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17 pages, 9918 KiB  
Article
Aspirin Foliar Spray-Induced Changes in Light Energy Use Efficiency, Chloroplast Ultrastructure, and ROS Generation in Tomato
by Julietta Moustaka, Ilektra Sperdouli, Emmanuel Panteris, Ioannis-Dimosthenis S. Adamakis and Michael Moustakas
Int. J. Mol. Sci. 2025, 26(3), 1368; https://doi.org/10.3390/ijms26031368 - 6 Feb 2025
Abstract
Aspirin (Asp) is extensively used in human health as an anti-inflammatory, antipyretic, and anti-thrombotic drug. In this study, we investigated if the foliar application of Asp on tomato plants has comparable beneficial effects on photosynthetic function to that of salicylic acid (SA), with [...] Read more.
Aspirin (Asp) is extensively used in human health as an anti-inflammatory, antipyretic, and anti-thrombotic drug. In this study, we investigated if the foliar application of Asp on tomato plants has comparable beneficial effects on photosynthetic function to that of salicylic acid (SA), with which it shares similar physiological characteristics. We assessed the consequences of foliar Asp-spray on the photosystem II (PSII) efficiency of tomato plants, and we estimated the reactive oxygen species (ROS) generation and the chloroplast ultrastructural changes. Asp acted as an osmoregulator by increasing tomato leaf water content and offering antioxidant protection. This protection kept the redox state of plastoquinone (PQ) pull (qp) more oxidized, increasing the fraction of open PSII reaction centers and enhancing PSII photochemistry (ΦPSII). In addition, Asp foliar spray decreased reactive oxygen species (ROS) formation, decreasing the excess excitation energy on PSII. This resulted in a lower singlet oxygen (1O2) generation and a lower quantum yield for heat dissipation (ΦNPQ), indicating the photoprotective effect provided by Asp, especially under excess light illumination. Simultaneously, we observed a decrease in stomatal opening by Asp, which reduced the transpiration. Chloroplast ultrastructural data revealed that Asp, by offering a photoprotective effect, decreased the need for the photorespiration process, which reduces photosynthetic performance. It is concluded that Asp shares similar physiological characteristics with SA, having an equivalent beneficial impact to SA by acting as a biostimulant of the photosynthetic function for an enhanced crop yield. Full article
(This article belongs to the Special Issue Molecular Mechanisms of Plant Abiotic Stress Tolerance)
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Figure 1

Figure 1
<p>The chlorophyll content of water (WA)-sprayed and Aspirin (Asp)-sprayed leaves 24- and 96-h after the spray, expressed in relative units (<span class="html-italic">n</span> = 10 ± SD). Significant differences are shown by different lower-case letters (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The effective quantum yield of PSII photochemistry (Φ<span class="html-italic"><sub>PSII</sub></span>) at the growth light intensity (GLI) (<b>a</b>) and at the high light intensity (HLI) (<b>b</b>) of WA-sprayed and Asp-sprayed leaves 24- and 96-h after the spray (<span class="html-italic">n</span> = 6 ± SD). Significant differences are shown by different lower-case letters (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The quantum yield of regulated non-photochemical energy loss in PSII (Φ<span class="html-italic"><sub>NPQ</sub></span>) at the GLI (<b>a</b>) and at the HLI (<b>b</b>) of WA-sprayed and Asp-sprayed leaves 24- and 96-h after the spray (<span class="html-italic">n</span> = 6 ± SD). Significant differences are shown by different lower-case letters (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The quantum yield of non-regulated energy loss in PSII (Φ<span class="html-italic"><sub>NO</sub></span>) at the GLI (<b>a</b>) and at the HLI (<b>b</b>) of WA-sprayed and Asp-sprayed leaves 24- and 96-h after the spray (<span class="html-italic">n</span> = 6 ± SD). Significant differences are shown by different lower-case letters (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The non-photochemical quenching (NPQ), at the GLI (<b>a</b>) and at the HLI (<b>b</b>) of WA-sprayed and Asp-sprayed leaves 24- and 96-h after the spray (<span class="html-italic">n</span> = 6 ± SD). Significant differences are shown by different lower-case letters (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The fraction of open PSII reaction centers (RCs) (q<span class="html-italic">p</span>), at the GLI (<b>a</b>) and at the HLI (<b>b</b>) of WA-sprayed and Asp-sprayed leaves 24- and 96-h after the spray (<span class="html-italic">n</span> = 6 ± SD). Significant differences are shown by different lower-case letters (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 7
<p>The efficiency of the open PSII RCs (F<span class="html-italic">v</span>’/F<span class="html-italic">m</span>’) at the GLI (<b>a</b>) and at the HLI (<b>b</b>) of WA-sprayed and Asp-sprayed leaves 24- and 96-h after the spray (<span class="html-italic">n</span> = 6 ± SD). Significant differences are shown by different lower-case letters (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The electron transport rate (ETR) at the GLI (<b>a</b>) and at the HLI (<b>b</b>) of WA-sprayed and Asp-sprayed leaves 24- and 96-h after the spray (<span class="html-italic">n</span> = 6 ± SD). Significant differences are shown by different lower-case letters (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The excitation pressure at PSII (1 − q<span class="html-italic">L</span>), measured at the GLI (<b>a</b>) and at the HLI (<b>b</b>) of WA-sprayed and Asp-sprayed leaves 24- and 96-h after the spray (<span class="html-italic">n</span> = 6 ± SD). Significant differences are shown by different lower-case letters (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 10
<p>The excess excitation energy at PSII (EXC), at the GLI (<b>a</b>) and at the HLI (<b>b</b>) of WA- sprayed and Asp-sprayed leaves 24- and 96-h after the spray (<span class="html-italic">n</span> = 6 ± SD). Significant differences are shown by different lower-case letters (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 11
<p>The relationship between the excess excitation energy (EXC) and the excitation pressure at PSII (1 − q<span class="html-italic">L</span>) at the GLI (<b>a</b>) and at the HLI (<b>b</b>) of WA- sprayed and Asp-sprayed leaves 24- and 96-h after the spray (based on the data of <a href="#ijms-26-01368-f009" class="html-fig">Figure 9</a>a,b and <a href="#ijms-26-01368-f010" class="html-fig">Figure 10</a>a,b). Each blue dot represents the paired measurement of the variables, while the red line is the regression line that shows the relationship between the two variables.</p>
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<p>ROS production in tomato leaflets of WA-sprayed (<b>a</b>) and Asp-sprayed <b>(b</b>) leaves 24-h after the spray. The light green color indicates ROS generation. Scale bar, 200 μm.</p>
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<p>Transmission electron micrographs of mesophyll cells of tomato leaves sprayed with water (<b>a</b>,<b>b</b>) or with Asp (<b>c</b>,<b>d</b>). Note the peroxisomes (arrows in (<b>a</b>)), which include electron-dense crystals (asterisk in (<b>b</b>)) in cells of WA-sprayed leaves. Starch grains (sg) can be observed in chloroplasts of Asp-sprayed leaves (<b>c</b>,<b>d</b>) but not in those of WA-sprayed leaves. m: mitochondrion. Scale bars as indicated on the micrographs.</p>
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19 pages, 1229 KiB  
Review
Photorespiratory Metabolism and Its Regulatory Links to Plant Defence Against Pathogens
by Iwona Ciereszko and Elżbieta Kuźniak
Int. J. Mol. Sci. 2024, 25(22), 12134; https://doi.org/10.3390/ijms252212134 - 12 Nov 2024
Viewed by 941
Abstract
When plants face biotic stress, the induction of defence responses imposes a massive demand for carbon and energy resources, which could decrease the reserves allocated towards growth. These growth–defence trade-offs have important implications for plant fitness and productivity and influence the outcome of [...] Read more.
When plants face biotic stress, the induction of defence responses imposes a massive demand for carbon and energy resources, which could decrease the reserves allocated towards growth. These growth–defence trade-offs have important implications for plant fitness and productivity and influence the outcome of plant–pathogen interactions. Biotic stress strongly affects plant cells’ primary metabolism, including photosynthesis and respiration, the main source of energy and carbon skeletons for plant growth, development, and defence. Although the nature of photosynthetic limitations imposed by pathogens is variable, infection often increases photorespiratory pressure, generating conditions that promote ribulose-1,5-bisphosphate oxygenation, leading to a metabolic shift from assimilation to photorespiration. Photorespiration, the significant metabolic flux following photosynthesis, protects the photosynthetic apparatus from photoinhibition. However, recent studies reveal that its role is far beyond photoprotection. The intermediates of the photorespiratory cycle regulate photosynthesis, and photorespiration interacts with the metabolic pathways of nitrogen and sulphur, shaping the primary metabolism for stress responses. This work aims to present recent insights into the integration of photorespiration within the network of primary metabolism under biotic stress. It also explores the potential implications of regulating photosynthetic–photorespiratory metabolism for plant defence against bacterial and fungal pathogens. Full article
(This article belongs to the Special Issue Plant Respiration in the Light and Photorespiration)
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Figure 1

Figure 1
<p>Photorespiratory pathway overview (simplified scheme). The photorespiration cycle occurs in the cell’s three main compartments: chloroplasts, peroxisomes and mitochondria; these organelles are often located close to each other, which allows for the efficient exchange of metabolites during the cycle. The photorespiration cycle is also called C2, oxidative photosynthetic carbon pathway. Gas exchange associated with photorespiration is the uptake of oxygen and the release of CO<sub>2</sub> (in light) and NH<sub>3</sub>. The first reaction that starts photorespiration occurs in the chloroplast: the oxygenation of the ribulose-1,5-bisphosphate (RuBP) catalysed by the enzyme RuBP carboxylase/oxygenase (Rubisco); products of this reaction are 3-phosphoglycerate and 2-phosphoglycolate. The enzyme Rubisco also catalyzes the assimilation of carbon dioxide (to which it has a greater affinity) to RuBP, initiating the Calvin–Benson cycle reaction of photosynthesis in the chloroplast stroma. Carbon dioxide or oxygen, substrates for Rubisco, enter the leaves through open stomata and diffuse into the chloroplasts. The oxygenation product, 2-phosphoglycolate, is dephosphorylated by phosphatase (PGP) into glycolate, which is transported to the following organelle, the peroxisome (chloroplastic glycolate/glycerate translocator in the inner membrane is known to be involved). Glycolate is oxidised to glyoxylate and H<sub>2</sub>O<sub>2</sub> thanks to glycolate oxidase (GOX) activity (or glycolate dehydrogenase–in green algae). Glyoxylate is then converted to glycine in the peroxisomes by aminotransferases: glutamate: glyoxylate aminotransferase (GGAT) and serine: glyoxylate aminotransferase (SGAT). Glycine is then transported to the mitochondrion, where two molecules are converted to serine in reactions catalysed by the multienzyme glycine decarboxylase (GDC, a complex that consists of P-protein, T-protein and H-, L-proteins), and serine hydroxymethyltransferase (SHMT); CO<sub>2</sub> and NH<sub>3</sub> are released in those reactions. Serine is exported to peroxisome, where the action of aminotransferase SGAT converts it into hydroxypyruvate, and next, the enzyme hydroxypyruvate reductase (HPR) catalyses the reduction of hydroxypyruvate to glycerate. Glycerate is transported to the chloroplast, where the phosphorylation reaction is catalysed by glycerate-3-kinase (GLYK), and 3-phosphoglycerate is produced, ready to be involved in the Calvin–Benson cycle. Abbreviations: CAT—enzyme catalase; GDC—glycine decarboxylase; GLYK—glycerate-3-kinase; GOX—glycolate oxidase; HPR—3-hydroxy pyruvate reductase; PGP—2-phosphoglycolate phosphatase; Rubisco—ribulose-1,5-bisphosphate carboxylase/oxygenase; RuBP—ribulose-1,5-bisphosphate; SHMT—serine hydroxy-methyltransferase (enzyme that catalyses the reversible interconversion of serine and glycine).</p>
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<p>Schematic representation of events associated with photorespiratory organelles during plant–pathogen interactions. Plants perceive pathogen-expressed PAMPs (e.g., flg22) or endogenous elicitors (DAMPs) via extracellular receptors (PRR) and initiate PTI. After PAMP/DAMP perception, a rise in cytosolic Ca<sup>2+</sup> levels and extracellular ROS production by NADPH-oxidase homolog RBOHD and peroxidases occur. Calcium (Ca<sup>2+</sup>) is involved in regulating apoplast ROS production and intracellular stress signalling. ROS can directly inhibit pathogen growth and influence redox homeostasis in cellular compartments and pathogen-response signal transduction pathways. Pathogen sensing and ROS accumulation in the apoplast lead to stomatal closure, restricting pathogen entry. Stomatal closure triggers increased photorespiratory activity and modulates the status of defence phytohormones such as SA, JA and ABA. Pathogens deliver effectors sensed by intracellular NLR, leading to ETI exemplified by HR. Effectors can suppress PTI and facilitate virulence, e.g., COR promotes stomata reopening by manipulating stress signalling. Pathogen effectors also target chloroplasts, peroxisomes and mitochondria, triggering different molecular mechanisms to inhibit plant immunity. Enhanced photorespiration supports plant immunity. Photorespiratory H<sub>2</sub>O<sub>2</sub> and redox regulators, metabolites (glycine, serine) and enzymes (GOX, GDC), along with NO and Ca<sup>2+</sup>, may help resist pathogens by limiting microbial growth and upregulating defence gene expression. During photorespiration, the organelle-organelle interactions between chloroplasts, peroxisomes and mitochondria and between them and other cellular compartments (e.g., nucleus, plasma membrane and endoplasmic reticulum) are facilitated by organelle protrusions (stromules, peroxules and matrixules) and membrane contact sites. This enables the exchange of immune signals during ETI. The retrograde/anterograde signalling positions chloroplasts and mitochondria as essential hubs in sensing and integrating various stress signals and regulating metabolism during the response to biotic stress. Abbreviations: ABA—abscisic acid; CO—coronatine; DAMPs—damage-associated molecular patterns; ETI—effector-triggered immunity; GDC—glycine decarboxylase; GOX—glycolate oxidase; HR—hypersensitive response; JA—jasmonic acid; MAPK—mitogen-activated protein kinase; NLRs—nucleotide-binding leucine-rich repeat receptors; NO–nitric oxide; PAMPs—pathogen-associated molecular patterns; PRRs—pattern recognition receptors; PTI—pattern-triggered immunity; RBOHD—respiratory burst oxidase homolog protein D; ROS—reactive oxygen species; SA—salicylic acid.</p>
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37 pages, 3352 KiB  
Review
Photosynthetic Electron Flows and Networks of Metabolite Trafficking to Sustain Metabolism in Photosynthetic Systems
by Neda Fakhimi and Arthur R. Grossman
Plants 2024, 13(21), 3015; https://doi.org/10.3390/plants13213015 - 28 Oct 2024
Viewed by 1317
Abstract
Photosynthetic eukaryotes have metabolic pathways that occur in distinct subcellular compartments. However, because metabolites synthesized in one compartment, including fixed carbon compounds and reductant generated by photosynthetic electron flows, may be integral to processes in other compartments, the cells must efficiently move metabolites [...] Read more.
