being susceptible, and 36 lines (26.09%) classified as highly susceptible. The indoor fungicide screening was conducted through pathogen medium application, and enilconazole, pyraclostrobin, methylthiophanate, and flusilazole were screened for the best fungicide inhibition with a 100% inhibition rate compared with the control. This study provides reference for field pharmaceutical control in sorghum production. Full article

(This article belongs to the Topic Plant Responses to Environmental Stress)

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Figure 1

Figure 1
<p>Colony and conidia of <span class="html-italic">Kabatiella zeae</span>. (<b>A</b>) The adverse vision of mycelial growth of <span class="html-italic">Kabatiella zeae</span> on PDA medium after 15 days at temperature of 25 °C; (<b>B</b>) the reverse vision of mycelial growth of <span class="html-italic">Kabatiella zeae</span> on PDA medium after 15 days at temperature 25 °C; (<b>C</b>) conidia morphology under microscope; (<b>D</b>) morphology of spores attached to leaves under microscope. The morphology of pathogens on a leaf, as observed under a scanning electron microscope, was highlighted in red.</p> Full article ">Figure 2
<p>Effects of temperature conditions on the growth of <span class="html-italic">Kabatiella zeae</span>. (<b>A</b>) Colony diameter of the pathogen grew at different temperatures on PDA medium for 7 days. Different lowercase letters between each bar indicate significant differences among the different temperature (<span class="html-italic">p</span> &lt; 0.05); (<b>B</b>) morphology of pathogen colonies at different temperatures.</p> Full article ">Figure 3
<p>Effects of light conditions on the growth of <span class="html-italic">Kabatiella zeae</span>. (<b>A</b>) Colony diameter of the pathogen grew under different light conditions on PDA medium at 25 °C for 7 days. Different lowercase letters between each bar indicate significant differences among the different light conditions (<span class="html-italic">p</span> &lt; 0.05); (<b>B</b>) morphology of pathogen colonies under different light conditions.</p> Full article ">Figure 4
<p>Effect of culture medium on the growth of <span class="html-italic">Kabatiella zeae</span>. (<b>A</b>) Colony diameters of the pathogen grew on different media at 25 °C for 7 days. Different lowercase letters between each bar indicate significant differences among the different culture media (<span class="html-italic">p</span> &lt; 0.05); (<b>B</b>) colony morphology of the pathogen in different culture media.</p> Full article ">Figure 5
<p>Effects of carbon and nitrogen sources on the growth of <span class="html-italic">Kabatiella zeae</span>. (<b>A</b>) Diameter of pathogen colony under different carbon source conditions. (<b>B</b>) Colony diameter of the pathogen grew under different nitrogen source conditions. All treatments were grown at 25 °C for 7 days. Different lowercase letters between each bar indicate significant differences (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 6
<p>Inhibitory effect of nine different fungicides at six concentrations (from 3.12 to 100 mg/L) on <span class="html-italic">Kabatiella zeae</span>. The CK treatment was no fungicide being added to <span class="html-italic">Kabatiella zeae</span>. The growth condition was 25 °C for 7 days on PDA medium.</p> Full article ">Figure 7
<p>The graphs illustrate the inhibitory effects of various pesticides on northern anthracnose of sorghum. (<b>A</b>) Antifungal rate of methylthiophanate. (<b>B</b>) Antifungal rate of dithianon. (<b>C</b>) Antifungal rate of pyraclostrobin. (<b>D</b>) Antifungal rate of fluazinam. (<b>E</b>) Antifungal rate of azoxystrobin. (<b>F</b>) Antifungal rate of enilconazole. (<b>G</b>) Antifungal rate of tebuconazole. (<b>H</b>) Antifungal rate of flusilazole. (<b>I</b>) Antifungal rate of bromothalonil. The growth condition was 25 °C for 7 days on PDA medium. Different lowercase letters between each bar indicate significant differences (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 8
<p>Symptoms of different disease grades on diseased leaves. The number in this figure presented the rating scale of the diseased leaves.</p> Full article ">

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Open AccessArticle

Transcriptome Profiling Reveals the Response of Seed Germination of Peganum harmala to Drought Stress

by Zhen Zhang, Hongyan Su, Qingen Li and Mengfei Li

Plants 2024, 13(12), 1649; https://doi.org/10.3390/plants13121649 - 14 Jun 2024

Viewed by 676

Abstract

Peganum harmala L. is a perennial herbaceous plant that plays critical roles in protecting the ecological environment in arid, semi-arid, and desert areas. Although the seed germination characteristics of P. harmala in response to environmental factors (i.e., drought, temperature, and salt) have been [...] Read more.

Peganum harmala L. is a perennial herbaceous plant that plays critical roles in protecting the ecological environment in arid, semi-arid, and desert areas. Although the seed germination characteristics of P. harmala in response to environmental factors (i.e., drought, temperature, and salt) have been investigated, the response mechanism of seed germination to drought conditions has not yet been revealed. In this study, the changes in the physiological characteristics and transcriptional profiles in seed germination were examined under different polyethylene glycol (PEG) concentrations (0–25%). The results show that the seed germination rate was significantly inhibited with an increase in the PEG concentration. Totals of 3726 and 10,481 differentially expressed genes (DEGs) were, respectively, generated at 5% and 25% PEG vs. the control (C), with 1642 co-expressed DEGs, such as drought stress (15), stress response (175), and primary metabolism (261). The relative expression levels (RELs) of the key genes regulating seed germination in response to drought stress were in accordance with the physiological changes. These findings will pave the way to increase the seed germination rate of P. harmala in drought conditions. Full article

(This article belongs to the Topic Plant Responses to Environmental Stress)

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Figure 1

Figure 1
<p>Seed germination characteristics of <span class="html-italic">Peganum harmala</span> under PEG treatments. Images (<b>A</b>–<b>D</b>) show the germination rate, hypocotyl length, radicle length, and fresh weight, respectively. Different letters indicate a significant difference at the <span class="html-italic">p</span> &lt; 0.05 level.</p> Full article ">Figure 2
<p>Volcano plot of unigenes and number of DEGs under 5% and 25% PEG vs. C. Images (<b>A</b>,<b>B</b>) show the volcano plots; image (<b>C</b>) shows the number of DEGs.</p> Full article ">Figure 3
<p>Classification of DEGs under PEG treatments. Images (<b>A</b>,<b>B</b>) show the identified and unidentified DEGs; image (<b>C</b>) shows their Venn diagram; and image (<b>D</b>) shows the classification of the co-expressed DEGs.</p> Full article ">Figure 4
<p>The RELs of drought stress genes in <span class="html-italic">P. harmala</span> (mean ± SD, n = 3). Abbreviations: <span class="html-italic">ADH1</span>, alcohol dehydrogenase class P; <span class="html-italic">ADHIII</span>, alcohol dehydrogenase class 3; <span class="html-italic">ANN1</span>, annexin D1; <span class="html-italic">CRY1</span>, cryptochrome 1; <span class="html-italic">CRY2</span>, cryptochrome 2; <span class="html-italic">ERD14</span>, dehydrin ERD14; <span class="html-italic">DRPD</span>, desiccation-related protein PCC27-45; <span class="html-italic">EDL3</span>, EID1-like F-box protein 3; <span class="html-italic">HVA22E</span>, HVA22-like protein e; <span class="html-italic">PLAT1</span>, PLAT domain-containing protein 1; <span class="html-italic">ARP1</span>, probable RNA-binding protein ARP1; <span class="html-italic">ASPG1</span>, protein ASPARTIC PROTEASE IN GUARD CELL 1; <span class="html-italic">ERD7</span>, protein EARLY-RESPONSIVE TO DEHYDRATION 7; <span class="html-italic">REM4.2</span>, remorin 4.2; <span class="html-italic">CDSP32</span>, thioredoxin-like protein CDSP32; vs., versus.</p> Full article ">Figure 5
<p>The RELs of antioxidant enzymes genes in <span class="html-italic">P. harmala</span> under 5% and 25% PEG vs. C (mean ± SD, n = 3). Abbreviations: <span class="html-italic">BAS1</span>, 2-Cys peroxiredoxin BAS1; <span class="html-italic">PER31</span>, peroxidase 31; <span class="html-italic">PRXIIE-1</span>, peroxiredoxin-2E-1; <span class="html-italic">PEX11C</span>, peroxisomal membrane protein 11C; <span class="html-italic">Gpx3</span>, glutathione peroxidase 3; <span class="html-italic">Gpx4</span>, phospholipid hydroperoxide glutathione peroxidase; <span class="html-italic">APX1</span>, L-ascorbate peroxidase; <span class="html-italic">APX3</span>, L-ascorbate peroxidase 3; <span class="html-italic">AFRR</span>, monodehydroascorbate reductase; <span class="html-italic">MDAR4</span>, monodehydroascorbate reductase 4; <span class="html-italic">CATA</span>, catalase; <span class="html-italic">CAT2</span>, catalase isozyme 2; <span class="html-italic">PNC1</span>, cationic peroxidase 1; <span class="html-italic">PNC2</span>, cationic peroxidase 2; vs., versus.</p> Full article ">Figure 6
<p>The RELs of osmolytes genes in <span class="html-italic">P. harmala</span> under 5% and 25% PEG vs. C (mean ± SD, n = 3). Abbreviations: <span class="html-italic">GLC1</span>, glucan endo-1,3-beta-glucosidase; <span class="html-italic">At5g58090</span>, glucan endo-1,3-beta-glucosidase 6; <span class="html-italic">G6pc2</span>, glucose-6-phosphatase 2; <span class="html-italic">GAPA</span>, glyceraldehyde-3-phosphate dehydrogenase A; <span class="html-italic">PGM1</span>, phosphoglucomutase; <span class="html-italic">Gcg</span>, pro-glucagon; <span class="html-italic">INVA</span>, alkaline/neutral invertase A; <span class="html-italic">SUS2</span>, sucrose synthase 2; <span class="html-italic">SPP1</span>, sucrose-phosphatase 1; <span class="html-italic">Pfkl</span>, ATP-dependent 6-phosphofructokinase; <span class="html-italic">FBP</span>, fructose-1,6-bisphosphatase; <span class="html-italic">ALFC</span>, fructose-bisphosphate aldolase; <span class="html-italic">FBA6</span>, fructose-bisphosphate aldolase 6; <span class="html-italic">At4g10260</span>, probable fructokinase-5; <span class="html-italic">BGAL</span>, beta-galactosidase; <span class="html-italic">GOLS1</span>, galactinol synthase 1; <span class="html-italic">OFUT7</span>, O-fucosyltransferase 7; <span class="html-italic">OFUT39</span>, O-fucosyltransferase 39; <span class="html-italic">TPS6</span>, alpha,alpha-trehalose-phosphate synthase (UDP-forming) 6; <span class="html-italic">TPPA</span>, trehalose-phosphate phosphatase A; <span class="html-italic">MSR1</span>, protein MANNAN SYNTHESIS-RELATED 1; <span class="html-italic">MAN1</span>, mannan endo-1,4-beta-mannosidase 1; <span class="html-italic">DSP4</span>, phosphoglucan phosphatase DSP4; <span class="html-italic">PTST</span>, protein PTST homolog 3; <span class="html-italic">SS3</span>, soluble starch synthase 3; <span class="html-italic">APA1</span>, aspartic proteinase A1; <span class="html-italic">FTSH5</span>, ATP-dependent zinc metalloprotease FTSH 5; <span class="html-italic">CYP-3</span>, cysteine proteinase 3; <span class="html-italic">LOC109345795</span>, gamma conglutin 1; <span class="html-italic">Os04g0650000</span>, oryzain alpha chain; <span class="html-italic">RMD5</span>, protein RMD5 homolog; <span class="html-italic">SCPL34</span>, serine carboxypeptidase-like 34; <span class="html-italic">At4g32940</span>, vacuolar-processing enzyme gamma-isozyme; vs., versus.</p> Full article ">Figure 7
<p>The RELs of genes directly associated with seed germination of <span class="html-italic">P</span>. <span class="html-italic">harmala</span> under 5% and 25% PEG vs. C (mean ± SD, n = 3). Abbreviations: <span class="html-italic">GMPM1</span>, 18 kDa seed maturation protein; <span class="html-italic">ASP</span>, 21 kDa seed protein; <span class="html-italic">ACT7</span>, actin 7; <span class="html-italic">AC97</span>, actin 97; <span class="html-italic">AP2</span>, floral homeotic protein APETALA 2; <span class="html-italic">At4g25140</span>, oleosin 18.5 kDa; <span class="html-italic">At5g40420</span>, oleosin 21.2 kDa; <span class="html-italic">OLE18</span>, oleosin Zm-II; <span class="html-italic">SPD1</span>, protein SEEDLING PLASTID DEVELOPMENT 1; <span class="html-italic">SBP65</span>, seed biotin-containing protein SBP65; <span class="html-italic">pec2a1a</span>, Vicilin Car i 2.0101; <span class="html-italic">At2g18540</span>, vicilin-like seed storage protein At2g18540; vs., versus.</p> Full article ">Figure 8
<p>The RELs of TFs directly associated with seed germination and stress response in <span class="html-italic">P</span>. <span class="html-italic">harmala</span> under 5% and 25% PEG vs. C (mean ± SD, n = 3). Abbreviations: <span class="html-italic">MYB4</span>, transcription factor MYB4; <span class="html-italic">MYB102</span>, transcription factor MYB102; <span class="html-italic">MYB330</span>, Myb-related protein 330; <span class="html-italic">BZIP34</span>, basic leucine zipper 34; <span class="html-italic">BZIP53</span>, bZIP transcription factor 53; <span class="html-italic">WRKY6</span>, WRKY transcription factor 6; <span class="html-italic">WRKY71</span>, WRKY transcription factor 71; <span class="html-italic">NAC019</span>, NAC domain-containing protein 19; <span class="html-italic">NAC92</span>, NAC domain-containing protein 92; <span class="html-italic">NAC056</span>, NAC transcription factor 56; <span class="html-italic">BHLH94</span>, transcription factor bHLH94; <span class="html-italic">BHLH128</span>, transcription factor bHLH128; vs., versus.</p> Full article ">Figure 9
<p>The RELs of genes associated with hormones in <span class="html-italic">P. harmala</span> under 5% and 25% PEG vs. C (mean ± SD, n = 3). Abbreviations: <span class="html-italic">GID1B</span>, gibberellin receptor GID1B; <span class="html-italic">ABP20</span>, auxin-binding protein ABP20; <span class="html-italic">AX10A</span>, auxin-induced protein X10A; <span class="html-italic">ARF5</span>, auxin response factor 5; <span class="html-italic">IAA16</span>, auxin-responsive protein IAA16; <span class="html-italic">SAUR32</span>, auxin-responsive protein SAUR32; <span class="html-italic">AHK2</span>, histidine kinase 2; <span class="html-italic">AHP4</span>, histidine-containing phosphotransfer protein 4; <span class="html-italic">ARR3</span>, two-component response regulator ARR3; <span class="html-italic">CYP94B1</span>, cytochrome P450 94B1; <span class="html-italic">GRXC9</span>, glutaredoxin-C9; <span class="html-italic">CPN20</span>, 20 kDa chaperonin; <span class="html-italic">PYL4</span>, abscisic acid receptor PYL4; <span class="html-italic">NRP1</span>, nodulin-related protein 1; <span class="html-italic">ERF.C.3</span>, ethylene-response factor C3; <span class="html-italic">ERF1B</span>, ethylene-responsive TF 1B; <span class="html-italic">ERF113</span>, ethylene-responsive TF ERF113; <span class="html-italic">EIN3</span>, protein ETHYLENE INSENSITIVE 3; vs., versus.</p> Full article ">Figure 10
<p>An outlined model of seed germination of <span class="html-italic">P. harmala</span> in response to drought stress. Abbreviations: <span class="html-italic">ADH1</span>, alcohol dehydrogenase class P; <span class="html-italic">ANN1</span>, annexin D1; <span class="html-italic">CRYs</span>, cryptochromes; <span class="html-italic">pgdC</span>, 6-phosphogluconate dehydrogenase, decarboxylating 1; <span class="html-italic">SUSs</span>, sucrose synthase; <span class="html-italic">FBAs</span>, fructose-bisphosphate aldolases; <span class="html-italic">APA1</span>, aspartic proteinase A1; <span class="html-italic">FTSHs</span>, ATP-dependent zinc metalloprotease FTSHs; <span class="html-italic">RPT1</span>, 26S proteasome regulatory subunit 7; <span class="html-italic">MYBs</span>, transcription factor MYBs; <span class="html-italic">BZIPs</span>, bZIP transcription factors; <span class="html-italic">WRKYs</span>, WRKY transcription factors; <span class="html-italic">GMPM1</span>, 18 kDa seed maturation protein; <span class="html-italic">ASP</span>, 21 kDa seed protein; <span class="html-italic">At4g25140</span>, oleosin 18.5 kDa.</p> Full article ">