Photosynthetic eukaryotes have metabolic pathways that occur in distinct subcellular compartments. However, because metabolites synthesized in one compartment, including fixed carbon compounds and reductant generated by photosynthetic electron flows, may be integral to processes in other compartments, the cells must efficiently move metabolites among the different compartments. This review examines the various photosynthetic electron flows used to generate ATP and fixed carbon and the trafficking of metabolites in the green alga Chlamydomomas reinhardtii; information on other algae and plants is provided to add depth and nuance to the discussion. We emphasized the trafficking of metabolites across the envelope membranes of the two energy powerhouse organelles of the cell, the chloroplast and mitochondrion, the nature and roles of the major mobile metabolites that move among these compartments, and the specific or presumed transporters involved in that trafficking. These transporters include sugar-phosphate (sugar-P)/inorganic phosphate (Pi) transporters and dicarboxylate transporters, although, in many cases, we know little about the substrate specificities of these transporters, how their activities are regulated/coordinated, compensatory responses among transporters when specific transporters are compromised, associations between transporters and other cellular proteins, and the possibilities for forming specific ‘megacomplexes’ involving interactions between enzymes of central metabolism with specific transport proteins. Finally, we discuss metabolite trafficking associated with specific biological processes that occur under various environmental conditions to help to maintain the cell’s fitness. These processes include C4 metabolism in plants and the carbon concentrating mechanism, photorespiration, and fermentation metabolism in algae. Full article
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Figure 1
<p>Photosynthetic and respiratory electron transport and their interactions. Linear photosynthetic electron transport (bottom) involves excitation of reaction centers, photosystem I and II, (PSI, PSII) and extraction of electrons from H<sub>2</sub>O by the PSII O<sub>2</sub> evolving complex. Extracted electrons pass through PSII reaction centers, the plastoquinone (PQ) pool, Cytochrome b<sub>6</sub>f (Cytb<sub>6</sub>f), plastocyanin (PC) and to PSI where they are used to generate reduced ferredoxin (FDX) and NADPH through the activity of Ferredoxin NADP<sup>+</sup> reductase (FNR); the NADPH and the ATP synthesized by the ATP synthase (fueled by the proton gradient across thylakoid membranes) and the NADPH are used to reduce CO<sub>2</sub> and drive metabolic processes in the cells. Reducing electrons generated on the acceptor side of PSI or PSII can be routed through AEF pathways: Cyclic electron flow occurs through both PGR5/PGRL1, and the NDA2 pathways. In the Mehler reaction, PSI-derived electrons are used to reduce O<sub>2</sub>, and the ROS generated (O<sub>2</sub><sup>−</sup> and H<sub>2</sub>O<sub>2</sub>) can be converted to H<sub>2</sub>O through superoxide dismutase and catalase/ascorbate peroxidase. In pseudocyclic electron flow (PCEF), electrons from PSI/FDX are transferred to the diiron proteins (FLV) to reduce O<sub>2</sub> to H<sub>2</sub>O. The plastoquinol terminal oxidase (PTOX) catalyzes the reduction of O<sub>2</sub> on the acceptor side of PSII. In chloroplast-to-mitochondria electron flow (CMEF), electrons are exported from the chloroplast to the mitochondrion through the function of OAA (or 2-oxoglutarate)/malate redox shuttles on both the chloroplast and mitochondria envelopes; reductant is shuttled between the compartments through the interconversion of malate/NAD(P)<sup>+</sup> to OAA/NAD(P)H. ***, Transporting triose-P out of chloroplasts is another potential avenue for delivering reductant to mitochondria. However, since there is still little reported evidence for that route of delivery, the extent to which it provides reductant to power mitochondrial respiration is uncertain. The electrons released from these reductants are used to drive oxidative phosphorylation in mitochondria (top) through either cytochrome oxidase (complex IV) or alternative oxidases (AOXs), generating additional ATP. ICEM, inner chloroplast envelope membrane; OCEM, outer chloroplast envelope membrane; IMM, inner mitochondrion membrane; OMM, outer mitochondrion membrane. Representation of the photosynthetic electron transport chain was modified from Grossman in [<a href="#B18-plants-13-03015" class="html-bibr">18</a>]. Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p>
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<p>Overview of characterized and uncharacterized major fixed carbon and reductant transporters on inner chloroplast and mitochondrial envelope membranes. (<b>A</b>) Transporters on the chloroplast envelope in <span class="html-italic">Chlamydomonas</span> are divided into two major groups: sugar-P/phosphate transporters, including GPTs, TPTs, and PPTs, which transport both fixed carbon and reductants, and dicarboxylate transporters, which mainly shuttle reductants. Based on Doebbe et al. [<a href="#B19-plants-13-03015" class="html-bibr">19</a>], there might also be a hexose transporter on the chloroplast envelope, although the specificity and the mechanism of transport have not been elucidated. While the substrates transported are indicated, many of these bidirectional sugar-P transporters can use multiple substrates (and counter-substrate); Pi is considered a preferred counter-substrate. (<b>B</b>) Transporters on the mitochondrial inner membrane of plants, with analogous proteins in <span class="html-italic">Chlamydomonas</span>), include di- and tricarboxylate transporters. Most of the metabolites translocated across the mitochondria membrane are associated with the TCA cycle. Reductant imported into the mitochondria can drive oxidative phosphorylation. CCPs (two in <span class="html-italic">Chlamydomonas</span>) are mitochondrial transporters that increase in abundance (transcript level) under low CO<sub>2</sub> conditions, but their functions are yet to be determined. Dark gray indicates that some transporters from this category have been characterized in <span class="html-italic">Chlamydomonas</span>, while light gray indicates that the transporter has not yet been characterized; the presence of these transporters on the mitochondrial envelope of <span class="html-italic">Chlamydomonas</span> and their contribution to its metabolism are predicted from information derived from plant system. DIC, dicarboxylate translocator on the mitochondrial envelope; DiT, dicarboxylate translocator on the chloroplast envelope; DTC, tricarboxylate translocator; GPT, glucose 6-phosphate/phosphate transporter; HK, hexokinase; HXT, hexose transporter; MDH, malate dehydrogenase; PPT, phosphoenol pyruvate/phosphate transporter; TPT, triose-phosphate/phosphate transporter; DOXP/MEP, 1-deoxyxylulose 5-phosphate/2-C-methylerythritol 4-phosphate pathway; TCA, tricarboxylic acid cycle; AA, amino acid; ATP, adenosine triphosphate; Asp, aspartate; CO<sub>2</sub>, carbon dioxide; DHAP, dihydroxyacetone phosphate; E4P, D-erythrose 4-phosphate; Glc, glucose; Glc1P, glucose 1-phosphate; Glc6P, glucose 6-phosphate; G3P, glyceraldehyde-3-phosphate; Glu, glutamate; 3-PGA, 3-phosphoglyceric acid; NAD(P)(H), nicotinamide adenine dinucleotide (phosphate); OAA, oxaloacetate; 2-OG, 2-oxoglutarate; PEP, phosphoenol pyruvate; Pi, inorganic phosphate; RuBP, ribulose 1,5-bisphosphate; Ru5P, ribose-5-phosphate; ICEM, inner chloroplast envelope membrane; OCEM, outer chloroplast envelope membrane; IMM, inner mitochondrion membrane; OMM, outer mitochondrion membrane. Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p>
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<p>Metabolite transport required for various metabolic processes in the cell. (<b>A</b>) CO<sub>2</sub>-concentrating mechanism in a NADP-malic enzyme type C4 plant, such as sugarcane [adapted with some modifications from [<a href="#B156-plants-13-03015" class="html-bibr">156</a>]]. (<b>B</b>) Trafficking of Ci across the plasma membrane and to the pyrenoid. CO<sub>2</sub> either directly diffuses across the plasma membrane or is transported by LCI1. It can also be converted to bicarbonate by the activity of CAH1 and transported by HLA3, LCIA, and BSTs through the plasma membrane, chloroplast envelope, and thylakoid membranes, respectively. In the thylakoids that penetrate the pyrenoid, the bicarbonate is converted to CO<sub>2</sub> through the activity of CAH3, and then fixed by Rubisco. The components and their locations in the cell are based on studies with <span class="html-italic">Chlamydomonas</span> [<a href="#B157-plants-13-03015" class="html-bibr">157</a>]. (<b>C</b>) Putative metabolite transport required for photorespiration in <span class="html-italic">Chlamydomonas</span>. Transporters responsible for metabolite transport at the chloroplast and mitochondrial envelope membranes; most of these transporters have not yet been identified in <span class="html-italic">Chlamydomonas</span> (except for ammonium transporter, AMT). (<b>D</b>) Fermentation in <span class="html-italic">Chlamydomonas</span>. Glycerol, lactate, CO<sub>2</sub>, H<sub>2</sub>, acetate, ethanol, and formate are fermentative end-products. The succinate-producing pathway (reverse TCA cycle) is not shown. Gray arrows in the cytosol indicate omitted steps under fermentative conditions. The ethanol pathway in mitochondria has been shown only at the activity level, so it is indicated in gray. Transporters responsible for metabolite transport at the chloroplast envelope membrane and mitochondria membrane are not yet characterized [adapted from <span class="html-italic">Chlamydomonas</span> Source Book [<a href="#B158-plants-13-03015" class="html-bibr">158</a>]]. AAT1, alanine aminotransferase; ACK, acetate kinase; ADH, alcohol dehydrogenase; AGT, alanine-glyoxylate transaminase; AldDH, aldehyde dehydrogenase; BST, bestrophin; CAH, carbonic anhydrase; DLDH1, dihydrolipoyl dehydrogenase; EPYC1, essential pyrenoid component 1; FDX, ferredoxin; GCSH/P/T, glycine cleavage system, H-protein/P-protein/T-protein; GPDH, glycerol-3-phosphate dehydrogenase; GPP, glycerol-3-phosphate phosphatase; GYD, glycolate dehydrogenase; HPR, hydroxypyruvate reductase; HYDA, FeFe hydrogenase; LCI, low-CO<sub>2</sub> inducible protein; LDH1, lactate dehydrogenase 1; MME, malic enzyme; PDC3, pyruvate decarboxylase; PEPC, phosphoenolpyruvate carboxylase; PFL1, pyruvate formate lyase 1; PFR1, pyruvate ferredoxin oxidoreductase; PGP, phosphoglycolate phosphatase; PPDK, pyruvate phosphate dikinase; PAT, phosphate acetyltransferase; Rubisco, ribulose-1,5-bisphosphate carboxylase/oxygenase; SGA1, serine glyoxylate aminotransferase; SHMT, serine hydroxymethyltransferase; AcAld, acetaldehyde; AcCoA, acetyl coenzyme A; CO<sub>2</sub>, carbon dioxide; DHAP, dihydroxyacetone phosphate; ETOH, ethanol; Glc6P, glucose 6-phosphate; G3P, glyceraldehyde-3-phosphate; GP, glycerol-3-phosphate; H<sub>2</sub>, hydrogen; HCO<sub>3</sub><sup>−</sup>, bicarbonate; NH<sub>3</sub>, ammonium; NADPH, reduced nicotinamide adenine dinucleotide phosphate; O<sub>2</sub>, oxygen; OAA, oxaloacetate; PEP, phosphoenolpyruvate; 3-PGA, 3-phosphoglyceric acid; Pi, inorganic phosphate; RuBP, ribulose 1,5-bisphosphate. ICEM, inner chloroplast envelope membrane; OCEM, outer chloroplast envelope membrane. Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p>
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<p>Metabolite transport required for various metabolic processes in the cell. (<b>A</b>) CO<sub>2</sub>-concentrating mechanism in a NADP-malic enzyme type C4 plant, such as sugarcane [adapted with some modifications from [<a href="#B156-plants-13-03015" class="html-bibr">156</a>]]. (<b>B</b>) Trafficking of Ci across the plasma membrane and to the pyrenoid. CO<sub>2</sub> either directly diffuses across the plasma membrane or is transported by LCI1. It can also be converted to bicarbonate by the activity of CAH1 and transported by HLA3, LCIA, and BSTs through the plasma membrane, chloroplast envelope, and thylakoid membranes, respectively. In the thylakoids that penetrate the pyrenoid, the bicarbonate is converted to CO<sub>2</sub> through the activity of CAH3, and then fixed by Rubisco. The components and their locations in the cell are based on studies with <span class="html-italic">Chlamydomonas</span> [<a href="#B157-plants-13-03015" class="html-bibr">157</a>]. (<b>C</b>) Putative metabolite transport required for photorespiration in <span class="html-italic">Chlamydomonas</span>. Transporters responsible for metabolite transport at the chloroplast and mitochondrial envelope membranes; most of these transporters have not yet been identified in <span class="html-italic">Chlamydomonas</span> (except for ammonium transporter, AMT). (<b>D</b>) Fermentation in <span class="html-italic">Chlamydomonas</span>. Glycerol, lactate, CO<sub>2</sub>, H<sub>2</sub>, acetate, ethanol, and formate are fermentative end-products. The succinate-producing pathway (reverse TCA cycle) is not shown. Gray arrows in the cytosol indicate omitted steps under fermentative conditions. The ethanol pathway in mitochondria has been shown only at the activity level, so it is indicated in gray. Transporters responsible for metabolite transport at the chloroplast envelope membrane and mitochondria membrane are not yet characterized [adapted from <span class="html-italic">Chlamydomonas</span> Source Book [<a href="#B158-plants-13-03015" class="html-bibr">158</a>]]. AAT1, alanine aminotransferase; ACK, acetate kinase; ADH, alcohol dehydrogenase; AGT, alanine-glyoxylate transaminase; AldDH, aldehyde dehydrogenase; BST, bestrophin; CAH, carbonic anhydrase; DLDH1, dihydrolipoyl dehydrogenase; EPYC1, essential pyrenoid component 1; FDX, ferredoxin; GCSH/P/T, glycine cleavage system, H-protein/P-protein/T-protein; GPDH, glycerol-3-phosphate dehydrogenase; GPP, glycerol-3-phosphate phosphatase; GYD, glycolate dehydrogenase; HPR, hydroxypyruvate reductase; HYDA, FeFe hydrogenase; LCI, low-CO<sub>2</sub> inducible protein; LDH1, lactate dehydrogenase 1; MME, malic enzyme; PDC3, pyruvate decarboxylase; PEPC, phosphoenolpyruvate carboxylase; PFL1, pyruvate formate lyase 1; PFR1, pyruvate ferredoxin oxidoreductase; PGP, phosphoglycolate phosphatase; PPDK, pyruvate phosphate dikinase; PAT, phosphate acetyltransferase; Rubisco, ribulose-1,5-bisphosphate carboxylase/oxygenase; SGA1, serine glyoxylate aminotransferase; SHMT, serine hydroxymethyltransferase; AcAld, acetaldehyde; AcCoA, acetyl coenzyme A; CO<sub>2</sub>, carbon dioxide; DHAP, dihydroxyacetone phosphate; ETOH, ethanol; Glc6P, glucose 6-phosphate; G3P, glyceraldehyde-3-phosphate; GP, glycerol-3-phosphate; H<sub>2</sub>, hydrogen; HCO<sub>3</sub><sup>−</sup>, bicarbonate; NH<sub>3</sub>, ammonium; NADPH, reduced nicotinamide adenine dinucleotide phosphate; O<sub>2</sub>, oxygen; OAA, oxaloacetate; PEP, phosphoenolpyruvate; 3-PGA, 3-phosphoglyceric acid; Pi, inorganic phosphate; RuBP, ribulose 1,5-bisphosphate. ICEM, inner chloroplast envelope membrane; OCEM, outer chloroplast envelope membrane. Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p>
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21 pages, 2709 KiB  
Article
Integrating Spectral Sensing and Systems Biology for Precision Viticulture: Effects of Shade Nets on Grapevine Leaves
by Renan Tosin, Igor Portis, Leandro Rodrigues, Igor Gonçalves, Catarina Barbosa, Jorge Teixeira, Rafael J. Mendes, Filipe Santos, Conceição Santos, Rui Martins and Mário Cunha
Horticulturae 2024, 10(8), 873; https://doi.org/10.3390/horticulturae10080873 - 18 Aug 2024
Viewed by 1467
Abstract
This study investigates how grapevines (Vitis vinifera L.) respond to shading induced by artificial nets, focusing on physiological and metabolic changes. Through a multidisciplinary approach, grapevines’ adaptations to shading are presented via biochemical analyses and hyperspectral data that are then combined with [...] Read more.
This study investigates how grapevines (Vitis vinifera L.) respond to shading induced by artificial nets, focusing on physiological and metabolic changes. Through a multidisciplinary approach, grapevines’ adaptations to shading are presented via biochemical analyses and hyperspectral data that are then combined with systems biology techniques. In the study, conducted in a ‘Moscatel Galego Branco’ vineyard in Portugal’s Douro Wine Region during post-veraison, shading was applied and predawn leaf water potential (Ψpd) was then measured to assess water stress. Biochemical analyses and hyperspectral data were integrated to explore adaptations to shading, revealing higher chlorophyll levels (chlorophyll a-b 117.39% higher) and increased Reactive Oxygen Species (ROS) levels in unshaded vines (52.10% higher). Using a self-learning artificial intelligence algorithm (SL-AI), simulations highlighted ROS’s role in stress response and accurately predicted chlorophyll a (R2: 0.92, MAPE: 24.39%), chlorophyll b (R2: 0.96, MAPE: 17.61%), and ROS levels (R2: 0.76, MAPE: 52.17%). In silico simulations employing flux balance analysis (FBA) elucidated distinct metabolic phenotypes between shaded and unshaded vines across cellular compartments. Integrating these findings provides a systems biology approach for understanding grapevine responses to environmental stressors. The leveraging of advanced omics technologies and precise metabolic models holds immense potential for untangling grapevine metabolism and optimizing viticultural practices for enhanced productivity and quality. Full article
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Graphical abstract

Graphical abstract
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<p>Representation of an integrated methodology combining the genomics, metabolomics, and systems biology approaches, aimed at establishing connections between laboratory experiments and field observations. Also, represents the integration of sensors for detecting molecular components and monitoring the physiological state of plants to feed the systems biology.</p>
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<p>Mean spectra absorbance from leaves on vines exposed to unshaded conditions and shaded conditions. a.u.: arbitrary units.</p>
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<p>Predawn leaf water potential variation in grapevine leaves under unshaded and shaded conditions. * Statistically significant (<span class="html-italic">p</span> &lt; 0.05) according to <span class="html-italic">t</span>-test.</p>
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<p>Results of the use of foliar spectral data combined with self-learning artificial intelligence (SL-AI) in modelling: (<b>a</b>) chlorophyll <span class="html-italic">a</span> (mg/gFM), (<b>b</b>) chlorophyll <span class="html-italic">b</span> (mg/gFM), and (<b>c</b>) reactive oxygen species (ROS—ABS/gFM). N = number of samples considered. Coefficient of determination (R<sup>2</sup>), root mean square error (RMSE), and mean absolute percentage error (MAPE—%).</p>
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<p>Photorespiration cycle under (<b>a</b>) shaded and (<b>b</b>) unshaded conditions, showcasing the Photorespiration III hypothesis with the objective reactions in the flux balance analyses of Peroxisomal Catalase and Peroxisomal Glycolate Oxidase. The red and blue arrows represent respectively the flux balance analysis (FBA) of the reactions involved under shaded and unshaded conditions, offering a dynamic regulation of photorespiratory metabolism in response to environmental light conditions. The arrows’ sizes indicates each pathway’s intensity under the respective condition. Figure adapted from Huma, Kundu, Poolman, Kruger and Fell [<a href="#B46-horticulturae-10-00873" class="html-bibr">46</a>].</p>
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<p>Panel (<b>a</b>) shows the phenotype spaces for the three Flux Balance Analysis (FBA) models under different photorespiration conditions. For unshaded conditions, FBA models include FBA_P_CT_U (Peroxisomal Catalase Reaction as objective), FBA_P_96_U (Peroxisomal Glycolate Oxidase Reaction), and FBA_B3_U (Peroxisomal Catalase Reaction as objective and Peroxisomal Glycolate Oxidase Reaction as objective). Similarly, shaded conditions are represented by FBA_P_CT_S, FBA_P_96_S and FBA_B3_S, respectively. Panel (<b>b</b>) demonstrates the phenotype spaces resulting from Monte Carlo (MC) simulations, illustrating the range of variations in chlorophyll and reactive oxygen species (ROS) levels assessed in laboratory experiments. MC simulations encompass MC_B3 (Peroxisomal Catalase Reaction as objective and Peroxisomal Glycolate Oxidase Reaction as objective), MC_P_96 (Peroxisomal Glycolate Oxidase Reaction as objective), and MC_P_CT (Peroxisomal Catalase Reaction as objective). In the case of MC_P_96 and MC_P_CT, 1 represents higher ROS (unshaded condition) and 10, lower ROS (shaded condition). MC_B3 did not result in a valid MC simulation.</p>
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40 pages, 2188 KiB  
Review
Photosynthesis: Genetic Strategies Adopted to Gain Higher Efficiency
by Naveed Khan, Seok-Hyun Choi, Choon-Hwan Lee, Mingnan Qu and Jong-Seong Jeon
Int. J. Mol. Sci. 2024, 25(16), 8933; https://doi.org/10.3390/ijms25168933 - 16 Aug 2024
Viewed by 4336
Abstract
The global challenge of feeding an ever-increasing population to maintain food security requires novel approaches to increase crop yields. Photosynthesis, the fundamental energy and material basis for plant life on Earth, is highly responsive to environmental conditions. Evaluating the operational status of the [...] Read more.