13 pages, 5033 KiB  

Open AccessArticle

Do Morphological Variations in Sclerotinia sclerotiorum Strains Indicate Differences in Aggressiveness?

by Ramóna Vizi, József Kiss, György Turóczi, Nóra Dobra and Zoltán Pálinkás

Stresses 2024, 4(2), 367-379; https://doi.org/10.3390/stresses4020024 - 7 Jun 2024

Viewed by 872

Abstract

White mold (Sclerotinia sclerotiorum de Bary) is one of the most important fungal diseases of winter oilseed rape (OSR). Since the pathogen can persist in the soil for a long time with its sclerotia, prevention and non-chemical methods (specifically biological agents) are [...] Read more.

White mold (Sclerotinia sclerotiorum de Bary) is one of the most important fungal diseases of winter oilseed rape (OSR). Since the pathogen can persist in the soil for a long time with its sclerotia, prevention and non-chemical methods (specifically biological agents) are important pillars in the integrated plant protection strategy against this pathogen. Mapping the intraspecific variability of the pathogen is an important step in the development of resistance to S. sclerotiorum. S. sclerotiorum isolates were collected from different OSR growing locations in Hungary during the 2020/21 and 2021/22 growing seasons. The morphological characteristics of sclerotia obtained from infected OSR stems were studied in the laboratory, and seedlings of four OSR hybrids were infected in vitro with isolates. The strains from four locations have different morphological characteristics. Significant differences in the level of aggressivity were also observed between strains; a correlation was also found between mycelial growth after 24 h, weight of sclerotia, and aggressivity. Among the four tested hybrids, OSR PT271 proved to be the most susceptible to most S. sclerotinia strains. Full article

(This article belongs to the Topic Plant Responses to Environmental Stress)

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Figure 1
<p>Isolates from the two growing seasons (2020/21 and 2021/22).</p> Full article ">Figure 2
<p>Mycelial growth of the <span class="html-italic">Sclerotinia sclerotiorum</span> strains after 24, 48, and 72 h. Bars with different letters are significantly different.</p> Full article ">Figure 3
<p>Sclerotial production per PDA plate of <span class="html-italic">Sclerotinia sclerotiorum</span> strains at 240 h after inoculation. Bars with different letters are significantly different.</p> Full article ">Figure 4
<p>Total sclerotial weight per PDA plate of <span class="html-italic">Sclerotinia sclerotiorum</span> strains at 240 h after inoculation. Bars with different letters are significantly different.</p> Full article ">Figure 5
<p>Level of aggressivity of <span class="html-italic">Sclerotinia sclerotiorum</span> strains on the four hybrids. Bars with different letters are significantly different.</p> Full article ">Figure 6
<p>The correlations between the mycelial growth after 24 h and 48 h, number of sclerotia, weight of sclerotia, and aggressivity of the eight strains on the four hybrids.</p> Full article ">Figure 7
<p>The level of aggressivity is correlated between the two growing seasons for the four examined hybrids.</p> Full article ">Figure 8
<p>The four different sampling locations for <span class="html-italic">Sclerotinia sclerotiorum</span> isolates in Hungary [<a href="#B44-stresses-04-00024" class="html-bibr">44</a>].</p> Full article ">Figure 9
<p>A scale from 0 to 4 used to determine the level of aggressivity.</p> Full article ">

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22 pages, 14878 KiB  

Open AccessArticle

Physiological Characteristics and Transcriptomic Responses of Pinus yunnanensis Lateral Branching to Different Shading Environments

by Chiyu Zhou, Xuesha Gu, Jiangfei Li, Xin Su, Shi Chen, Junrong Tang, Lin Chen, Nianhui Cai and Yulan Xu

Plants 2024, 13(12), 1588; https://doi.org/10.3390/plants13121588 - 7 Jun 2024

Viewed by 676

Abstract

Pinus yunnanensis is an important component of China’s economic development and forest ecosystems. The growth of P. yunnanensis seedlings experienced a slow growth phase, which led to a long seedling cultivation period. However, asexual reproduction can ensure the stable inheritance of the superior [...] Read more.

Pinus yunnanensis is an important component of China’s economic development and forest ecosystems. The growth of P. yunnanensis seedlings experienced a slow growth phase, which led to a long seedling cultivation period. However, asexual reproduction can ensure the stable inheritance of the superior traits of the mother tree and also shorten the breeding cycle. The quantity and quality of branching significantly impact the cutting reproduction of P. yunnanensis, and a shaded environment affects lateral branching growth, development, and photosynthesis. Nonetheless, the physiological characteristics and the level of the transcriptome that underlie the growth of lateral branches of P. yunnanensis under shade conditions are still unclear. In our experiment, we subjected annual P. yunnanensis seedlings to varying shade intensities (0%, 25%, 50%, 75%) and studied the effects of shading on growth, physiological and biochemical changes, and gene expression in branching. Results from this study show that shading reduces biomass production by inhibiting the branching ability of P. yunnanensis seedlings. Due to the regulatory and protective roles of osmotically active substances against environmental stress, the contents of soluble sugars, soluble proteins, photosynthetic pigments, and enzyme activities exhibit varying responses to different shading treatments. Under shading treatment, the contents of phytohormones were altered. Additionally, genes associated with phytohormone signaling and photosynthetic pathways exhibited differential expression. This study established a theoretical foundation for shading regulation of P. yunnanensis lateral branch growth and provides scientific evidence for the management of cutting orchards. Full article

(This article belongs to the Topic Plant Responses to Environmental Stress)