The global challenge of feeding an ever-increasing population to maintain food security requires novel approaches to increase crop yields. Photosynthesis, the fundamental energy and material basis for plant life on Earth, is highly responsive to environmental conditions. Evaluating the operational status of the photosynthetic mechanism provides insights into plants’ capacity to adapt to their surroundings. Despite immense effort, photosynthesis still falls short of its theoretical maximum efficiency, indicating significant potential for improvement. In this review, we provide background information on the various genetic aspects of photosynthesis, explain its complexity, and survey relevant genetic engineering approaches employed to improve the efficiency of photosynthesis. We discuss the latest success stories of gene-editing tools like CRISPR-Cas9 and synthetic biology in achieving precise refinements in targeted photosynthesis pathways, such as the Calvin-Benson cycle, electron transport chain, and photorespiration. We also discuss the genetic markers crucial for mitigating the impact of rapidly changing environmental conditions, such as extreme temperatures or drought, on photosynthesis and growth. This review aims to pinpoint optimization opportunities for photosynthesis, discuss recent advancements, and address the challenges in improving this critical process, fostering a globally food-secure future through sustainable food crop production. Full article
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<p>A comprehensive schematic diagram of the electron transport chain (ETC) showing linear and cyclic electron flow along with the strategies used to manipulate or enhance the electron-transfer process. The common photosynthetic machinery complex is shown by a grey box. The dotted red lines represent the linear electron transfer (LET). It starts with water oxidation, and the electron transfers through PSII-PQ-Cytb6f-PC-PSI. PSI then transfers the electrons to ferredoxin, which reduces NADP+ to NADPH. During LET, protons are translocated from the stroma to the lumen, generating a proton gradient across the thylakoid membrane. This proton gradient drives ATP synthesis from ADP by ATP synthase and protonates the light-harvesting complex, changing it from the light-harvesting mode to light-dissipative mode through the PsbS gene (light yellow oval box) and xanthophyll cycle (light brown square box). Proteins involved in the conventional electron-transfer chain for which overexpression/knockout has been shown to affect photosynthesis are enclosed in a star shape. The overexpression of the nuclear-encoded D1 gene discussed in the text is shown by the yellow box. Moreover, the exogenous overexpression of soluble electron transfer cytochrome c6 and ferredoxin gene from algal Porphyra yezoensis (Py) and Methanothermobacter thermautotrophicus (MtFd) are depicted in green and violet boxes, respectively. In parallel to LET, other alternative electron transfers are also present, such as cyclic electron transfer (CET). There are two types of CET; the dotted blue lines show PGR5/PGRL1-type CET, while the dotted orange lines show NDH-dependent CET. Additionally, the KEA3 potassium ion channel, which influences photosynthetic efficiency, is highlighted. The square boxes represent master regulators that control various genes involved in photosynthetic components. Another square box illustrates circadian clock genes that regulate the expression of photosynthesis-related genes, particularly those involved in pigment regulation. The ATP and NADPH generated during the light reactions of photosynthesis are utilized by the Calvin-Benson cycle (CBC) to fix carbon dioxide and synthesize carbohydrates.</p>
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<p>A systematic representation of the Calvin-Benson Cycle. The eleven enzymes of the CBC are indicated in white boxes with black borders. Endogenous enzymes that have been genetically manipulated in previous studies, as discussed in our review, are enclosed in star shapes. The formation of the GAPDH-CP12-PRK complex is depicted by dotted lines. The exogenous overexpression of cyanobacterial (cy) genes is shown in light brown boxes. Enzymes: RubisCO, ribulose-1,5-bisphosphate carboxylase/oxygenase; PGK, phosphoglycerate kinase; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; TPI, triose phosphate isomerase; FBPA, fructose-1,6-bisphosphate aldolase; FBPase, fructose-1,6-bisphosphatase; transketolase; SBPA, sedohuptulose bisphosphate aldolase; SBPase, sedoheptulose-1,7-bisphosphatase; RPE, ribulose-5-phosphate 3-epimerase; RPI, ribose-5-phosphate isomerase; PRK, phosphoribulokinase. Metabolites: RuBP, ribulose-1,5-bisphosphate; 3-PGA, 3-phosphoglycerate; 1,3-PGA, 1,3-bisphosphoglycerate; G3P, glyceraldehyde-3-phosphate; DHAP, dihydroxyacetone phosphate; 1F,6P, fructose-1,6-bisphosphate; F6P, fructose-6-phosphate; Xu5P, xylulose-5-phosphate; E4P, erythrose-4-phosphate; 1S,7P, sedoheptulose-1,7-bisphosphate; S7P, sedoheptulose-7-phosphate; RuP, ribulose-5-phosphate.</p>
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<p>This diagram illustrates the natural photorespiration process alongside four engineered by-pass systems and a novel enzymatic strategy to enhance photosynthesis efficiency. The natural photorespiration pathway, depicted with black arrows, involves a series of reactions across the chloroplast, peroxisome, and mitochondria, with key enzymes in white boxes (cytosolic reactions are not presented here for simplicity). Proteins involved in conventional photorespiration, whose overexpression affects photosynthesis, are enclosed in star shapes. Engineered pathways designed to reduce natural inefficiencies are enclosed in grey boxes, each color-coded to represent different bypass strategies. These include modifications within the chloroplast and peroxisome to reduce the loss of carbon and energy typically associated with photorespiration. A new-to-nature enzymatic approach (Tartronyl pathway, TaCo) is also highlighted. In the TaCo pathway, glycolate is first converted into glycolyl-CoA by the enzyme GCS. The enzyme GCC carboxylates glycolyl-CoA to form tatronyl-CoA. Finally, tatronyl-CoA is transformed into glycerate by the enzyme TCR, showcasing the potential to further optimize carbon fixation through synthetic biology. This comprehensive representation integrates both traditional and innovative tactics, providing a multi-faceted view of current advancements in photorespiration modification. The details for each engineered strategy highlighted here are explained in the text. Species from which genes are sourced to engineer by-pass processes include the following: At, <span class="html-italic">Arabidopsis thaliana</span>; Cm, <span class="html-italic">Cucurbita maxim</span>; Cr, <span class="html-italic">Chlamydomonas reinhardtii</span>; Ec, <span class="html-italic">Escherichia coli</span>. Enzyme names: CAT, catalase; GCC, glycolyl-CoA carboxylase; GCL, glyoxylate carboligase; GCS, glycolyl CoA synthase; GDC, glycine decarboxylase; GDH, glycolate dehydrogenase; GGAT, glutamate glyoxylate aminotransferase; GLYK, glycerate kinase; GLO, glycolate oxidase; GOX, glycolate oxidase; HPR1, hydroxypyruvate reductase 1; HY1, hydroxypyruvate isomerase; MS, malate synthase; PGP, phosphoglycolate phosphatase; RubisCO, ribulose-1,5-bisphosphate carboxylase/oxygenase; SGAT, serine glyoxylate aminotransferase; SHMT, serine hydroxymethyl transferase; TSR, tartronic semialdehyde reductase. Metabolite: 2-PG, 2-phosphoglycolate; 3-PGA, 3-phosphoglycerate; RuBP, ribulose-1,5-bisphosphate; TSA, tartronic semialdehyde; PLGG1, plastidial glycolate/glycerate transporter 1 (the transporter is shown by orange helix-like structure).</p>
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33 pages, 1336 KiB  
Review
Enhancing Photosynthesis and Plant Productivity through Genetic Modification
by Mansoureh Nazari, Mojtaba Kordrostami, Ali Akbar Ghasemi-Soloklui, Julian J. Eaton-Rye, Pavel Pashkovskiy, Vladimir Kuznetsov and Suleyman I. Allakhverdiev
Cells 2024, 13(16), 1319; https://doi.org/10.3390/cells13161319 - 7 Aug 2024
Cited by 2 | Viewed by 4998
Abstract
Enhancing crop photosynthesis through genetic engineering technologies offers numerous opportunities to increase plant productivity. Key approaches include optimizing light utilization, increasing cytochrome b6f complex levels, and improving carbon fixation. Modifications to Rubisco and the photosynthetic electron transport chain are central to [...] Read more.
Enhancing crop photosynthesis through genetic engineering technologies offers numerous opportunities to increase plant productivity. Key approaches include optimizing light utilization, increasing cytochrome b6f complex levels, and improving carbon fixation. Modifications to Rubisco and the photosynthetic electron transport chain are central to these strategies. Introducing alternative photorespiratory pathways and enhancing carbonic anhydrase activity can further increase the internal CO2 concentration, thereby improving photosynthetic efficiency. The efficient translocation of photosynthetically produced sugars, which are managed by sucrose transporters, is also critical for plant growth. Additionally, incorporating genes from C4 plants, such as phosphoenolpyruvate carboxylase and NADP-malic enzymes, enhances the CO2 concentration around Rubisco, reducing photorespiration. Targeting microRNAs and transcription factors is vital for increasing photosynthesis and plant productivity, especially under stress conditions. This review highlights potential biological targets, the genetic modifications of which are aimed at improving photosynthesis and increasing plant productivity, thereby determining key areas for future research and development. Full article
(This article belongs to the Section Plant, Algae and Fungi Cell Biology)
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<p>Approximate diagram of the possible regulation of photosynthetic activity and its relationship with nuclear-encoded proteins, indicating the most promising targets for plant transformation to increase photosynthetic processes and productivity. Phosphoenolpyruvate carboxylase (PEPC) catalyzes the conversion of phosphoenolpyruvate (PEP) and bicarbonate (HCO<sub>3</sub><sup>−</sup>) to oxaloacetate during C<sub>4</sub> and CAM photosynthesis, thereby increasing CO<sub>2</sub> fixation. The cytochrome <span class="html-italic">b<sub>6</sub>f</span> complex (Cyt <span class="html-italic">b<sub>6</sub>f</span>) plays a crucial role in the electron transport chain between PSII and PSI, contributing to the generation of the proton gradient used for ATP synthesis. ATP synthase utilizes a proton gradient to synthesize ATP from ADP and inorganic phosphate. Chlorophylls (Chls) and carotenoids (Cars) are pigments involved in light absorption and protection against photodamage. Carbonic anhydrase (CA) catalyzes the interconversion of CO<sub>2</sub> and HCO<sub>3</sub><sup>−</sup>, facilitating efficient CO<sub>2</sub> utilization for the Calvin cycle. The Rubisco large subunit (RbcL) and small subunit (RbcS) are components of Rubisco, the enzyme responsible for CO<sub>2</sub> fixation in the Calvin cycle. The Rubisco assembly factor (RF) assists in the assembly and activation of Rubisco, thereby increasing its efficiency. Rubisco activase (RCA) facilitates Rubisco activation by removing inhibitory sugar-phosphate compounds from its active sites. Transcription factors (TFs), such as Golden2-like (GLK), regulate chloroplast development and photosynthesis-related gene expression. NAC (NAM, ATAF, and CUC) factors are involved in various aspects of plant development and stress responses. Basic leucine zippers (bZIPs) play roles in the stress response, hormone signaling, and development. SQUAMOSA promoter binding protein-like (SBP) is involved in flower development and phase transition. The sucrose transporter (SUT) facilitates the transport of sucrose across cellular membranes. Aquaporins facilitate water transport across cell membranes, impacting turgor pressure and cell expansion. MicroRNAs (miRNAs) are small noncoding RNA molecules that regulate gene expression posttranscriptionally by binding to complementary sequences on target mRNAs. Argonaute (Ago), a part of the RNA-induced silencing complex (RISC), guides miRNA to its target mRNA, leading to mRNA degradation or translational repression. miRNAs, such as miR156, miR396, miR319, miR408, miR162, miR397, and miR172, regulate various aspects of plant growth and development by targeting specific mRNAs. Peroxisomes, organelles involved in the oxidative metabolism and detoxification of ROS, play a role in cellular signaling and stress responses. Chloroplast–nucleus signaling is characterized by feedback mechanisms in which changes in chloroplast function affect nuclear gene expression, influencing overall plant responses to environmental factors.</p>
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<p>Calvin cycle: Rubisco catalyzes the carboxylation of ribulose-1,5-bisphosphate (RuBP) to fix CO<sub>2</sub>, forming 3-phosphoglycerate (GP), the first stable product. GP is then converted to triose phosphate (TP) via ATP and NADPH. TP can regenerate RuBP or form sugars such as glucose and fructose. ATP provides energy, and NADPH supplies reducing power for these conversions. Photorespiration: When Rubisco reacts with O<sub>2</sub> instead of CO<sub>2</sub>, RuBP is oxygenated, producing GP and phosphoglycolate (PG). PG is processed via the photorespiratory pathway to recover carbon and return it to the Calvin cycle. ATP and NADPH are utilized to convert the intermediates back into GP. O<sub>2</sub> competes with CO<sub>2</sub> at the Rubisco active site, leading to photorespiration. ATP and NADPH generated during the light-dependent reactions of photosynthesis support both the Calvin cycle and photorespiration.</p>
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15 pages, 3602 KiB  
Article
Flux Calculation for Primary Metabolism Reveals Changes in Allocation of Nitrogen to Different Amino Acid Families When Photorespiratory Activity Changes
by Nils Friedrichs, Danial Shokouhi and Arnd G. Heyer
Int. J. Mol. Sci. 2024, 25(15), 8394; https://doi.org/10.3390/ijms25158394 - 1 Aug 2024
Viewed by 824
Abstract
Photorespiration, caused by oxygenation of the enzyme Rubisco, is considered a wasteful process, because it reduces photosynthetic carbon gain, but it also supplies amino acids and is involved in amelioration of stress. Here, we show that a sudden increase in photorespiratory activity not [...] Read more.
Photorespiration, caused by oxygenation of the enzyme Rubisco, is considered a wasteful process, because it reduces photosynthetic carbon gain, but it also supplies amino acids and is involved in amelioration of stress. Here, we show that a sudden increase in photorespiratory activity not only reduced carbon acquisition and production of sugars and starch, but also affected diurnal dynamics of amino acids not obviously involved in the process. Flux calculations based on diurnal metabolite profiles suggest that export of proline from leaves increases, while aspartate family members accumulate. An immense increase is observed for turnover in the cyclic reaction of glutamine synthetase/glutamine-oxoglutarate aminotransferase (GS/GOGAT), probably because of increased production of ammonium in photorespiration. The hpr1-1 mutant, defective in peroxisomal hydroxypyruvate reductase, shows substantial alterations in flux, leading to a shift from the oxoglutarate to the aspartate family of amino acids. This is coupled to a massive export of asparagine, which may serve in exchange for serine between shoot and root. Full article
(This article belongs to the Special Issue Plant Respiration in the Light and Photorespiration)
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<p>Diurnal profiles of metabolite concentrations in µmol g<sup>−1</sup> fresh weight over time in hours from light on for metabolites that showed significant genotype × daytime interaction in a two-way ANOVA (<span class="html-italic">p</span> &lt; 0.05, n = 5) at least under one CO<sub>2</sub> treatment. Graphs show diurnal dynamics for Col-0 (black dashed lines) and the <span class="html-italic">hpr1-1</span> mutant (red lines for eCO<sub>2</sub>; blue lines for aCO<sub>2</sub>) at elevated CO<sub>2</sub> (<b>A</b>–<b>G</b>) and after a sudden shift to ambient CO<sub>2</sub> level (<b>H</b>–<b>N</b>). Lines connect means of measurements at time points 0, 2, 4, 6, 8, 16 and 24 h starting from light on. Light off was at 8 h. Error bars show standard error of the mean (n = 5). Results of the ANOVA for all metabolites are given in <a href="#app1-ijms-25-08394" class="html-app">Supplemental S1 and S2</a>.</p>
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<p>Model used for simulating primary metabolism. Black arrows show routes of carbon; dashed arrows show routes of nitrogen. Gly: glycine, Ser: serine; HP: sugar phosphates, ex4: export of Ser; AA1: pool of cysteine, pyruvate family and aromatic amino acids; MF: pool of malate and fumarate; exp1: sugar export, exp3: export of AA1, Cit: citrate, Asf: aspartate family; KgF: oxoglutarate family; Glu: glutamate; αKG: oxoglutarate, exp5: export of Asf; exp2: export of KgF, Gln: glutamine, NH4<sup>+</sup>: ammonium. Reactions 1: net photosynthesis; 2: starch build and degradation; 3: synthesis of sucrose, including hydrolysis to hexoses (sucrose cycling); 4: export; 5: synthesis of AA1; 6: export; 7: photorespiratory Gly production; 8. Gly decarboxylation and Ser synthesis; 9: hydroxypyruvate reduction; 10: export or import of Ser; 11: synthesis of malate and fumarate; 12: respiration; 13: synthesis of citrate; 14: synthesis of Asf; 15: export; 16, 17: GS/GOGAT cycle; 18: synthesis of KgF; 19: export.</p>
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<p>Measured and simulated metabolite levels in µmol g<sup>−1</sup> fresh weight for Col-0 (black) and <span class="html-italic">hpr1-1</span> (red) plants grown constantly at elevated CO<sub>2</sub> level (1000 ppm). Closed circles at time points 0, 2, 4, 6, 8, 16 and 24 are means of five-fold replication. Open circles were created by a smoothing spline (see <a href="#sec2-ijms-25-08394" class="html-sec">Section 2</a>). Lines are results of simulations with the best fitting parameter set. For abbreviations of metabolites, see <a href="#ijms-25-08394-f002" class="html-fig">Figure 2</a>.</p>
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<p>Measured and simulated metabolite levels in µmol g<sup>−1</sup> fresh weight for Col-0 (black) and <span class="html-italic">hpr1-1</span> (blue) plants shifted from elevated to ambient CO<sub>2</sub> level (~450 ppm). Closed circles at time points 0, 2, 4, 6, 8, 16 and 24 are means of five-fold replication. Open circles were created by a smoothing spline (see <a href="#sec2-ijms-25-08394" class="html-sec">Section 2</a>). Lines are results of simulations with the best fitting parameter set. For abbreviations of metabolites, see <a href="#ijms-25-08394-f002" class="html-fig">Figure 2</a>.</p>
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<p>Calculated flux trajectories in µmol g<sup>−1</sup> h<sup>−1</sup> for Col-0 (dashed black lines) and <span class="html-italic">hpr1-1</span> (red lines) constantly grown at elevated CO<sub>2</sub> level (1000 ppm). All fluxes are termed based on reaction products, except f_hpr, which is the HPR reaction, and f_shmt, which is a proxy for reactions leading from Ser to Gly. All exports out of the model scope have the prefix ex_. Numbers in parentheses refer to the numbering of reactions in <a href="#ijms-25-08394-f002" class="html-fig">Figure 2</a>.</p>
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<p>Calculated flux trajectories in µmol g-1 h-1 for Col-0 (dashed black lines) and hpr1-1 (blue lines) shifted from elevated to ambient CO<sub>2</sub> level (~450 ppm). All fluxes are termed based on reaction products, except f_hpr, which is the HPR reaction, and f_shmt, which is a proxy for reactions leading from Ser to Gly. All exports out of the model scope have the prefix ex_. Numbers in parentheses refer to the numbering of reactions in <a href="#ijms-25-08394-f002" class="html-fig">Figure 2</a>.</p>
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<p>Schemes of carbon allocation during day (<b>A</b>–<b>D</b>) and night (<b>E</b>–<b>H</b>) for Col-0 (right) and hpr1-1 (left). (<b>A</b>,<b>B</b>,<b>E</b>,<b>F</b>): eCO<sub>2</sub>; (<b>C</b>,<b>D</b>,<b>G</b>,<b>H</b>): aCO<sub>2</sub>. The width of arrows indicates flux into a metabolite pool integrated over day and night, respectively.</p>
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19 pages, 1916 KiB  
Review
Impacts of Drought on Photosynthesis in Major Food Crops and the Related Mechanisms of Plant Responses to Drought
by Meiyu Qiao, Conghao Hong, Yongjuan Jiao, Sijia Hou and Hongbo Gao
Plants 2024, 13(13), 1808; https://doi.org/10.3390/plants13131808 - 30 Jun 2024
Cited by 24 | Viewed by 4683
Abstract
Drought stress is one of the most critical threats to crop productivity and global food security. This review addresses the multiple effects of drought on the process of photosynthesis in major food crops. Affecting both light-dependent and light-independent reactions, drought leads to severe [...] Read more.