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<p>The response of <span class="html-italic">P. yunnanensis</span> branches number and branch length to different shading treatments. Different letters indicate significant differences between treatments within the same month, according to Duncan’s multiple range test (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 2
<p>Biomass in different organs of <span class="html-italic">P. yunnanensis</span> responding to shading ((<b>a</b>) Main root biomass; (<b>b</b>) Lateral root biomass; (<b>c</b>) Main stem biomass; (<b>d</b>) Needles biomass; (<b>e</b>) Lateral branch biomass; (<b>f</b>) Shoot branch biomass). The same letters above the error bars indicate that there are no statistically significant differences, according to Duncan’s multiple range test (<span class="html-italic">p</span> &lt; 0.05). Different colors indicate different treatments.</p> Full article ">Figure 3
<p>The effect of shading on chlorophyll a, chlorophyll b, total chlorophyll, and carotenoid content in branching of <span class="html-italic">P. yunnanensis</span> ((<b>a</b>) Chlorophyll a content; (<b>b</b>) Chlorophyll b content; (<b>c</b>) Total chlorophyll content; (<b>d</b>) Carotenoids content).</p> Full article ">Figure 4
<p>The response of physiological characteristics to different shading treatments over time. (<b>a</b>) Peroxidase (POD) content; (<b>b</b>) catalase (CAT) content; (<b>c</b>) superoxide dismutase (SOD) content; (<b>d</b>) malondialdehyde (MDA) content; (<b>e</b>) Soluble sugar content; (<b>f</b>) Soluble protein content.</p> Full article ">Figure 5
<p>The response of endogenous hormones in the branches of <span class="html-italic">P. yunnanensis</span> to different degrees of shading (μg/g). Gibberellins (GA3, GA4, and GA7), auxin (IAA), abscisic acid (ABA), jasmonic acids (JA, JA-Ile, and MEJA), cytokinins (ZR, 2IP, and IPA), and salicylic acid (SA) content under different shading treatments. The same letters above the error bars indicate that there are no statistically significant differences according to Duncan’s multiple range test (<span class="html-italic">p</span> &lt; 0.05). Different colors indicate different treatments.</p> Full article ">Figure 6
<p>GO classifications of assembly of uni-genes in <span class="html-italic">P. yunnanensis</span> under different shadings.</p> Full article ">Figure 7
<p>In order to characterize the DEGs, the pairs of different shading levels (CK, L1, L2, and L3) of <span class="html-italic">P. yunnanensis</span> seedlings were compared. (<b>A</b>) Venn diagram of DEGs in different comparison groups. (<b>B</b>) Statistical data on upregulation and downregulation of genes between different shading treatment groups. (<b>C</b>) Cluster analysis of DEGs in <span class="html-italic">P. yunnanensis</span> with different levels of shading.</p> Full article ">Figure 8
<p>The top 20 KEGG pathways significantly enriched in differentially expressed genes under various shading conditions in <span class="html-italic">P. yunnanensis</span>. (<b>A</b>) CK vs. L1. (<b>B</b>) CK vs. L2. (<b>C</b>) CK vs. L3. (<b>D</b>) L1 vs. L2. (<b>E</b>) L1 vs. L3. (<b>F</b>) L2 vs. L3.</p> Full article ">Figure 9
<p>Heating maps of gene expression associated with photosynthesis pathway in <span class="html-italic">P. yunnanensis</span> under different shading treatments. Red colors represent up-regulation, and purple colors represent downregulation.</p> Full article ">Figure 10
<p>Expression profiles of gene expression related to plant hormone signal transduction pathways. Red colors present upregulation, and purple colors present downregulation.</p> Full article ">Figure 11
<p>RT-qPCR validation of DEGs in <span class="html-italic">P. yunnanensis</span> under different shading levels. <span class="html-italic">X</span>-axis: shading treatments; left <span class="html-italic">Y</span>-axis: FPKM values from RNA-seq; right <span class="html-italic">Y</span>-axis: relative expression levels from RT-qPCR. The white bars represent the RNA-seq expression levels under different treatments, while the gray bars represent the RT-qPCR expression levels under different treatments.</p> Full article ">

13 pages, 2201 KiB  

Open AccessArticle

UV-B Radiation Disrupts Membrane Lipid Organization and Suppresses Protein Mobility of GmNARK in Arabidopsis

by Qiulin Liu, Tianyu Wang, Meiyu Ke, Chongzhen Qian, Jiejie Li, Xi Huang, Zhen Gao, Xu Chen and Tianli Tu

Plants 2024, 13(11), 1536; https://doi.org/10.3390/plants13111536 - 1 Jun 2024

Viewed by 572

Abstract

While it is well known that plants interpret UV-B as an environmental cue and a potential stressor influencing their growth and development, the specific effects of UV-B-induced oxidative stress on the dynamics of membrane lipids and proteins remain underexplored. Here, we demonstrate that [...] Read more.

While it is well known that plants interpret UV-B as an environmental cue and a potential stressor influencing their growth and development, the specific effects of UV-B-induced oxidative stress on the dynamics of membrane lipids and proteins remain underexplored. Here, we demonstrate that UV-B exposure notably increases the formation of ordered lipid domains on the plasma membrane (PM) and significantly alters the behavior of the Glycine max nodule autoregulation receptor kinase (GmNARK) protein in Arabidopsis leaves. The GmNARK protein was located on the PM and accumulated as small particles in the cytoplasm. We found that UV-B irradiation interrupted the lateral diffusion of GmNARK proteins on the PM. Furthermore, UV-B light decreases the efficiency of surface molecule internalization by clathrin-mediated endocytosis (CME). In brief, UV-B irradiation increased the proportion of the ordered lipid phase and disrupted clathrin-dependent endocytosis; thus, the endocytic trafficking and lateral mobility of GmNARK protein on the plasma membrane are crucial for nodule formation tuning. Our results revealed a novel role of low-intensity UV-B stress in altering the organization of the plasma membrane and the dynamics of membrane-associated proteins. Full article

(This article belongs to the Topic Plant Responses to Environmental Stress)

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Figure 1
<p>UV-B light increases the proportion of ordered lipid phase and disrupts clathrin-mediated endocytosis. (<b>A</b>,<b>B</b>) WL-grown <span class="html-italic">pAtRemorin1.2:AtRemorin1.2-GFP</span> (in <span class="html-italic">Arabidopsis</span>) seedlings were exposed to UV-B light (40 μW/cm²) for 24 h or in long-term (4 d) treatment or continuously grown under WL. The Remorin1.2-GFP fluorescence signal on the PM was calculated (from left to right: n = 118, 128, and 123 cells (<b>B</b>)). (<b>C</b>–<b>F</b>) PM lipid order was visualized in <span class="html-italic">Arabidopsis</span> leaf epidermal cells in a series of UV-B treatments (<b>C</b>,<b>D</b>). The seedlings were treated with WL or UV-B (40 μW/cm², 24 h) in the absence or presence of 10 mM of mβcd (24 h) (<b>E</b>,<b>F</b>). Then, the treated seedlings were stained with di-4-ANEPPDHQ. Radiometric color-coded GP images were generated in HSB pictures (<b>C</b>,<b>E</b>), and mean GP value was calculated (from left to right: n = 88, 147, 165, and 156 images (<b>D</b>); n = 85, 110, 88, and 148 images (<b>F</b>)). Scale bars, 10 μm (<b>A</b>,<b>C</b>,<b>E</b>). Error bar = S.D. <span class="html-italic">p</span>-values were determined using two-tailed Student’s t-test, assuming equal variances (** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001; **** <span class="html-italic">p</span> &lt; 0.0001).</p> Full article ">Figure 2
<p>UV-B disrupts the internalization and lateral diffusion of GmNARK proteins. (<b>A</b>) GmNARK-GFP distribution of WL-grown <span class="html-italic">35s:GmNARK-GFP</span> (in <span class="html-italic">Arabidopsis</span>) was visualized. The internalized GmNARK-GFP proteins within the cytosol are highlighted by arrows. (<b>B</b>–<b>F</b>) GmNARK-GFP foci on the PM were recorded using VA-TIRFM in 5-day-old <span class="html-italic">35s:GmNARK-GFP</span> (in <span class="html-italic">Arabidopsis</span>) seedlings under WL, UV-B light (20 or 40 μW/cm² for 48 h), WL supplemented with LatB (10 μM, 1 h), or WL supplemented with Tyr23A/51A (30 μM, 1 h). Cotyledon epidermal cells were observed. Trajectories of GmNARK-GFP bright foci (<b>B</b>,<b>C</b>,<b>E</b>) and weak foci (<b>D</b>,<b>F</b>) were individually tracked. Mean-squared displacement (MSD) and diffusion coefficient of GmNARK-GFP particle are plotted and quantified in (<b>E</b>) (left panel: n = 60 images; right panel: n = 14, 14, 12, 11, 14, and 14 images), (<b>F</b>) (left panel: n = 15 images; right panel: n = 12 and 11 images). Scale bars, 10 µm (<b>A</b>), 5 µm (<b>B</b>,<b>C</b>), and 1 µm (<b>D</b>). Error bar = S.D. <span class="html-italic">p</span>-values were determined using two-tailed Student’s t-test, assuming equal variances (* <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; 0.0001; ns, not significant).</p> Full article ">Figure 3
<p>UV-B impairs mobility of GmNARK proteins and attenuation of GmNARK protein levels. (<b>A</b>,<b>B</b>) CLC2-RFP lifetime on PM was quantitatively measured in WT <span class="html-italic">Arabidopsis</span> leaf epidermal cells. 5-day-old WT seedlings were treated with WL or UV-B (40 μW/cm²) for 48 h before being imaged using VA-TIRFM. The kymographs of the 120 s time course showed the residence of CLC2-RFP particles on the PM, indicated by white boxes (<b>A</b>). Quantitative analyses of CLC2-RFP particle lifetimes are shown (B: n = 241 and 39 images for each treatment). (<b>C</b>,<b>D</b>) UV-B-treated (40 μW/cm², 24 h or 48 h pretreatment) and WL-grown 5-day-old <span class="html-italic">35s:GmNARK-GFP</span> (in <span class="html-italic">Arabidopsis</span>) specimens were incubated in 200 µM of BFA for 2 h. Internalization signal of GmNARK-GFP-termed BFA body was quantified as a ratio comparing each GmNARK-GFP signal on the PM (D: n = 142, 74, and 109 cells from left to right). The comparison ratio of the internalization signal under UV-B relative to WL is shown as percentage (<b>D</b>). (<b>E</b>,<b>F</b>) Four-day-old <span class="html-italic">35s:GmNARK-GFP Arabidopsis</span> seedlings were subjected to UV-B-treatment (10 μW/cm⁻², 0, 2 d, 3 d, or 5 d pretreatment). Total GmNARK-GFP protein and AtRemorin1.1 levels and microsome protein were tested using Western blot. Proteins were analyzed via immunoblotting with anti-GFP, anti-Remorin1.1, and anti-RPN6 antibodies. RPN6 was used as a loading control. <span class="html-italic">uvr8-6</span> is <span class="html-italic">Atuvr8</span> mutant <span class="html-italic">Arabidopsis</span> (<b>E</b>). Quantification of GmNARK-GFP and REM1.1 protein levels was conducted. Protein signal ratios were used to indicate the signal strength of GmNARK-GFP and REM1.1 relative to each RPN6 signal on the protein blot, measured in arbitrary units via densitometry (<b>F</b>). Scale bars, 10 µm (<b>C</b>); error bar = S.D. <span class="html-italic">p</span>-values were determined using two-tailed Student’s t-test assuming equal variances; **** <span class="html-italic">p</span> &lt; 0.0001).</p> Full article ">

10 pages, 1289 KiB  

Open AccessArticle

Comparison of the Waterlogging Tolerance and Morphological Responses of Five Urochloa spp. Grasses

by Rafael Marzall Amaral, Lesly Astrid Calva Sarango, Cristiano Eduardo Rodrigues Reis, Tulio Otávio Jardim D’almeida Lins, Ericka Beatriz Schultz and Daniel Carballo Guerrero

Stresses 2024, 4(2), 320-329; https://doi.org/10.3390/stresses4020020 - 8 May 2024

Viewed by 828

Abstract

Periods with high precipitation and temporary waterlogging in the humid tropics are challenging to the production and survival of some grasses of the genus Urochloa. This study aimed to evaluate the tolerance of five types of grass belonging to the genus Urochloa [...] Read more.

Periods with high precipitation and temporary waterlogging in the humid tropics are challenging to the production and survival of some grasses of the genus Urochloa. This study aimed to evaluate the tolerance of five types of grass belonging to the genus Urochloa under waterlogging conditions through productive and morphological traits. The grasses [U. arrecta (Tanner), U. arrecta x U. mutica (Brachipará), U. brizantha cv. Marandú, U. hybrid cv. Cayman and U. humidicola cv. Llanero] were planted in pots and kept under field capacity for 33 days; then, half of them were submitted to (i) field capacity (33% humidity retention) and the other half were submitted to (ii) waterlogging conditions (2 cm of water above soil level) for 28 days. In this study, Tanner and Brachipará grasses showed higher dry shoot mass under waterlogging conditions, which were followed by Llanero, Cayman, and Marandú, respectively. Llanero, Tanner, and Brachipará presented higher waterlogging tolerance coefficients, 78.7, 76.5, and 64.5, respectively, being less affected than Cayman and Marandú (41.0 and 23.1, respectively). Brachipará, Tanner, and Cayman presented a higher root volume under waterlogging conditions, while Marandú root volume decreased by 88.77%. The Tanner, Brachipará, and Llanero genotypes were more tolerant to poorly drained or waterlogged soils than Cayman and Marandú genotypes. Full article

(This article belongs to the Topic Plant Responses to Environmental Stress)

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Figure 1

Figure 1
<p>(<b>a</b>) Dry shoot mass (g·pot⁻¹) and (<b>b</b>) dry root mass (g·pot⁻¹) of five <span class="html-italic">Urochloa</span> genotypes under two soil moisture contents (field capacity—gray bar and waterlogging—white bar). Wide vertical bars indicate the mean, and thin vertical bars indicate standard-error values. Different letters indicate differences (<span class="html-italic">p</span> ≤ 0.05, Tukey test). Where G is genotype, SMC is soil moisture content, and G x SMC is the interaction between genotype and soil moisture content. F-test significance code, n.s. (<span class="html-italic">p</span> &gt; 0.1), · (<span class="html-italic">p</span> ≤ 0.1), * (<span class="html-italic">p</span> ≤ 0.05), and *** (<span class="html-italic">p</span> ≤ 0.001).</p> Full article ">Figure 2
<p>Example of adventitious aerial roots in Tanner grass (<span class="html-italic">Urochloa arrecta</span>) at day 28 in waterlogging conditions.</p> Full article ">Figure 3
<p>(<b>a</b>) Leaf elongation rate (cm.day⁻¹), (<b>b</b>) leaf length (cm), (<b>c</b>) leaf appearance rate (leaf.day⁻¹), (<b>d</b>) total of green leaves, (<b>e</b>) stem elongation rate (cm.day⁻¹), and (<b>f</b>) final stem length (cm) analyzed as a function of main effects: <span class="html-italic">Urochloa</span> genotypes (light gray bars) and soil moisture contents (field capacity (FC)– dark gray bar and waterlogging (WL)—white bar). Wide vertical bars indicate means, and thin vertical bars indicate standard-error values. Different letters indicate differences (<span class="html-italic">p</span> ≤ 0.05, Tukey test). Where G is genotype, SMC is soil moisture content, and G x SMC is the interaction between genotype and soil moisture content. F-test significance code, n.s. (<span class="html-italic">p</span> &gt; 0.1) and *** (<span class="html-italic">p</span> ≤ 0.001).</p> Full article ">Figure 4
<p>Green tillers per pot as a function of recovery days of five <span class="html-italic">Urochloa</span> genotypes: Brachipará, Cayman, Llanero, Marandú, and Tanner under two soil moisture contents (field capacity—solid line with gray squares and waterlogging—dashed line with white squares). Vertical bars indicate the confidence interval (95%).</p> Full article ">

14 pages, 9901 KiB  

Open AccessArticle

MicroRNA164 Affects Plant Responses to UV Radiation in Perennial Ryegrass

by Chang Xu, Xin Huang, Ning Ma, Yanrong Liu, Aijiao Xu, Xunzhong Zhang, Dayong Li, Yue Li, Wanjun Zhang and Kehua Wang

Plants 2024, 13(9), 1242; https://doi.org/10.3390/plants13091242 - 30 Apr 2024

Cited by 1 | Viewed by 926

Abstract

Increasing the ultraviolet radiation (UV) level, particularly UV-B due to damage to the stratospheric ozone layer by human activities, has huge negative effects on plant and animal metabolism. As a widely grown cool-season forage grass and turfgrass in the world, perennial ryegrass ( [...] Read more.

Increasing the ultraviolet radiation (UV) level, particularly UV-B due to damage to the stratospheric ozone layer by human activities, has huge negative effects on plant and animal metabolism. As a widely grown cool-season forage grass and turfgrass in the world, perennial ryegrass (Lolium perenne) is UV-B-sensitive. To study the effects of miR164, a highly conserved microRNA in plants, on perennial ryegrass under UV stress, both OsmiR164a overexpression (OE164) and target mimicry (MIM164) transgenic perennial ryegrass plants were generated using agrobacterium-mediated transformation, and UV-B treatment (~600 μw cm⁻²) of 7 days was imposed. Morphological and physiological analysis showed that the miR164 gene affected perennial ryegrass UV tolerance negatively, demonstrated by the more scorching leaves, higher leaf electrolyte leakage, and lower relative water content in OE164 than the WT and MIM164 plants after UV stress. The increased UV sensitivity could be partially due to the reduction in antioxidative capacity and the accumulation of anthocyanins. This study indicated the potential of targeting miR164 and/or its targeted genes for the genetic manipulation of UV responses in forage grasses/turfgrasses; further research to reveal the molecular mechanism underlying how miR164 affects plant UV responses is needed. Full article

(This article belongs to the Topic Plant Responses to Environmental Stress)

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Figure 1
<p>Generation and molecular confirmation of Os-miR164a overexpression (OE164) and target mimicry (MIM164) transgenic perennial ryegrass plants. (<b>A</b>–<b>F</b>) Callus transformation and transgenic plant generation process. (<b>G</b>) The schematic map of the T-DNA region of Os-miR164a overexpression (35S::Os-miR164//35S::Hyg) and target mimicry (35S::At-ISP1-MIM164//35S::Hyg) construct; UBi, maize ubiquitin promotor; 35S, CaMV 35S promoter; LB/RB, left/right border; NOS, nopaline synthase; PolyA, poly adenine; Hyg, hygromycin B phosphotransferase gene. (<b>H</b>,<b>I</b>) PCR assay of miRNA164 overexpression and target mimicry transgenic plants; T1–8/T1–10, putative OE164/MIM164 plants; +, positive control, pZH01. (<b>J</b>,<b>K</b>) RT-PCR assay of miRNA164 overexpression (<b>J</b>) and target mimicry (<b>K</b>) transgenic plants; T1–5/T1–5, putative OE164/MIM164 plants; +, positive control, pTCK303; −, negative control, H2O; WT, wild type; M, molecular marker.</p> Full article ">Figure 2
<p>Phenotypic analysis of Os-<span class="html-italic">miR164a</span> overexpression (OE164) and target mimicry (MIM164) transgenic perennial ryegrass plants. (<b>A</b>) Comparison of whole plants of OE164 and WT. (<b>B</b>) Comparison of whole plants of MIM164 and WT. (<b>C</b>) Comparison of leaves of OE164, WT, and MIM164. (<b>D</b>–<b>F</b>) Plant height, leaf length, and leaf width of OE164, WT, and MIM164. The different lowercase letters indicate a significant difference among plants at <span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">n</span> = 40.</p> Full article ">Figure 3
<p>Morphological changes (<b>A</b>,<b>B</b>) and leaf relative water content (<b>C</b>); leaf electrolyte leakage (<b>D</b>) of OE164, WT, and MIM164 plants after UV+. The different lowercase letters indicate a significant difference among the plants 3 and 7 days after UV+ at <span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">n</span> = 5.</p> Full article ">Figure 4
<p>Leaf pigments and total phenolic content of OE164, WT, and MIM164 plants after UV+. (<b>A</b>–<b>C</b>) Chlorophyll a and b content and chlorophyll a/b ratio; (<b>D</b>) carotenoid content; (<b>E</b>) total anthocyanin content; (<b>F</b>) total phenolic content. UV−, normal growth condition. UV+3d and UV+7d 3 and 7 days after UV+. The different lowercase letters indicate a significant difference among the plants at <span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">n</span> = 5.</p> Full article ">Figure 5
<p>The activities of antioxidant enzymes and production of reactive oxygen species of OE164, WT, and MIM164 plants after UV stress. (<b>A</b>–<b>D</b>) The activities of superoxide dismutase (SOD), guaiacol peroxidase (POD), catalase (CAT), and ascorbate peroxidase (APX); (<b>E</b>,<b>F</b>) leaf NBT and DAB staining and staining intensity quantification. UV−, normal growth condition. UV+3d and UV+7d 3 and 7 days after UV treatment. The different lowercase letters indicate a significant difference among the plants at <span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">n</span> = 5.</p> Full article ">

18 pages, 1338 KiB  

Open AccessArticle

Phosphorus Dynamics in Stressed Soil Systems: Is There a Chemical and Biological Compensating Effect?

by Bruna Arruda, Fábio Prataviera, Wilfrand Ferney Bejarano Herrera, Denise de Lourdes Colombo Mescolotti, Antonio Marcos Miranda Silva, Hudson Wallace Pereira de Carvalho, Paulo Sergio Pavinato and Fernando Dini Andreote

Stresses 2024, 4(2), 251-268; https://doi.org/10.3390/stresses4020015 - 2 Apr 2024

Viewed by 1113

Abstract

Here, we hypothesized the occurrence of a compensatory relationship between the application of P and different microbial communities in the soil, specifically in relation to the chemical and biological effects in the soil–plant–microorganisms’ interaction. We aimed to evaluate the plant–microbiota responses in plants [...] Read more.

Here, we hypothesized the occurrence of a compensatory relationship between the application of P and different microbial communities in the soil, specifically in relation to the chemical and biological effects in the soil–plant–microorganisms’ interaction. We aimed to evaluate the plant–microbiota responses in plants grown in soils hosting distinct microbial communities and rates of P availability. Two experiments were carried out in a greenhouse. The first experiment evaluated four manipulated soil microbiome compositions, four P rates, and two plant species. Manipulated soil systems were obtained by the following: (i) autoclaving soil for 1 h at 121 °C (AS); (ii) inoculating AS with soil suspension dilution (AS + 10⁻³); (iii) heating natural soil at 80 °C for 1 h (NH80); or (iv) using natural soil (NS) without manipulation. The P rates added were 0, 20, 40, and 60 mg kg⁻¹, and the two plant species tested were grass (brachiaria) and leguminous (crotalaria). Inorganic labile P (P_AER), microbial P (P_MIC), acid phosphatase activity (AP_ASE), and shoot P uptake (P_UPT) were assessed for each system. Brachiaria presented a compensatory effect for P_UPT, whereby the addition of P under conditions of low microbial community enhanced P absorption capacity from the soil. However, in a system characterized by low P input, the increase in the soil biodiversity was insufficient to enhance brachiaria P_UPT. Likewise, crotalaria showed a higher P_UPT under high P application and low microbial community. The second experiment used three manipulated microbiome compositions: AS + 10⁻³; NH80; and NS and three P rates added: 0, 20, and 40 mg kg⁻¹. In addition, two treatments were set: without and with mycorrhiza inoculation. Brachiaria showed an increase in the P_UPT under low microbial communities (AS + 10⁻³; NH80) with P addition (20 and 40 mg kg⁻¹ of P), but no mycorrhization was observed. In the undisturbed microbial community (NS), under no P input (0 mg kg⁻¹ of P), brachiaria showed low mycorrhization and low P_UPT. Finally, NS and the recommended P input (40 mg kg⁻¹ of P) represented a balance between chemical and biological fertility, promoting the equilibrium between mycorrhization and P_UPT. Full article

(This article belongs to the Topic Plant Responses to Environmental Stress)

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Figure 1
<p>Principal component analysis (PCA) of P_AER, AP_ASE, P_MIC, and P_UPT from samples taken after cropping with brachiaria and crotalaria in soils subjected to soil microbial manipulation and P rate application. Treatment abbreviations are as follows: sterilized soil by autoclaving twice (121 °C, 103 kPa, 1 h) without soil dilution inoculation (AS); autoclaved soil followed by re-inoculation with dilution from the natural soil (10% <span class="html-italic">w</span>/<span class="html-italic">v</span> of natural soil) (AS + 10⁻³); soil heated at 80 °C for 1 h (NH80); and natural soil without manipulation (NS). P fertilizer was applied as triple superphosphate at the following rates: 0; 20; 40; and 60 mg kg⁻¹ of P. P_AER: inorganic labile P extracted using anion exchange resin; AP_ASE: acid phosphatase activity; P_MIC: microbial P; P_UPT: up taken P on shoot.</p> Full article ">Figure 2
<p>Relationship between P uptake (P_UPT) and mycorrhization rate (MYC) from samples taken after cropping with brachiaria in soils subjected to three soil microbial communities: three P rate application and mycorrhiza inoculation. Treatment abbreviations are as follows: autoclaved soil followed by re-inoculation with dilution from the natural soil (10% <span class="html-italic">w</span>/<span class="html-italic">v</span> of natural soil) (AS + 10⁻³); soil heated at 80 °C for 1 h (NH80); and natural soil without manipulation (NS); P fertilizer was applied as triple superphosphate at the following rates: 0; 20; and 40 mg kg⁻¹ of P; mycorrhiza inoculation: non-mycorrhiza inoculation (NMI) and with mycorrhiza inoculation (WMI).</p> Full article ">

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Supplementary material:
Supplementary File 1 (ZIP, 172 KiB)

17 pages, 5145 KiB  

Open AccessArticle

Photosystem II Tolerance to Excess Zinc Exposure and High Light Stress in Salvia sclarea L.

by Michael Moustakas, Anelia Dobrikova, Ilektra Sperdouli, Anetta Hanć, Julietta Moustaka, Ioannis-Dimosthenis S. Adamakis and Emilia Apostolova

Agronomy 2024, 14(3), 589; https://doi.org/10.3390/agronomy14030589 - 15 Mar 2024

Cited by 3 | Viewed by 992

Abstract

High light (HL) intensity has a substantial impact on light energy flow and partitioning within photosynthetic apparatus. To realize the impact of HL intensity on zinc (Zn) tolerance mechanisms in clary sage (Salvia sclarea L., Lamiaceae) plants, we examined the effect of [...] Read more.