Drought stress is one of the most critical threats to crop productivity and global food security. This review addresses the multiple effects of drought on the process of photosynthesis in major food crops. Affecting both light-dependent and light-independent reactions, drought leads to severe damage to photosystems and blocks the electron transport chain. Plants face a CO2 shortage provoked by stomatal closure, which triggers photorespiration; not only does it reduce carbon fixation efficiency, but it also causes lower overall photosynthetic output. Drought-induced oxidative stress generates reactive oxygen species (ROS) that damage cellular structures, including chloroplasts, further impairing photosynthetic productivity. Plants have evolved a variety of adaptive strategies to alleviate these effects. Non-photochemical quenching (NPQ) mechanisms help dissipate excess light energy as heat, protecting the photosynthetic apparatus under drought conditions. Alternative electron pathways, such as cyclical electron transmission and chloroplast respiration, maintain energy balance and prevent over-reduction of the electron transport chain. Hormones, especially abscisic acid (ABA), ethylene, and cytokinin, modulate stomatal conductance, chlorophyll content, and osmotic adjustment, further increasing the tolerance to drought. Structural adjustments, such as leaf reordering and altered root architecture, also strengthen tolerance. Understanding these complex interactions and adaptive strategies is essential for developing drought-resistant crop varieties and ensuring agricultural sustainability. Full article
(This article belongs to the Special Issue Mechanism of Drought and Salinity Tolerance in Crops)
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Figure 1
<p>Effects of drought stress on the photosynthesis of major food crops. Drought stress not only reduces the rate of light reaction and dark reaction, but also restricts the acquirement of the substances for photosynthesis. Moreover, drought stress results in the accumulation of reactive oxygen species (ROS), which causes various damages in chloroplasts.</p>
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<p>Drought stress responses in major food crops, with an emphasis on photosynthesis. Plants respond to drought at various levels to protect photosynthesis and other aspects of plants. NPQ, non-photochemical quenching. CET, cyclic electron transfer. WUE, water use efficiency.</p>
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<p>Photorespiration and drought response in C<sub>3</sub> and C<sub>4</sub> plants. The mechanisms of photorespiration and drought response in C<sub>3</sub> (such as rice) and C<sub>4</sub> (such as maize) plants differ. Under drought stress, both C<sub>3</sub> and C<sub>4</sub> plants increase ATP and NADPH consumption and reduce ROS production. There are higher water consumption requirements and increased photorespiration in C<sub>3</sub> plants, while C<sub>4</sub> plants exhibit higher photosynthetic rates and efficiency.</p>
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17 pages, 3167 KiB  
Article
Drought Has a Greater Negative Effect on the Growth of the C3 Chenopodium quinoa Crop Halophyte than Elevated CO2 and/or High Temperature
by Zulfira Rakhmankulova, Elena Shuyskaya, Maria Prokofieva, Kristina Toderich, Luizat Saidova, Nina Lunkova and Pavel Voronin
Plants 2024, 13(12), 1666; https://doi.org/10.3390/plants13121666 - 16 Jun 2024
Cited by 2 | Viewed by 1023
Abstract
Plant growth and productivity are predicted to be affected by rising CO2 concentrations, drought and temperature stress. The C3 crop model in a changing climate is Chenopodium quinoa Willd—a protein-rich pseudohalphyte (Amaranthaceae). Morphophysiological, biochemical and molecular genetic studies were performed on [...] Read more.
Plant growth and productivity are predicted to be affected by rising CO2 concentrations, drought and temperature stress. The C3 crop model in a changing climate is Chenopodium quinoa Willd—a protein-rich pseudohalphyte (Amaranthaceae). Morphophysiological, biochemical and molecular genetic studies were performed on quinoa grown at ambient (400 ppm, aCO2) and elevated (800 ppm, eCO2) CO2 concentrations, drought (D) and/or high temperature (eT) treatments. Among the single factors, drought caused the greatest stress response, inducing disturbances in the light and dark photosynthesis reactions (PSII, apparent photosynthesis) and increasing oxidative stress (MDA). Futhermore, compensation mechanisms played an important protective role against eT or eCO2. The disruption of the PSII function was accompanied by the activation of the expression of PGR5, a gene of PSI cyclic electron transport (CET). Wherein under these conditions, the constant Rubisco content was maintained due to an increase in its biosynthesis, which was confirmed by the activation of rbcL gene expression. In addition, the combined stress treatments D+eT and eCO2+D+eT caused the greatest negative effect, as measured by increased oxidative stress, decreased water use efficiency, and the functioning of protective mechanisms, such as photorespiration and the activity of antioxidant enzymes. Furthermore, decreased PSII efficiency and increased non-photochemical quenching (NPQ) were not accompanied by the activation of protective mechanisms involving PSI CET. In summary, results show that the greatest stress experienced by C. quinoa plants was caused by drought and the combined stresses D+eT and eCO2+D+eT. Thus, drought consistently played a decisive role, leading to increased oxidative stress and a decrease in defense mechanism effectiveness. Full article
(This article belongs to the Special Issue Plant Ecophysiological Adaptation to Environmental Stress II)
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Figure 1
<p>The effect of drought, elevated temperature and combined factors on growth and water-ionic parameters in <span class="html-italic">Chenopodium quinoa</span> plants under ambient (400 ppm, aCO<sub>2</sub>) and elevated (800 ppm, eCO<sub>2</sub>) CO<sub>2</sub> concentrations. (<b>A</b>) Dry biomass; (<b>B</b>) leaf mass per area, LMA; (<b>C</b>) water content; (<b>D</b>) proline content; (<b>E</b>–<b>G</b>) K<sup>+</sup> and Na<sup>+</sup> content. G—plants growing at aCO<sub>2</sub> (control) or eCO<sub>2</sub> without treatment; D—drought treatment; eT—elevated temperature treatment; D+eT—combined treatment with drought and elevated temperature. Values are means ± standard errors (<span class="html-italic">n</span> = 5). The different letters show statistically different means at <span class="html-italic">p</span> ≤ 0.05 (Tukey test).</p>
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<p>The effect of drought, elevated temperature and combined factors on photosynthetic and respiration parameters in <span class="html-italic">Chenopodium quinoa</span> plants under ambient (400 ppm, aCO<sub>2</sub>) and elevated (800 ppm, eCO<sub>2</sub>) CO<sub>2</sub> concentrations. (<b>A</b>) Effective quantum yield of PSII at given light intensities, <span class="html-italic">Φ</span><sub>PSII</sub>; (<b>B</b>) non-photochemical quenching of chlorophyll <span class="html-italic">a</span> fluorescence, NPQ; (<b>C</b>) maximum quantum yield of PSII, <span class="html-italic">F<sub>v</sub></span>/<span class="html-italic">F<sub>m</sub></span>; (<b>D</b>) time required to reach the maximum P700 oxidation level under far-red light (PSI); (<b>E</b>) apparent photosynthesis, A; (<b>F</b>) transpiration intensity, E; (<b>G</b>) water use efficiency, WUE; (<b>H</b>) dark respiration, Rd. G—plants growing at aCO<sub>2</sub> (control) or eCO<sub>2</sub> without treatment; D—drought treatment; eT—elevated temperature treatment; D+eT—combined treatment with drought and elevated temperature. Values are means ± standard errors (<span class="html-italic">n</span> = 5). The different letters show statistically different means at <span class="html-italic">p</span> ≤ 0.05 (Tukey test).</p>
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<p>The effect of drought, elevated temperature and combined factors on photosynthetic gene expression in <span class="html-italic">Chenopodium quinoa</span> plants under ambient (400 ppm, aCO<sub>2</sub>) and elevated (800 ppm, eCO<sub>2</sub>) CO<sub>2</sub> concentrations. (<b>A</b>,<b>B</b>) <span class="html-italic">psaA</span> and <span class="html-italic">psaB</span>, genes encoding apoproteins 1 and 2 of PSI; (<b>C</b>) <span class="html-italic">psbA</span>, gene encoding protein D1 of PSII; (<b>D</b>) <span class="html-italic">PGR5</span>, gene encoding PGR5 protein of the main cyclic electron transport (CET) pathway of PSI; (<b>E</b>) <span class="html-italic">NdhH</span>, gene encoding the H subunit of the NADH dehydrogenase in the second CET pathway of PSI; (<b>F</b>) <span class="html-italic">rbcL</span>, gene encoding the Rubisco large subunit. G—plants growing at aCO<sub>2</sub> (control) or eCO<sub>2</sub> without treatment; D—drought treatment; eT—elevated temperature treatment; D+eT—combined treatment with drought and elevated temperature. Values are means ± standard errors (<span class="html-italic">n</span> = 5). The different letters show statistically different means at <span class="html-italic">p</span> ≤ 0.05 (Tukey test).</p>
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<p>The effect of drought, elevated temperature and combined factors on photosynthesis enzyme content in <span class="html-italic">Chenopodium quinoa</span> plants under ambient (400 ppm, aCO<sub>2</sub>) and elevated (800 ppm, eCO<sub>2</sub>) CO<sub>2</sub> concentrations. (<b>A</b>) Western blots for photosynthetic enzymes from soluble total proteins extracted from leaves of <span class="html-italic">C. quinoa</span> plants, (<b>B</b>) Ribulose-1,5-bisphophate carboxylase/oxygenase (Rubisco, subunit L), (<b>C</b>) Glycine decarboxylase (GDC P protein). Equal protein loading was checked by staining the blots with Ponceau. G—plants growing at aCO<sub>2</sub> (control) or eCO<sub>2</sub> without treatment; D—drought treatment; eT—elevated temperature treatment; D+eT—combined treatment with drought and elevated temperature. Values are means ± standard errors (<span class="html-italic">n</span> = 5). The different letters show statistically different means at <span class="html-italic">p</span> ≤ 0.05 (Tukey test).</p>
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<p>The effect of drought, elevated temperature and combined factors on the lipid peroxidation and antioxidant enzyme activity in <span class="html-italic">Chenopodium quinoa</span> plants under ambient (400 ppm, aCO<sub>2</sub>) and elevated (800 ppm, eCO<sub>2</sub>) CO<sub>2</sub> concentrations. (<b>A</b>) malondialdehyde content, MDA; (<b>B</b>) superoxide dismutase activity, SOD; (<b>C</b>) catalase activity, CAT; (<b>D</b>) guaiacol peroxidase activity, POD. G—plants growing at aCO<sub>2</sub> (control) or eCO<sub>2</sub> without treatment; D—drought treatment; eT—elevated temperature treatment; D+eT—combined treatment with drought and elevated temperature. Values are means ± standard errors (<span class="html-italic">n</span> = 5). The different letters show statistically different means at <span class="html-italic">p</span> ≤ 0.05 (Tukey test).</p>
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<p>Principal component analysis (PCA) of the physiological data of <span class="html-italic">Chenopodium quinoa</span> plants under drought, elevated temperature and combined factors and ambient (400 ppm, aCO<sub>2</sub>) and elevated (800 ppm, eCO<sub>2</sub>) CO<sub>2</sub> concentrations during cultivation. Control—plants growing at aCO<sub>2</sub> without treatment; D—drought treatment at aCO<sub>2</sub>; eT—elevated temperature treatment at aCO<sub>2</sub>; D+eT—combined treatment with drought and elevated temperature at aCO<sub>2</sub>; eCO<sub>2</sub>—plants growing at eCO<sub>2</sub> without treatment; eCO<sub>2</sub>+D—drought treatment at eCO<sub>2</sub>; eCO<sub>2</sub>+eT—elevated temperature treatment at eCO<sub>2</sub>; eCO<sub>2</sub>+D+eT—combined treatment with drought and elevated temperature at eCO<sub>2</sub>. The most significant parameter values for the first principal component (PC1) and the second principal component (PC2) are highlighted in bold.</p>
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<p>Changes in physiological, biochemical and molecular genetic parameters under individual and combined effects of drought, elevated temperatures and ambient (400 ppm, aCO<sub>2</sub>) and elevated (800 ppm, eCO<sub>2</sub>) CO<sub>2</sub> concentrations. D—drought treatment at aCO<sub>2</sub>; eT—elevated temperature treatment at aCO<sub>2</sub>; D+eT—combined treatment with drought and elevated temperature at aCO<sub>2</sub>; eCO<sub>2</sub>—plants growing at eCO<sub>2</sub> without treatment; eCO<sub>2</sub>+D—drought treatment at eCO<sub>2</sub>; eCO<sub>2</sub>+eT—elevated temperature treatment at eCO<sub>2</sub>; eCO<sub>2</sub>+D+eT—combined treatment with drought and elevated temperature at eCO<sub>2</sub>. Green means an increase in the parameters, red means a decrease in the parameters. Dark green and dark red mean a strong increase and decrease in parameters, respectively.</p>
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29 pages, 5195 KiB  
Article
Elevated Temperature Effects on Protein Turnover Dynamics in Arabidopsis thaliana Seedlings Revealed by 15N-Stable Isotope Labeling and ProteinTurnover Algorithm
by Kai-Ting Fan, Yuan Xu and Adrian D. Hegeman
Int. J. Mol. Sci. 2024, 25(11), 5882; https://doi.org/10.3390/ijms25115882 - 28 May 2024
Cited by 1 | Viewed by 1396
Abstract
Global warming poses a threat to plant survival, impacting growth and agricultural yield. Protein turnover, a critical regulatory mechanism balancing protein synthesis and degradation, is crucial for the cellular response to environmental changes. We investigated the effects of elevated temperature on proteome dynamics [...] Read more.