High light (HL) intensity has a substantial impact on light energy flow and partitioning within photosynthetic apparatus. To realize the impact of HL intensity on zinc (Zn) tolerance mechanisms in clary sage (Salvia sclarea L., Lamiaceae) plants, we examined the effect of the altered chlorophyll and nutrient uptake under excess Zn supply on the response mechanism of photosystem II (PSII) photochemistry. Eight-week-old clary sage plants were treated with 5 μM Zn (control) or 900 μM Zn in Hoagland nutrient solution. Leaf elemental analysis for Zn, Mn, Mg, and Fe was performed by inductively coupled plasma mass spectrometry (ICP-MS), whereas PSII functioning under HL was evaluated by chlorophyll fluorescence imaging analysis. Exposure of S. sclarea plants to 900 μM Zn increased leaf Zn accumulation and decreased leaf Mg and chlorophyll. The decreased non-photochemical quenching (NPQ) provided evidence of the photoprotection offered by the smaller light-harvesting antennae due to the reduced chlorophyll. The increased Mn after Zn exposure corresponded with higher efficiency of the oxygen-evolving complex (OEC) that was significantly correlated with the maximum efficiency of photosystem II (PSII) photochemistry (Fv/Fm). An increased electron transport rate (ETR) coincided with increased leaf Fe, which is known to play a vital role in the enzymes engaged in ETR. The decreased (32%) NPQ after an 8-day exposure to Zn caused an increased (10%) quantum yield of non-regulated energy loss in PSII (Φ_NO), indicative of an increased singlet oxygen (¹O₂) production. It is suggested that the decreased NPQ induced acclimation responses of clary sage plants to HL and excess Zn by increasing ¹O₂ production. The reduced (18%) excess excitation energy (EXC) at PSII and the increased (24%) quantum yield of PSII photochemistry (Φ_PSII) and ETR indicated improved photosynthetic efficiency under excess Zn and HL intensity. Therefore, the exposure of medicinal plants to excess Zn not only boosts their photosynthetic efficiency, enhancing crop yields, but can also improve Fe and Zn content, ameliorating the human health deficiency of these two essential micronutrients. Full article

(This article belongs to the Topic Plant Responses to Environmental Stress)

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Figure 1
<p>Changes in leaf elemental content of clary sage plants: (<b>a</b>) zinc (µg g⁻¹ DW); (<b>b</b>) magnesium (µg g⁻¹ DW); of control plants and after 5 days of exposure to Zn. Different letters indicate statistical differences (<span class="html-italic">p</span> &lt; 0.05). Error bars symbolize SD.</p> Full article ">Figure 2
<p>Changes in leaf elemental content of clary sage plants: (<b>a</b>) iron (µg g⁻¹ DW); (<b>b</b>) manganese (µg g⁻¹ DW); of control plants, and after 5 days of exposure to Zn. Different letters indicate statistical differences (<span class="html-italic">p</span> &lt; 0.05). Error bars symbolize SD.</p> Full article ">Figure 3
<p>Effects of excess Zn on clary sage plants: (<b>a</b>) chlorophyll content of control plants and after 8 days of exposure to 900 μM Zn; (<b>b</b>) the functionality of the oxygen-evolving complex (F<span class="html-italic">v</span>/F<span class="html-italic">o</span>); of control plants, and after 3, 5, and 8 days of exposure to Zn. Different letters indicate statistical differences (<span class="html-italic">p</span> &lt; 0.05). Error bars symbolize SD.</p> Full article ">Figure 4
<p>Effects of excess Zn on clary sage plants: (<b>a</b>) the maximum efficiency of PSII photochemistry (F<span class="html-italic">v</span>/F<span class="html-italic">m</span>) of control plants, and after 3, 5, and 8 days of exposure to Zn. Different letters indicate statistical differences (<span class="html-italic">p</span> &lt; 0.05). Error bars symbolize SD. (<b>b</b>) correlation analysis between F<span class="html-italic">v</span>/F<span class="html-italic">m</span> and F<span class="html-italic">v</span>/F<span class="html-italic">o</span> of control plants, and after 3, 5, and 8 days of exposure to Zn (based on data of <a href="#agronomy-14-00589-f003" class="html-fig">Figure 3</a>b and <a href="#agronomy-14-00589-f004" class="html-fig">Figure 4</a>a). Each blue dot represents a paired observation of the variables and the red line is a regression line that shows the trend/relationship between the two variables. The pattern of the dots and their proximity to the red line indicates a strong positive correlation.</p> Full article ">Figure 5
<p>Changes in light energy use in clary sage plants after exposure to 900 μM Zn: (<b>a</b>) the quantum yield of PSII photochemistry (Φ<span class="html-italic">_PSII</span>); (<b>b</b>) the quantum yield of regulated non-photochemical energy loss in PSII (Φ<span class="html-italic">_NPQ</span>); of control plants, and after 3, 5, and 8 days of exposure to Zn. Different letters indicate statistical differences (<span class="html-italic">p</span> &lt; 0.05). Error bars symbolize SD.</p> Full article ">Figure 6
<p>Changes in the quantum yield of non-regulated energy loss in PSII (Φ<span class="html-italic">_NO</span>) and the non-photochemical quenching (NPQ) after exposure to 900 μM Zn: (<b>a</b>) the non-regulated energy loss in PSII (Φ<span class="html-italic">_NO</span>); (<b>b</b>) the non-photochemical quenching (NPQ); of control plants, and after 3, 5, and 8 days of exposure to Zn. Different letters indicate statistical differences (<span class="html-italic">p</span> &lt; 0.05). Error bars symbolize SD.</p> Full article ">Figure 7
<p>The percentage of open PSII reaction centers and their efficiency after Zn exposure: (<b>a</b>) the fraction of open PSII reaction centers (q<span class="html-italic">p</span>); (<b>b</b>) the efficiency of open PSII reaction centers (F<span class="html-italic">v</span>′/F<span class="html-italic">m</span>′); of control plants, and after 3, 5, and 8 days of exposure to Zn. Different letters indicate statistical differences (<span class="html-italic">p</span> &lt; 0.05). Error bars symbolize SD.</p> Full article ">Figure 8
<p>Changes on the electron transport rate (ETR) (<b>a</b>), and the excess excitation energy (EXC) (<b>b</b>); of control plants, and after 3, 5, and 8 days of exposure to 900 μM Zn. Different letters indicate statistical differences (<span class="html-italic">p</span> &lt; 0.05). Error bars symbolize SD.</p> Full article ">Figure 9
<p>Representative color-coded leaf images of PSII functionality after exposure of clary sage plants to Zn and 900 μmol photons m⁻² s⁻¹. Φ<span class="html-italic">_PSII</span>, Φ<span class="html-italic">_NPQ,</span> and q<span class="html-italic">p</span> of control plants, and after 5 and 8 days of exposure to Zn. The red labels on the leaf surface are the corresponding values of the measured chlorophyll fluorescence parameter documenting the spatiotemporal heterogeneity. A color code, representing each chlorophyll fluorescence parameter value, is shown.</p> Full article ">

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Supplementary material:
Supplementary File 1 (ZIP, 571 KiB)

20 pages, 5592 KiB  

Open AccessArticle

Transcriptome Profiling Reveals Molecular Responses to Salt Stress in Common Vetch (Vicia sativa L.)

by Yanmei Sun, Na Zhao, Hongjian Sun, Shan Xu, Yiwen Lu, Haojie Xi, Zhenfei Guo and Haifan Shi

Plants 2024, 13(5), 714; https://doi.org/10.3390/plants13050714 - 3 Mar 2024

Cited by 1 | Viewed by 1454

Abstract

Common vetch (Vicia sativa L.) is an important annual diploid leguminous forage. In the present study, transcriptomic profiling in common vetch in response to salt stress was conducted using a salt-tolerant line (460) and a salt-sensitive line (429). The common responses in [...] Read more.

Common vetch (Vicia sativa L.) is an important annual diploid leguminous forage. In the present study, transcriptomic profiling in common vetch in response to salt stress was conducted using a salt-tolerant line (460) and a salt-sensitive line (429). The common responses in common vetch and the specific responses associated with salt tolerance in 460 were analyzed. Several KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, including plant hormone and MAPK (mitogen-activated protein kinase) signaling, galactose metabolism, and phenylpropanoid phenylpropane biosynthesis, were enriched in both lines, though some differentially expressed genes (DEGs) showed distinct expression patterns. The roots in 460 showed higher levels of lignin than in 429. α-linolenic acid metabolism, carotenoid biosynthesis, the photosynthesis-antenna pathway, and starch and sucrose metabolism pathways were specifically enriched in salt-tolerant line 460, with higher levels of accumulated soluble sugars in the leaves. In addition, higher transcript levels of genes involved in ion homeostasis and reactive oxygen species (ROS) scavenging were observed in 460 than in 429 in response to salt stress. The transcriptomic analysis in common vetch in response to salt stress provides useful clues for further investigations on salt tolerance mechanism in the future. Full article

(This article belongs to the Topic Plant Responses to Environmental Stress)

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Figure 1

Figure 1
<p>Analysis of salt tolerance in two common vetch lines, 429 and 460. Plant performance was photographed using 8-day-old seedlings of the salt-tolerant line 460 and salt-sensitive line 429 after 3 d of 150 mM NaCl treatment (<b>a</b>,<b>b</b>), followed by measurements of dry weight of leaves (<b>c</b>) and roots (<b>d</b>) as well as the relative water content (RWC) in leaves (<b>e</b>). Means of fifteen samples and standard errors are presented. The same letter above the columns of each gene indicates no significant difference at <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 2
<p>Analysis of the differentially expressed genes (DEGs) in leaves and roots in two common vetch lines in response to salt stress. The number of up-regulated (Up) and down-regulated (Down) DEGs in leaves (<b>a</b>) and roots (<b>b</b>) in salt-tolerant line 460 and salt-sensitive line 429 after 2 h and 26 h of 150 mM NaCl treatment is summarized. Venn diagrams showed the overlap of DEGs in leaves (<b>c</b>) and roots (<b>d</b>) of 429 and 460 after 150 mM NaCl treatment for 2 h and 26 h compared with control. Software (<a href="https://www.omicshare.com/tools/" target="_blank">https://www.omicshare.com/tools/</a> (accessed on 23 November, 2023)) was used for the Venn diagram. SL, leaves in salt-sensitive line 429; TL, leaves in salt-tolerant line 460; SR, roots in salt-sensitive line 429; TR, roots salt-tolerant line 460; 0, 1, and 2 represent 0, 2 h, and 26 h after salt treatment, respectively.</p> Full article ">Figure 3
<p>KEGG pathway enrichment analysis of the DEGs in leaves and roots of 429 and 460 in response to salt stress. All the KEGG pathways that are significantly enriched in leaves (<b>a</b>) and roots (<b>b</b>) in salt-tolerant line 460 and salt-sensitive line 429 after 2 h and 26 h of salt treatment compared with 0 h are presented. Y-axis represents KEGG enrichment terms. The color of the dot represents −log₁₀ (Q value). It was shown that the statistical significance increased from blue to red (red represents high significance, while blue represents low). The size of the dot represents the ratio of enriched DEGs to background genes. SL, leaves in salt-sensitive line 429; TL, leaves in salt-tolerant line 460; SR, roots in salt-sensitive line 429; TR, roots salt-tolerant line 460; 0, 1, and 2 represent 0, 2 h, and 26 h after salt treatment, respectively.</p> Full article ">Figure 4
<p>Transcript levels of the genes involved in lignin synthesis in two common vetch lines in response to salt stress. The transcription level of <span class="html-italic">CAD1</span> (<b>a</b>,<b>b</b>), <span class="html-italic">CAD2</span> (<b>c</b>,<b>d</b>), <span class="html-italic">CCR1</span> (<b>e</b>,<b>f</b>), and <span class="html-italic">CCR2</span> (<b>g</b>,<b>h</b>) was detected in salt-tolerant line 460 and salt-sensitive line 429 after 0 h, 2 h, and 26 h of NaCl treatment using qRT-PCR analysis. The data from leaves are presented in (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>), while those from roots are presented in (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>). Means of three independent samples and standard errors are presented. The same letter above the columns of each gene indicates no significant difference at <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 5
<p>Analysis of lignin in two common vetch lines in response to salt stress. Lignin content in leaves (<b>a</b>) and roots (<b>b</b>) of salt-tolerant line (460) and salt-sensitive line (429) was measured after 3 d of 150 mM NaCl treatment. Histochemical analysis of lignin in root cross sections was conducted using phloroglucinol (<b>c</b>). Three independent experiments were carried out, and similar results were obtained (<b>c</b>). Means of three independent samples and standard errors are presented. The same letter above the columns of each gene indicates no significant difference at <span class="html-italic">p</span> &lt; 0.05. Scale bar in (<b>c</b>) = 50 µm.</p> Full article ">Figure 6
<p>Heatmap of the DEGs involved in starch and sucrose metabolism in two common vetch lines in response to salt stress. The DEGs involved in starch and sucrose metabolism in leaves (<b>a</b>) and roots (<b>b</b>) were presented. The color scale represents log₂-transformed FPKM (fragments per kilobyte per million reads) values. The gradual change in color indicates the different expression levels of DEGs. Red indicates up-regulation, and blue indicates down-regulation. SL, leaves in salt-sensitive line 429; TL, leaves in salt-tolerant line 460; SR, roots in salt-sensitive line 429; TR, roots salt-tolerant line 460; 0, 1, and 2 represent 0, 2 h, and 26 h after salt treatment, respectively.</p> Full article ">Figure 7
<p>Analysis of soluble sugars in two common vetch lines in response to salt stress. Sucrose (<b>a</b>,<b>b</b>), glucose (<b>c</b>,<b>d</b>), and fructose (<b>e</b>,<b>f</b>) in salt-tolerant line 460 and salt-sensitive line 429 were measured after 3 d of 150 mM NaCl treatment. The data from leaves are presented in (<b>a</b>,<b>c</b>,<b>e</b>), while those from roots are in (<b>b</b>,<b>d</b>,<b>f</b>). Means of three independent samples and standard errors are presented. The same letter above the columns of each gene indicates no significant difference at <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 8
<p>Heatmap of DEGs involved in Na⁺ and K⁺ transport and ROS scavenging in two common vetch lines in response to salt stress. The DEGs involved in Na⁺ and K⁺ transport in leaves (<b>a</b>) and roots (<b>b</b>) and ROS scavenging in leaves (<b>c</b>) and roots (<b>d</b>) are presented. The color scale represents log₂-transformed FPKM (fragments per kilobyte per million reads) values. The gradual change in color indicates the different expression levels of DEGs. Red indicates up-regulation, and blue indicates down-regulation. SL, leaves in salt-sensitive line 429; TL, leaves in salt-tolerant line 460; SR, roots in salt-sensitive line 429; TR, roots in salt-tolerant line 460; 0, 1, and 2 represent 0, 2 h, and 26 h after salt treatment, respectively.</p> Full article ">Figure 9
<p>Transcript levels of genes involved in ion homeostasis and ROS scavenging in two common vetch lines in response to salt stress. The transcription levels of <span class="html-italic">AKT2</span> (<b>a</b>,<b>b</b>), <span class="html-italic">HAK17</span> (<b>c</b>,<b>d</b>), <span class="html-italic">APX4</span> (<b>e</b>,<b>f</b>), <span class="html-italic">CAT1</span> (<b>g</b>,<b>h</b>), and <span class="html-italic">P5CS1</span> (<b>i</b>,<b>j</b>) were detected in salt-tolerant line (460) and salt-sensitive line (429) after 0, 2, and 26 h of NaCl treatment using qRT-PCR analysis. The data from leaves are presented in (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>,<b>i</b>), while those from roots are in (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>). Means of three independent samples and standard errors are presented. The same letter above the columns of each gene indicates no significant difference at <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">

13 pages, 1865 KiB  

Open AccessArticle

Isolation and Phenotypic Microarray Profiling of Different Pseudomonas Strains Isolated from the Rhizosphere of Curcuma longa L.

by Parul Pathak, Monika Singh, Ananya Naskar, Sandeep Kumar Singh, Nikunj Bhardwaj and Ajay Kumar

Stresses 2023, 3(4), 749-761; https://doi.org/10.3390/stresses3040051 - 13 Nov 2023

Viewed by 1470

Abstract

In the present study, different Pseudomonas strains were isolated from the rhizospheric soil of Curcuma longa (turmeric) and further identified and characterized based on morphological, biochemical, and molecular characteristics through the 16S rRNA gene sequencing analysis. The identified bacterial strains belong to the [...] Read more.

In the present study, different Pseudomonas strains were isolated from the rhizospheric soil of Curcuma longa (turmeric) and further identified and characterized based on morphological, biochemical, and molecular characteristics through the 16S rRNA gene sequencing analysis. The identified bacterial strains belong to the Pseudomonas genus viz. Pseudomonas sp. CL10, Pseudomonas sp. CL11, and P. fluorescence CLI4. However, the isolated strains tested positive for IAA production, siderophore production, and the solubilization of tricalcium phosphate during plant growth promoting traits analysis. Further phenotype microArray (PM) technology was used to evaluate the antibiotic and chemical sensitivity of the isolated bacterial strains. The antibiotics phleomycin, oxacillin, vancomycin, novobiocin, spiramycin, and rifampicin, as well as chemicals like, 5-7 dichloro-8-hydroxy quanaldine, 5-7 dichloro-8-hydroxyquinoline, domophenbrobide, and 3-5 dimethoxy benzyl alcohol, showed resistance in all the rhizobacterial strains. However, upon further detailed study, Pseudomonas sp. CL10 exhibited resistance to thirteen antibiotics, CL11 to fourteen, and CL14 showed resistance against seventeen antibiotics and chemical classes. The results of the study indicate that some of these strains can be used as bioinoculum to enhance the plant growth and health. Full article

(This article belongs to the Topic Plant Responses to Environmental Stress)

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Figure 1
<p>Colony morphology of isolated bacterial strains on agar plates and scanning electron micrographs of <span class="html-italic">Pseudomonas</span> sp., strains CL10, CL11, and CL14. The scale bar is 1 μm.</p> Full article ">Figure 2
<p>Phylogenetic tree from analysis of 16S rRNA gene sequence of the rhizospheric strains of <span class="html-italic">C. longa</span> L. GenBank accession numbers of nucleotide sequences are shown along with the name of bacterial strain. Phylogenetic analyses were conducted in MEGA 4.1.</p> Full article ">Figure 3
<p>Heat map dendrogram on the basis of antibiotic and chemical sensitivity of rhizobacterial isolates CL10, CL11, and CL 14 after 48 h of incubation (Mean value of four wells was taken to construct the heat map).</p> Full article ">Figure 4
<p>Antibiotic and chemical assay of rhizospheric bacterial strains: The figure showed complete and partial utilization of antibiotics and chemical on 12B and 15B PM plates of all the three rhizobacterial strains.</p> Full article ">

15 pages, 3826 KiB  

Open AccessReview

Response Mechanisms of Woody Plants to High-Temperature Stress

by Chao Zhou, Shengjiang Wu, Chaochan Li, Wenxuan Quan and Anping Wang

Plants 2023, 12(20), 3643; https://doi.org/10.3390/plants12203643 - 22 Oct 2023

Cited by 2 | Viewed by 2825

Abstract

High-temperature stress is the main environmental stress that restricts the growth and development of woody plants, and the growth and development of woody plants are affected by high-temperature stress. The influence of high temperature on woody plants varies with the degree and duration [...] Read more.

High-temperature stress is the main environmental stress that restricts the growth and development of woody plants, and the growth and development of woody plants are affected by high-temperature stress. The influence of high temperature on woody plants varies with the degree and duration of the high temperature and the species of woody plants. Woody plants have the mechanism of adapting to high temperature, and the mechanism for activating tolerance in woody plants mainly counteracts the biochemical and physiological changes induced by stress by regulating osmotic adjustment substances, antioxidant enzyme activities and transcription control factors. Under high-temperature stress, woody plants ability to perceive high-temperature stimuli and initiate the appropriate physiological, biochemical and genomic changes is the key to determining the survival of woody plants. The gene expression induced by high-temperature stress also greatly improves tolerance. Changes in the morphological structure, physiology, biochemistry and genomics of woody plants are usually used as indicators of high-temperature tolerance. In this paper, the effects of high-temperature stress on seed germination, plant morphology and anatomical structure characteristics, physiological and biochemical indicators, genomics and other aspects of woody plants are reviewed, which provides a reference for the study of the heat-tolerance mechanism of woody plants. Full article

(This article belongs to the Topic Plant Responses to Environmental Stress)

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Figure 1
<p>Effects of high-temperature stress on physiology and biochemistry of woody plants. Upward-pointing arrows indicate activated/upregulated physiological indices. Downward-pointing arrows indicate deactivated/downregulated physiological indices.</p> Full article ">Figure 2
<p>Effect of high-temperature stress on antioxidant enzyme activity of woody plants.</p> Full article ">Figure 3
<p>Effects of high-temperature stress on photosynthetic characteristics.</p> Full article ">

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14 pages, 16024 KiB  

Open AccessArticle

Influence of Surface Methane on Tropospheric Ozone Concentrations and Cereal Yield in Asia

by Kenichi Tatsumi

Agronomy 2023, 13(10), 2586; https://doi.org/10.3390/agronomy13102586 - 9 Oct 2023

Viewed by 1252

Abstract

Methane (CH₄) emanating from terrestrial sources serves as a precursor for the genesis of tropospheric ozone (O₃), a pernicious atmospheric contaminant that adversely modulates the physiological mechanisms of agricultural crops. Despite the acknowledged role of CH₄ in amplifying [...] Read more.