Global warming poses a threat to plant survival, impacting growth and agricultural yield. Protein turnover, a critical regulatory mechanism balancing protein synthesis and degradation, is crucial for the cellular response to environmental changes. We investigated the effects of elevated temperature on proteome dynamics in Arabidopsis thaliana seedlings using 15N-stable isotope labeling and ultra-performance liquid chromatography-high resolution mass spectrometry, coupled with the ProteinTurnover algorithm. Analyzing different cellular fractions from plants grown under 22 °C and 30 °C growth conditions, we found significant changes in the turnover rates of 571 proteins, with a median 1.4-fold increase, indicating accelerated protein dynamics under thermal stress. Notably, soluble root fraction proteins exhibited smaller turnover changes, suggesting tissue-specific adaptations. Significant turnover alterations occurred with redox signaling, stress response, protein folding, secondary metabolism, and photorespiration, indicating complex responses enhancing plant thermal resilience. Conversely, proteins involved in carbohydrate metabolism and mitochondrial ATP synthesis showed minimal changes, highlighting their stability. This analysis highlights the intricate balance between proteome stability and adaptability, advancing our understanding of plant responses to heat stress and supporting the development of improved thermotolerant crops. Full article
(This article belongs to the Special Issue Plant Omics: Sensing, Signaling, Regulation and Homeostasis)
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Graphical abstract

Graphical abstract
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<p>Scatter plots and normal Q-Q plots of all identified <span class="html-italic">Arabidopsis</span> peptides (top) vs. peptides selected with visual scores higher than 80, standard error lower than 10, and identified in at least three labeling time points (bottom). The panels on the left (<b>A</b>,<b>C</b>) are scatter plots of the standard error of log<sub>2</sub><span class="html-italic">k</span> (se.log<sub>2</sub><span class="html-italic">k</span>) against log<sub>2</sub><span class="html-italic">k</span>; the panels on the right (<b>B</b>,<b>D</b>) are normal Q-Q plots of each peptide’s turnover rate (log<sub>2</sub><span class="html-italic">k</span> values). This figure shows only the peptide data from the enriched shoot soluble fraction and includes data combined from both the control and heat treatment groups. The number of peptides is 10,400 (<b>A</b>,<b>B</b>) and 1273 (<b>C</b>,<b>D</b>). se, standard error.</p>
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<p>Peptide turnover rate distributions by tissue, fraction, and treatment. Histograms show peptide log<sub>2</sub><span class="html-italic">k</span> values plotted for enriched soluble, organelle, and microsomal fractions of root (<b>A</b>–<b>C</b>) or shoot (<b>D</b>–<b>F</b>) tissues. The control (ctrl) and 30 °C groups are plotted in the bottom and top frame, respectively. The <span class="html-italic">y</span>-axis is the number of peptide counts. The mean value is plotted as the dashed line in red. The bin width is 0.15 for all histograms.</p>
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<p>Protein turnover rate distributions by tissue, fraction, and treatment. Histograms show protein log<sub>2</sub><span class="html-italic">k</span> values plotted for enriched soluble, organelle, and microsomal fractions of root (<b>A</b>–<b>C</b>) or shoot (<b>D</b>–<b>F</b>) tissues. The control (ctrl) and 30 °C groups are plotted in the bottom and top frames, respectively. The <span class="html-italic">y</span>-axis is the number of protein counts. The median value is labeled and plotted as the dashed line in red. The bin width is 0.15 for all histograms.</p>
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<p>Box plots of the coefficient of variation (CV) of protein turnover rates plotted as a function of the number of peptide rates used in each calculation. The value of CV was calculated from the standard deviation of log<sub>2</sub><span class="html-italic">k</span> divided by the mean of log<sub>2</sub><span class="html-italic">k</span>. The dataset used in this plot analysis compromises both unique and shared peptides, separated according to the treatment groups: the control temperature (<b>A</b>) and the elevated temperature of 30 °C (<b>B</b>). Boxes show the interquartile range (IQR) of turnover rates of proteins. The error bar represents the entire range of rates, and the blue dots represent outliers (1.5 IQR). The number of data points in each <span class="html-italic">x</span>-axis category is given as <span class="html-italic">N</span>, below the <span class="html-italic">x</span>-axis of both plots.</p>
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<p>The distribution of changes in protein turnover rates across different tissues and enriched fractions in response to elevated temperature. (<b>A</b>) Histograms depict distributions of estimated changes in protein turnover rate (log<sub>2</sub><span class="html-italic">k</span>) in response to 30 °C, plotted for the soluble, organelle, and microsomal fractions of roots (top panel) or shoots (bottom panel), respectively. The bin width is 0.15 for all histograms. The median value is labeled and plotted as the dashed line in red. (<b>B</b>) Box plots of the estimated fold change in protein turnover rate constant (<span class="html-italic">k</span><span class="underline">)</span> in response to 30 °C of proteins identified in the root and shoot enriched soluble, organelle, and microsomal fractions. The analyzed data include only proteins with a significant change in log<sub>2</sub><span class="html-italic">k</span> (<span class="html-italic">p</span> &lt; 0.05) and at least one unique peptide identified in both the control and 30 °C groups, which was estimated using an LMM approach after peptide selection criteria were applied. Boxes show the interquartile range (IQR) of change in turnover rates <span class="html-italic">k</span>. The error bar represents the entire range of rates, and the closed circles represent outliers (1.5 IQR). The estimated changes in turnover rates were analyzed by Tukey’s HSD (Honest Significant Difference) test, with * for <span class="html-italic">p</span> &lt; 0.05, ** for <span class="html-italic">p</span> &lt; 0.01, and *** for <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>The relationship between protein function and protein turnover rates. Box plots of protein turnover rate log<sub>2</sub><span class="html-italic">k</span> for root (<b>A</b>) and shoot (<b>B</b>) proteins from the control experiment are sorted by functional categorization, adapted from the MapCave website (<a href="http://mapman.gabipd.org/web/guest/mapcave" target="_blank">http://mapman.gabipd.org/web/guest/mapcave</a>, accessed on 24 September 2014) using the TAIR10 database. Outliers are shown as closed circles. The used data include only proteins with at least two unique peptides. The number of proteins in each function category is given as <span class="html-italic">N</span> along the <span class="html-italic">y</span>-axis of both plots. The protein count of each function group is also labeled in the plot. Abbreviations: 2nd met, secondary metabolism; AA met, amino acid metabolism, C1-met, single carbon metabolism; cell, cell organization; cell wall, cell wall formation; CHO hydrolases, miscellaneous gluco-, galacto- and mannosidases; DNA, DNA processing; Glc-, Gal- and mannosidases, glucosyl-, galactosyl- and mannosyl- glycohydrolases; GNG, gluconeogenesis; GST, glutathione S-transferase metabolism; hormone met, hormone metabolism; lipid met, lipid metabolism; major CHO met, major carbohydrate metabolism; MIP, major-intrinsic proteins; MC ET/ATP syn, mitochondrial electron transport/ATP synthesis; N-met, nitrogen metabolism; OPP, oxidative pentose phosphate pathway; prot.assembly, protein assembly and cofactor ligation; prot.degrad, protein degradation; prot.folding; protein folding; prot.targeting, protein targeting; prot.PTM, protein post-translational modification; prot.syn, protein synthesis; PS.C2, photorespiration; PS.light, the light reaction of photosynthesis; PS.calvin cycle; the Calvin Cyle of photosynthesis; RNA, RNA processing; S-assimilation, sulfur assimilation; stress, stress responses; TCA, tricarboxylic acid cycle; transport, cellular transport; N/A, protein function not assigned.</p>
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<p>The relationship between protein function and change in turnover due to elevated temperature. Box plots of the estimated fold change in protein turnover rate constant (<span class="html-italic">k</span>) in response to 30 °C for root and shoot proteins are sorted by functional categorization, adapted from the MapCave website using the TAIR10 database. Outliers are shown as closed circles. (<b>A</b>) Enriched soluble fraction of root or shoot tissue homogenate. (<b>B</b>) Enriched organelle fraction of root or shoot tissue homogenate. (<b>C</b>) Enriched microsomal protein fraction of root or shoot tissue homogenate. The data include only proteins with a significant change in log<sub>2</sub><span class="html-italic">k</span> (<span class="html-italic">p</span> &lt; 0.05) and at least one unique peptide identified in both the control and 30 °C groups. The number of proteins in each function category is given as <span class="html-italic">N</span> along the <span class="html-italic">y</span>-axis of all plots. N/A, protein function not assigned.</p>
Full article ">Figure 7 Cont.
<p>The relationship between protein function and change in turnover due to elevated temperature. Box plots of the estimated fold change in protein turnover rate constant (<span class="html-italic">k</span>) in response to 30 °C for root and shoot proteins are sorted by functional categorization, adapted from the MapCave website using the TAIR10 database. Outliers are shown as closed circles. (<b>A</b>) Enriched soluble fraction of root or shoot tissue homogenate. (<b>B</b>) Enriched organelle fraction of root or shoot tissue homogenate. (<b>C</b>) Enriched microsomal protein fraction of root or shoot tissue homogenate. The data include only proteins with a significant change in log<sub>2</sub><span class="html-italic">k</span> (<span class="html-italic">p</span> &lt; 0.05) and at least one unique peptide identified in both the control and 30 °C groups. The number of proteins in each function category is given as <span class="html-italic">N</span> along the <span class="html-italic">y</span>-axis of all plots. N/A, protein function not assigned.</p>
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<p>Comparison of protein functions with the change in turnover rates in response to 30 °C between different protein fractions<b>.</b> Boxes show the interquartile range (IQR) of the estimated fold change in protein turnover rate constant (<span class="html-italic">k</span>). Proteins are sorted in functional categorization, comparing results between the enriched soluble, organelle, and microsomal fractions of root (<b>A</b>) or shoot (<b>B</b>) tissues. The error bar represents the entire range of rates, and the closed circles represent outliers (1.5 IQR). N/A, protein function not assigned.</p>
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16 pages, 751 KiB  
Review
Improving Crop Yield through Increasing Carbon Gain and Reducing Carbon Loss
by Palanivelu Vikram Karthick, Alagarswamy Senthil, Maduraimuthu Djanaguiraman, Kuppusamy Anitha, Ramalingam Kuttimani, Parasuraman Boominathan, Ramasamy Karthikeyan and Muthurajan Raveendran
Plants 2024, 13(10), 1317; https://doi.org/10.3390/plants13101317 - 10 May 2024
Cited by 1 | Viewed by 1487
Abstract
Photosynthesis is a process where solar energy is utilized to convert atmospheric CO2 into carbohydrates, which forms the basis for plant productivity. The increasing demand for food has created a global urge to enhance yield. Earlier, the plant breeding program was targeting [...] Read more.