Methane (CH₄) emanating from terrestrial sources serves as a precursor for the genesis of tropospheric ozone (O₃), a pernicious atmospheric contaminant that adversely modulates the physiological mechanisms of agricultural crops. Despite the acknowledged role of CH₄ in amplifying O₃ concentrations, the extant literature offers limited quantitative evaluations concerning the repercussions of CH₄-mediated O₃ on cereal yields. Employing the GEOS-Chem atmospheric chemistry model, the present investigation elucidates the ramifications of a 50% diminution in anthropogenic CH₄ concentrations on the yield losses of maize, soybean, and wheat across Asia for the fiscal year 2010. The findings unveil pronounced yield detriments attributable to O₃-induced phytotoxicity, with the Indo-Gangetic Plain and the North China Plain manifesting the most substantial yield impairments among the crops examined. A halving of anthropogenic CH₄ effluents could ameliorate considerable losses in cereal production across these agriculturally pivotal regions. CH₄-facilitated O₃ exerts a pernicious influence on cereal yields; nevertheless, targeted mitigation of CH₄ effluents, particularly in the vicinity of the North China Plain, holds the potential to substantially attenuate O₃ contamination, thereby catalyzing an enhancement in regional cereal production. Full article

(This article belongs to the Topic Plant Responses to Environmental Stress)

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Figure 1

Figure 1
<p>Surface O₃ mixing ratio for 2010 at (<b>a</b>) January, (<b>b</b>) February, (<b>c</b>) March, (<b>d</b>) April, (<b>e</b>) May, (<b>f</b>) June, (<b>g</b>) July, (<b>h</b>) August, (<b>i</b>) September, (<b>j</b>) October, (<b>k</b>) November, and (<b>l</b>) December.</p> Full article ">Figure 1 Cont.
<p>Surface O₃ mixing ratio for 2010 at (<b>a</b>) January, (<b>b</b>) February, (<b>c</b>) March, (<b>d</b>) April, (<b>e</b>) May, (<b>f</b>) June, (<b>g</b>) July, (<b>h</b>) August, (<b>i</b>) September, (<b>j</b>) October, (<b>k</b>) November, and (<b>l</b>) December.</p> Full article ">Figure 2
<p>Spatial Distribution of surface-level AOT40 values for 2010 under BASE (left half) and HALF (right half) simulation. (<b>a</b>,<b>b</b>) Maize, (<b>c</b>,<b>d</b>) soybean, (<b>e</b>,<b>f</b>) spring wheat, (<b>g</b>,<b>h</b>) winter wheat.</p> Full article ">Figure 2 Cont.
<p>Spatial Distribution of surface-level AOT40 values for 2010 under BASE (left half) and HALF (right half) simulation. (<b>a</b>,<b>b</b>) Maize, (<b>c</b>,<b>d</b>) soybean, (<b>e</b>,<b>f</b>) spring wheat, (<b>g</b>,<b>h</b>) winter wheat.</p> Full article ">Figure 3
<p>Relative yield loss (RYL) for 2010 under BASE (<b>left</b>) and HALF (<b>right</b>) scenarios. (<b>a</b>,<b>b</b>) Maize, (<b>c</b>,<b>d</b>) soybean, (<b>e</b>,<b>f</b>) spring wheat, and (<b>g</b>,<b>h</b>) winter wheat.</p> Full article ">Figure 3 Cont.
<p>Relative yield loss (RYL) for 2010 under BASE (<b>left</b>) and HALF (<b>right</b>) scenarios. (<b>a</b>,<b>b</b>) Maize, (<b>c</b>,<b>d</b>) soybean, (<b>e</b>,<b>f</b>) spring wheat, and (<b>g</b>,<b>h</b>) winter wheat.</p> Full article ">Figure 4
<p>Crop production loss (CPL) for 2010 under BASE (<b>left</b>) and HALF (<b>right</b>) scenarios. (<b>a</b>,<b>b</b>) Maize, (<b>c</b>,<b>d</b>) soybean, (<b>e</b>,<b>f</b>) spring wheat, and (<b>g</b>,<b>h</b>) winter wheat.</p> Full article ">

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17 pages, 5337 KiB  

Open AccessArticle

Pipe Cavitation Parameters Reveal Bubble Embolism Dynamics in Maize Xylem Vessels across Water Potential Gradients

by Yangjie Ren, Yitong Zhang, Shiyang Guo, Ben Wang, Siqi Wang and Wei Gao

Agriculture 2023, 13(10), 1867; https://doi.org/10.3390/agriculture13101867 - 24 Sep 2023

Viewed by 1446

Abstract

Maize, a crop of international relevance, frequently undergoes xylem embolism due to water shortage, negatively impacting growth, yield, and quality. Consequently, a refined comprehension of xylem embolism is vital for enhancing maize cultivation. Notwithstanding extensive research and the generation of analytical models for [...] Read more.

Maize, a crop of international relevance, frequently undergoes xylem embolism due to water shortage, negatively impacting growth, yield, and quality. Consequently, a refined comprehension of xylem embolism is vital for enhancing maize cultivation. Notwithstanding extensive research and the generation of analytical models for embolism mechanisms, prevalent models often disregard crop-specific hydraulic processes and the formation of embolisms via air bubbles in the xylem conduit. In this research, we present an inventive model applying pipe cavitation parameters to discern water potential and bubble formation in maize leaf xylem. The model integrates pivotal physiological traits of the maize–leaf count, leaf vein count, and diameter of xylem vessels—demonstrating robust correlations. Furthermore, we constructed Percent Loss of Conductivity (PLC) curve based on water potential and compared it with our model, offering interval data to observe embolization events triggered by air bubbles. Utilizing experimental data, our novel cavitation-parameter-based model effectively corresponds with observed bubble phenomena and appropriately characterizes water transport in plant xylem conduits. This method enabled us to observe the transition from bubble occurrence to cavitation embolism microscopically, which aligned with the embolism intervals provided by the model. This procedure reveals potential trends in bubble-induced embolism and deepens our knowledge of microscopic plant hydraulics and crop embolism. This work establishes a basis for understanding the generation of bubble embolisms in maize, assists in evaluating maize-plant water status for efficient water supply management throughout the growth cycle, and contributes towards potential water management strategies for maize. Full article

(This article belongs to the Topic Plant Responses to Environmental Stress)

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Figure 1
<p>Bubbles within the xylem vessels of maize leaves. V denotes the flow rate of sap within the xylem, Ψ_w signifies the pressure of the surrounding water potential, and r is the radius of the resulting bubble. P_g represents the gas pressure inside the air pie, P_w indicates the water surface tension, and P₁ is the pressure of the xylem sap, separated by the phase boundary layer.</p> Full article ">Figure 2
<p>Investigative findings from trial maize plots in Mengrun Township. (<b>a</b>) Tally of leaf quantities in exemplar maize plots; (<b>b</b>) examination of the principal lateral venation in maize foliage; and (<b>c</b>) cross-section study of the vascular bundles within maize leaf veins.</p> Full article ">Figure 3
<p>Model demonstrating the interrelations between the bubble radius, sap flow rate, and water potential in vascular xylem conduits within maize leaves. (<b>a</b>) A three-dimensional depiction of the bubble radius, sap flow rate, and water potential under various simulated cavitation conditions. (<b>b</b>) The correlation between sap flow rate within the conduit and bubble radius under diverse cavitation parameters. (<b>c</b>) Depiction of varying sap-flow-rate intervals in relation to water potential under assorted cavitation states. (<b>d</b>) Demarcation of regions representing different cavitation states, based on bubble radius and water potential under distinct cavitation parameters.</p> Full article ">Figure 4
<p>(<b>a</b>) Correlation between the Percentage Loss of Conductivity (PLC) and the water potential within maize leaves. (<b>b</b>) Mapping of the relationship between the derived model for maize leaves and the corresponding PLC, utilizing specific parameters.</p> Full article ">Figure 5
<p>Comparison of water potential in maize leaves under physiological conditions (CG), before embolism (BE), and after embolism (AE).</p> Full article ">

13 pages, 908 KiB  

Open AccessReview

An Overview of the Impacts of Climate Change on Vineyard Ecosystems in Niagara, Canada

by Diana Ribeiro Tosato, Heather VanVolkenburg and Liette Vasseur

Agriculture 2023, 13(9), 1809; https://doi.org/10.3390/agriculture13091809 - 14 Sep 2023

Viewed by 2220

Abstract

Vineyards are agroecosystems of great importance in the Niagara Region, Ontario (Canada). Due to its microclimate, this region is projected to be impacted by climate change with temperature increases, changes in precipitation patterns in all seasons, and greater frequency of extreme weather events. [...] Read more.

Vineyards are agroecosystems of great importance in the Niagara Region, Ontario (Canada). Due to its microclimate, this region is projected to be impacted by climate change with temperature increases, changes in precipitation patterns in all seasons, and greater frequency of extreme weather events. The aim of this review paper is to summarize which seasonal changes are expected to occur in the Niagara Region and assess how such changes are likely to affect the main components of the vineyard ecosystem (i.e., soil, vines, invertebrates, and pathogens). It is expected that by 2080 the region will experience an increase in temperature in all four seasons; an increase in precipitation during the fall, winter, and spring; and a decrease in precipitation during summer months. Impacts of the projected changes will likely lead to vine water stress, yield loss, increases in incidents of diseases, increases in the spread of new pests, and changes in grape quality ultimately resulting in lower wine quality and/or production. Current management practices will need to be better understood and adaptive strategies introduced to enhance grape growers’ ability to minimize these impacts. Full article

(This article belongs to the Topic Plant Responses to Environmental Stress)

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Figure 1
<p>General conceptual model of the impacts of climate change in the weather in the Niagara region, Ontario, Canada. Each box presents the projected changes according to IPCC [<a href="#B1-agriculture-13-01809" class="html-bibr">1</a>]. The arrows inside the boxes represent the trend expected for those factors if increases or decreases occur.</p> Full article ">Figure 2
<p>Simplified diagram of the most important interactions within a vineyard. The boxes represent the vineyard components and the arrows the direction of effects between them. Arrows found within the boxes indicate expected component increases or decreases. The component of the soil influences the growth and quality of the vines. The vines are also impacted by the invertebrates, which can provide a positive relationship when they are beneficial or negative when they are pests, virus vectors, or pathogenic fungi. The interaction within the vineyard will results in changes in grape quality and quantity.</p> Full article ">

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18 pages, 3158 KiB  

Open AccessArticle

Porous Minerals Improve Wheat Shoot Growth and Grain Yield through Affecting Soil Properties and Microbial Community in Coastal Saline Land

by Lan Ma, Yanjing Song, Jie Wang, Yan Shan, Tingting Mao, Xiaoyan Liang, Haiyang Zhang, Rao Fu, Junlin Li, Wenjing Nie, Meng Li, Jiajia Li, Kuihua Yi, Lu Wang, Xiangyu Wang and Hongxia Zhang

Agronomy 2023, 13(9), 2380; https://doi.org/10.3390/agronomy13092380 - 13 Sep 2023

Cited by 1 | Viewed by 1360

Abstract

Soil salinization has become a major environmental factor severely threatening global food security. The application of porous minerals could significantly ameliorate soil fertility and promote plant productivity under salt stress conditions. However, the effects of porous minerals on improving the salt resistance of [...] Read more.

Soil salinization has become a major environmental factor severely threatening global food security. The application of porous minerals could significantly ameliorate soil fertility and promote plant productivity under salt stress conditions. However, the effects of porous minerals on improving the salt resistance of grain crops in coastal saline soils is not fully studied. In this work, the shoot growth and grain yield of wheat plants grown in coastal saline fields, respectively amended with the four naturally available porous minerals, diatomite, montmorillonite, bentonite and zeolite, were assessed. The application of porous minerals, especially zeolite, significantly improved the biomass and grain yield of wheat plants under saline conditions, as demonstrated by the augmented plant fresh mass (14.8~61.2%) and increased seed size (3.8~58.8%) and number (1.4~57.5%). Soil property analyses exhibited that porous-mineral amendment decreased soil sodium content and sodium absorption ratio, and increased soil nutrients in both the rhizosphere and nonrhizosphere of wheat plants. Further quantitative-PCR and 16S high-throughput sequencing analysis revealed that porous-mineral application also remarkably increased the abundance of bacterial 16S rRNA (0.8~102.4%) and fungal 18S rRNA (89.2~209.6%), and altered the composition of the soil microbial community in the rhizosphere of wheat. Our findings suggest that zeolite could be used as an ideal salt soil amendment, and the changes in soil properties and microorganisms caused by the application of porous minerals like zeolite improved the salt resistance of wheat plants in coastal saline land, leading to increased shoot growth and seed production. Full article

(This article belongs to the Topic Plant Responses to Environmental Stress)

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Figure 1
<p>Porous minerals promoted vegetative growth of wheat. Plant height and fresh mass of wheat plants grown in control (CK) and different porous-mineral-treated plots at stem elongation stage were compared. (<b>A</b>) An overview photo showing the growth states of wheat plants in the experimental field. (<b>B</b>) Phenotypes of representative wheat plants. (<b>C</b>) Plant height. (<b>D</b>) Plant fresh mass. Values were means and standard deviations of three replicates (<span class="html-italic">n</span> = 3). Different lowercase letters denote significant difference among different porous-mineral treatments (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 2
<p>Porous minerals improved wheat biomass and grain yield at the maturity stage. (<b>A</b>) Phenotypes of representative wheat plants at maturity grown in control (CK) and different porous-mineral-treated plots. (<b>B</b>) Plant height. (<b>C</b>) Plant fresh mass. (<b>D</b>) Ear and seed sizes. (<b>E</b>–<b>I</b>) Ear lengths, ear weights, ear grain weights, ear grain numbers and 100-grain weights. Values were means and standard deviations of three replicates (<span class="html-italic">n</span> = 3). Different lowercase letters denote significant difference among different porous-mineral treatments (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 3
<p>Porous minerals influenced soil properties. (<b>A</b>) Soil pH values. (<b>B</b>) Electrical conductivity (EC). (<b>C</b>–<b>F</b>) K⁺, Na⁺, Ca²⁺ and Mg²⁺ contents. Values were means and standard deviations of three replicates (<span class="html-italic">n</span> = 3). Different lowercase letters denote significant difference among different porous-mineral treatments (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 4
<p>Porous minerals influenced soil property. (<b>A</b>) K⁺/Na⁺ ratio. (<b>B</b>) Sodium adsorption ratio (SAR). Values were means and standard deviations of three replicates (<span class="html-italic">n</span> = 3). Different lowercase letters denote significant difference among different porous-mineral treatments (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 5
<p>Inorganic N and soluble P content analysis in the rhizosphere and nonrhizosphere soil of wheat plants grown in control (CK) and different porous-mineral-amended plots were examined. (<b>A</b>) Inorganic N contents. (<b>B</b>) Soluble P contents. Values were means and standard deviations of three replicates (<span class="html-italic">n</span> = 3). Different lowercase letters denote significant difference among different porous-mineral treatments (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 6
<p>Soil microbial abundance analysis in the rhizosphere and nonrhizosphere of wheat plants grown in control (CK) and different porous-mineral-amended plots. (<b>A</b>) 16S rRNA gene copy numbers. (<b>B</b>) 18S rRNA gene copy numbers. Values are means and standard deviations of three replicates (<span class="html-italic">n</span> = 3). Different lowercase letters denote significant difference among different porous-mineral treatments (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 7
<p>Soil microbial community analysis in the rhizosphere and nonrhizosphere of wheat plants grown in control (CK) and different porous-mineral-amended plots. (<b>A</b>,<b>B</b>) Hierarchical clustering diagrams based on Bray–Curtis similarity at phylum levels. (<b>C</b>,<b>D</b>) Heat maps of the top 30 bacteria at genus levels. RS, rhizosphere soil; NRS, nonrhizosphere soil.</p> Full article ">

14 pages, 3263 KiB  

Open AccessEditor’s ChoiceArticle

Growth and Photosynthetic Responses to Increased LED Light Intensity in Korean Ginseng (Panax ginseng C.A. Meyer) Sprouts

by Jinnan Song, Jingli Yang and Byoung Ryong Jeong

Agronomy 2023, 13(9), 2375; https://doi.org/10.3390/agronomy13092375 - 13 Sep 2023

Cited by 3 | Viewed by 1458

Abstract

Compared to the traditional production of ginseng roots, Panax ginseng sprouts (PGSs) are currently regarded as a substitute due to the relatively short-term culture but still high nutrition. However, the optimal light intensity for the growth ability of PGSs and the characterizations of [...] Read more.

Compared to the traditional production of ginseng roots, Panax ginseng sprouts (PGSs) are currently regarded as a substitute due to the relatively short-term culture but still high nutrition. However, the optimal light intensity for the growth ability of PGSs and the characterizations of the responses of PGSs to the light intensity have been largely neglected. This study aimed to determine the influences of the light intensity on the growth, morphogenesis, and photosynthetic responses in PGSs. To this end, two-year-old ginseng rootlets were subjected to one of six light intensities (from 30 to 280 PPFD with 50 PPFD intervals) in a plant factory with artificial lighting (PFAL) via LED light for 10 weeks. On the whole, the recorded parameters of the PGSs showed gradually decreasing trends in response to the increasing light intensities. However, the 80 PPFD-treated PGSs possessed similar or greater root dry weights, leaf areas, carotenoids levels, and photosynthesis (the maximal PSII quantum yield) compared to those in the 30 PPFD regime. Additionally, photoinhibition symptoms as evidenced by chlorosis, necrosis, and stunted growth were observed as the light intensity attained 180 PPFD. Thus, 130 PPFD could be considered a safe point for the appearance of photoinhibition in PGSs. Taken together, we show that the light intensity range of 30–80 PPFD is suitable for maximizing the production of PGSs in PFALs. Full article

(This article belongs to the Topic Plant Responses to Environmental Stress)

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Figure 1
<p>The PGSs were cultivated under 6 different light intensity regimes.</p> Full article ">Figure 2
<p>The influence of light intensity on ginseng growth and morphology.</p> Full article ">Figure 3
<p>Effects of light intensity on ginseng (<b>A</b>) whole fresh weight and whole dry weight and (<b>B</b>) root fresh weight and root dry weight. Values are means ± SDs of n = 5 biological replicates. Significant differences for identical parameters among the light intensities are denoted by different letters, following Duncan’s multiple range test at <span class="html-italic">p</span> = 0.05 (one-way ANOVA). The significant interactions between the light intensity and the investigated parameters were determined via <span class="html-italic">F</span>-test and are indicated by different numbers of asterisks.</p> Full article ">Figure 4
<p>Effects of light intensity on ginseng (<b>A</b>) shoot length and stem length and (<b>B</b>) stem diameter. Values are means ± SDs of n = 5 biological replicates. Significant differences for identical parameters among different light intensities are denoted by different letters, following Duncan’s multiple range test at <span class="html-italic">p</span> = 0.05 (one-way ANOVA). The significant interactions between the light intensity and the investigated parameters were determined via <span class="html-italic">F</span>-test and are indicated by different numbers of asterisks (**, <span class="html-italic">p</span> &lt; 0.01; and ***, <span class="html-italic">p</span> &lt; 0.001).</p> Full article ">Figure 5
<p>The effects of light intensity on ginseng (<b>A</b>) leaf morphology and status, (<b>B</b>) leaf length and width, (<b>C</b>) petiole length, and (<b>D</b>) leaf area. The presented data are expressed as means ± SDs (n = 5 replicates). Different letters indicate significant differences according to Duncan’s multiple range test at <span class="html-italic">p</span> = 0.05 (one-way ANOVA). The significant interactions between the light intensity and the investigated leaf parameters were determined via <span class="html-italic">F</span>-test and are denoted by different numbers of asterisks (**, <span class="html-italic">p</span> &lt; 0.01; and ***, <span class="html-italic">p</span> &lt; 0.001).</p> Full article ">Figure 6
<p>The effects of light intensity on ginseng (<b>A</b>) chlorophyll contents, (<b>B</b>) carotenoids contents, and (<b>C</b>) the maximal quantum yield of photosystem II (Fv/Fm). The displayed values are expressed as means ± SDs (n = 5 independent replicates). Different lowercase letters indicate significant differences according to Duncan’s multiple range test at <span class="html-italic">p</span> = 0.05 (one-way ANOVA). The significant interactions between the light intensity and the investigated photosynthetic characteristics were determined via <span class="html-italic">F</span>-test and are denoted by different numbers of asterisks (**, <span class="html-italic">p</span> &lt; 0.01; and ***, <span class="html-italic">p</span> &lt; 0.001).</p> Full article ">Figure 7
<p>Multivariate data analysis using (<b>A</b>) PCA and (<b>B</b>) a heatmap of Pearson’s correlation matrix among the investigated growth and photosynthetic characteristics. Cyan color and brown color indicate low R values and high R values, respectively. Abbreviations: SHL: shoot length; STL: stem length; SD: shoot diameter; FW: whole fresh weight; DW: whole dry weight; RFW: root fresh weight; RDW: root dry weight; PL: petiole length; LL: leaf length; LW: leaf width; LA: leaf area; Cha: chlorophyll a; Chb: chlorophyll b; Ca: carotenoids; QY: the maximal PSII quantum yield (Fv/Fm).</p> Full article ">

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15 pages, 1956 KiB  

Open AccessArticle

De Novo Transcriptome Analysis of Solanum lycopersicum cv. Super Strain B under Drought Stress

by Hassan S. Al-Zahrani, Tarek A. A. Moussa, Hameed Alsamadany, Rehab M. Hafez and Michael P. Fuller

Agronomy 2023, 13(9), 2360; https://doi.org/10.3390/agronomy13092360 - 11 Sep 2023

Viewed by 1143

Abstract

Tomato cv. super strain B was widely cultivated in Saudi Arabia under drought stress. Illumina Hiseq-2000 was used to create the transcriptional profile of tomato cultivar super strain B. A total of 98,069 contigs were gathered, with an average length of 766 bp. [...] Read more.

Tomato cv. super strain B was widely cultivated in Saudi Arabia under drought stress. Illumina Hiseq-2000 was used to create the transcriptional profile of tomato cultivar super strain B. A total of 98,069 contigs were gathered, with an average length of 766 bp. Most of the genes in the gene ontology (GO) analysis were categorized into molecular function (MF) of ATP binding (1301 genes), metal ion binding (456 genes), protein kinase activity (392 genes), transferase activity (299 genes), Biological process (BP) of DNA-templated genes (366 genes), and regulation of transcription genes (209 genes), while cellular components (CC) of integral component of membrane (436 genes). The most dominant enzymes expressed were transferases (645 sequences). According to the KEGG pathway database, 15,638 transcripts were interpreted in 125 exclusive pathways. The major pathway groups were metabolic pathways (map01100, 315 genes) and biosynthesis of secondary metabolites (map01110, 188 genes). The total number of variants in the twelve chromosomes of super strain B compared with the tomato genome was 5284. The total number of potential SSRs was 5047 in 4806 unigenes. Trinucleotide repeats (3006, 59.5%) were the most found type in the transcriptome. A total of 4541 SNPs and 744 INDELs in tomato super strain B were identified when compared with the tomato genome. Full article

(This article belongs to the Topic Plant Responses to Environmental Stress)

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<p>Length distribution of the most expressed transcripts in cv. Super strain B (<span class="html-italic">S. lycoperscium</span>) transcriptome under drought stress.</p> Full article ">Figure 2
<p>Gene ontology classification showing the number of transcripts for biological processes, cellular components, and molecules expressed in cv. Super strain B under drought stress.</p> Full article ">Figure 3
<p>Number of unigenes for each enzyme commission (EC) category expressed in cv. Super strain B under drought stress.</p> Full article ">Figure 4
<p>Graphical representation of cv. super strain B unigene positions on the chromosomes of tomato and nucleotide-level variants found on the reference tomato chromosomes.</p> Full article ">Figure 5
<p>Phylogenetic relationship among Solanaceae species.</p> Full article ">

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