Photosynthesis is a process where solar energy is utilized to convert atmospheric CO2 into carbohydrates, which forms the basis for plant productivity. The increasing demand for food has created a global urge to enhance yield. Earlier, the plant breeding program was targeting the yield and yield-associated traits to enhance the crop yield. However, the yield cannot be further improved without improving the leaf photosynthetic rate. Hence, in this review, various strategies to enhance leaf photosynthesis were presented. The most promising strategies were the optimization of Rubisco carboxylation efficiency, the introduction of a CO2 concentrating mechanism in C3 plants, and the manipulation of photorespiratory bypasses in C3 plants, which are discussed in detail. Improving Rubisco’s carboxylation efficiency is possible by engineering targets such as Rubisco subunits, chaperones, and Rubisco activase enzyme activity. Carbon-concentrating mechanisms can be introduced in C3 plants by the adoption of pyrenoid and carboxysomes, which can increase the CO2 concentration around the Rubisco enzyme. Photorespiration is the process by which the fixed carbon is lost through an oxidative process. Different approaches to reduce carbon and nitrogen loss were discussed. Overall, the potential approaches to improve the photosynthetic process and the way forward were discussed in detail. Full article
(This article belongs to the Special Issue Photosynthesis and Carbon Metabolism in Higher Plants and Algae)
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Graphical abstract

Graphical abstract
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<p>Various photorespiratory bypasses are designed in plants. Different colors indicate the photorespiratory bypass pathways (1–7). CBC—Calvin Benson Cycle; TSA—Tartronic Semialdehyde; 3-PG—Phosphoglycerate; RuBP—Ribulose-1,5- Bis Phosphate.</p>
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21 pages, 2044 KiB  
Review
Multiple Roles of Glycerate Kinase—From Photorespiration to Gluconeogenesis, C4 Metabolism, and Plant Immunity
by Leszek A. Kleczkowski and Abir U. Igamberdiev
Int. J. Mol. Sci. 2024, 25(6), 3258; https://doi.org/10.3390/ijms25063258 - 13 Mar 2024
Cited by 5 | Viewed by 2047
Abstract
Plant glycerate kinase (GK) was previously considered an exclusively chloroplastic enzyme of the glycolate pathway (photorespiration), and its sole predicted role was to return most of the glycolate-derived carbon (as glycerate) to the Calvin cycle. However, recent discovery of cytosolic GK revealed metabolic [...] Read more.
Plant glycerate kinase (GK) was previously considered an exclusively chloroplastic enzyme of the glycolate pathway (photorespiration), and its sole predicted role was to return most of the glycolate-derived carbon (as glycerate) to the Calvin cycle. However, recent discovery of cytosolic GK revealed metabolic links for glycerate to other processes. Although GK was initially proposed as being solely regulated by substrate availability, subsequent discoveries of its redox regulation and the light involvement in the production of chloroplastic and cytosolic GK isoforms have indicated a more refined regulation of the pathways of glycerate conversion. Here, we re-evaluate the importance of GK and emphasize its multifaceted role in plants. Thus, GK can be a major player in several branches of primary metabolism, including the glycolate pathway, gluconeogenesis, glycolysis, and C4 metabolism. In addition, recently, the chloroplastic (but not cytosolic) GK isoform was implicated as part of a light-dependent plant immune response to pathogen attack. The origins of glycerate are also discussed here; it is produced in several cell compartments and undergoes huge fluctuations depending on light/dark conditions. The recent discovery of the vacuolar glycerate transporter adds yet another layer to our understanding of glycerate transport/metabolism and that of other two- and three-carbon metabolites. Full article
(This article belongs to the Special Issue Plant Respiration in the Light and Photorespiration)
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<p>A simplified view of two pre-mRNAs produced from alternative promoters of a single Arabidopsis <span class="html-italic">GLYK</span> gene, coding for chloroplastic (chl<span class="html-italic">GLYK</span>) and cytosolic (cyt<span class="html-italic">GLYK</span>) isoforms of GK (modified from [<a href="#B35-ijms-25-03258" class="html-bibr">35</a>]). Boxes represent exons. Transit peptide coding sequence is shown in green. Positions of first and second ATGs in the <span class="html-italic">GLYK</span> coding sequence are also indicated. Light-dependent transcription of the gene is under phytochrome control and involves alternative promoter selection, resulting in two mRNAs coding for the chloroplastic and cytosolic isoforms of GK, respectively [<a href="#B35-ijms-25-03258" class="html-bibr">35</a>].</p>
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<p>Glycerate metabolism and the diurnal roles of GK in primary metabolism (in C<sub>3</sub> species). (<b>A</b>) The glycolate pathway during the day; (<b>B</b>) Cytoplasmic bypass of glycerate toward gluconeogenesis and glycolysis. (<b>C</b>) The role of the vacuole as a retainer for glycerate flux (day and night). Five compartments (marked with different colors) are involved. Abbreviations: CBB, Calvin–Benson–Bassham cycle; GAP, glyceraldehyde phosphate; GK<sub>chl</sub>, chloroplastic GK; GK<sub>cyt</sub>, cytosolic GK; HPR, hydroxypyruvate reductase; 2PG, 2-phosphoglycolate.</p>
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<p>Flow of glycerate from the bundle sheath cells to the mesophyll cells during C<sub>4</sub> photosynthesis. (1) 3PGA phosphatase; (2) Glycerate kinase. Abbreviations: CBB cycle, Calvin–Benson–Bassham cycle; Pi, inorganic phosphate.</p>
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<p>A cartoon-like comparison of amino acid (aa) sequences of potato GK chloroplastic and cytosolic isoforms. From aa #119 (chloroplastic GK) and aa #43 (cytosolic GK), the proteins share 100% identity. The proteins are produced by a light-dependent <span class="html-italic">GLYK</span> promoter selection mechanism (see <a href="#ijms-25-03258-f001" class="html-fig">Figure 1</a>). The chloroplastic GK, but not cytosolic GK, contains a transit peptide (in green) which directs the protein to chloroplasts, and is removed upon the transfer. The transit peptide is recognized by AVRvnt1, an effector protein produced by <span class="html-italic">Phytophthora infestans</span>. This triggers plant protein Rpi-vnt1.1, which is involved in plant immune defense.</p>
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<p>Consequences of light regulation of the GK gene on protein amount/activity of GK isoforms. Green and red arrows refer to an increase or decrease of GK protein/activity, respectively. The light/shade (or light/dark) regulation of the GK promotor affects the ratio of the chloroplastic to cytosolic GK isoforms, the former prevalent in the light, and the latter building up in shade/night conditions. pGK, plastidial GK; cGK, cytosolic GK.</p>
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17 pages, 3424 KiB  
Article
Genome-Wide Identification and Expression Analysis of FAR1/FHY3 Gene Family in Cucumber (Cucumis sativus L.)
by Xuelian Li, Yihua Li, Yali Qiao, Siting Lu, Kangding Yao, Chunlei Wang and Weibiao Liao
Agronomy 2024, 14(1), 50; https://doi.org/10.3390/agronomy14010050 - 23 Dec 2023
Cited by 1 | Viewed by 1504
Abstract
The FAR1-RELATED SEQUENCE1 (FAR1) and FAR-RED ELONGATED HYPOCOTYL3 (FHY3) gene family plays a crucial role in various physiological and developmental processes, including seed germination, photomorphogenesis, flowering and stress responses. However, genome analysis of FAR1/FHY3 in cucumber (Cucumis sativus [...] Read more.
The FAR1-RELATED SEQUENCE1 (FAR1) and FAR-RED ELONGATED HYPOCOTYL3 (FHY3) gene family plays a crucial role in various physiological and developmental processes, including seed germination, photomorphogenesis, flowering and stress responses. However, genome analysis of FAR1/FHY3 in cucumber (Cucumis sativus L.) has not been systemically investigated. In this study, 20 FAR1/FHY3 genes in cucumber were identified. The 20 FAR1/FHY3 members are randomly distributed on six chromosomes. The examination of subcellular localization indicated that the nucleus is the primary site where the 20 FAR1/FHY3 members are predominantly found. The analysis of the phylogenetic tree further revealed that the FAR1/FHY3 genes in cucumber are grouped into three distinct categories, exhibiting remarkable resemblance to the corresponding genes in other plant species. The analysis of cis-acting elements showed that most FAR1/FHY3 genes contain a variety of hormones as well as stress-related and light response elements. Through scrutinizing the expression patterns in various tissues, it was discerned that these genes are prominently expressed in roots, stems and leaves, with roots exhibiting the highest level of expression. Additionally, the 20 cucumber FAR1/FHY3 genes are all responsive to jasmonic acid methyl ester (Me-JA) and abscisic acid (ABA). CsFAR6 and CsFAR12 are significantly induced by Me-JA and ABA, respectively. CsFAR13 positively responds to NaCl and PEG6000 stresses. CsFAR11, CsFAR15 and CsFAR13 are significantly induced by the dark. The findings presented in this study establish compelling support for the potential involvement of FAR1/FHY3 genes in the growth, development and stress response of cucumbers. Moreover, these results serve as a solid basis for future investigations into the functional analysis of FAR1/FHY3. Full article
(This article belongs to the Topic Vegetable Breeding, Genetics and Genomics)
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<p>Structure analysis of the <span class="html-italic">FAR1/FHY3</span> gene in cucumber. MEGA11 was employed to construct the evolutionary tree utilizing the complete cucumber FAR1/FHY3 protein sequences. Furthermore, TBtools software was utilized to generate the exon-intron diagram for cucumber <span class="html-italic">FAR1/FHY3</span> genes.</p>
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<p>The distribution and composition of FAR1/FHY3 proteins in cucumber pertaining to motif occurrence. (<b>A</b>) Distinct motifs are represented by colored boxes. (<b>B</b>) Sequential stacks of letters illustrate the amino acid sequences associated with each motif. The overall height of the stack signifies the information content in bits of the respective amino acid at each position of the motif. The specific height of each letter within the stack is calculated by multiplying the probability of occurrence at that position and the total information content of the stack. Width and bits are depicted on the X and Y axes, respectively.</p>
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<p>The distribution of <span class="html-italic">FAR1/FHY3</span> gene family members of chromosomes in cucumber. Each vertical line prominently displayed the number of chromosomes, while the gene names were conveniently presented on the right side of the corresponding chromosome. Yellow: <span class="html-italic">FAR1/FHY3</span> Class I; purple: <span class="html-italic">FAR1/FHY3</span> Class II; red: <span class="html-italic">FAR1/FHY3</span> Class III; chr: chromosome.</p>
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<p>Evolutionary relationships of FAR1/FHY3 family in various species. To construct the phylogenetic tree, we employed the maximum likelihood method. This involved the inclusion of 20 cucumber (<span class="html-italic">Cucumis sativus</span> L.) FAR1/FHY3 proteins, 14 Arabidopsis (<span class="html-italic">Arabidopsis thaliana</span> L.) FAR1/FHY3 proteins, and 27 tomato (<span class="html-italic">Solanum lycopersicum</span> L.) FAR1/FHY3 proteins. The three subgroups are colored differently. The three differently colored shapes represent FAR1/FHY3 proteins from 3 species. The green circle, blue rectangle, and red triangle represent cucumber, Arabidopsis, and tomato FAR1/FHY3 proteins, respectively.</p>
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<p>The collinearity analysis of the <span class="html-italic">FAR1/FHY3</span> gene family was conducted in <span class="html-italic">Cucumis sativus</span> L. and Arabidopsis thaliana L. Syntenic <span class="html-italic">FAR1/FHY3</span> gene pairs are represented by the marked red lines.</p>
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<p>The quantities of elements in the <span class="html-italic">CsFAR1/FHY3</span> genes’ upstream regions spanning 2000 bp. The grid’s diverse colors and numbers represent the counts of distinct cis-acting regulatory elements within these <span class="html-italic">CsFAR1/FHY3</span> genes.</p>
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<p>Expression levels of the <span class="html-italic">FAR1/FHY3</span> gene were analyzed in the roots, stems, and leaves of cucumber. The expression patterns of the <span class="html-italic">CsFAR1</span> and <span class="html-italic">CsFAR20</span> genes in various tissues are illustrated in figures (<b>A</b>–<b>T</b>) correspondingly. The error bars signify the standard error, which was calculated based on three independent replicates. The relative expression of each gene in distinct tissues is presented as the mean ± SE (n = 3). Bars labeled with different lowercase letters indicate significant differences, as determined by Duncan’s multiple range tests (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Expression leaves of <span class="html-italic">FAR1/FHY3</span> genes in cucumber under ABA, Me-JA, NaCl, dark and PEG6000 treatments. Seedlings were treated with 100 µM ABA, 100 µM Me-JA, 50 mM NaCl, dark and 8% (<span class="html-italic">w</span>/<span class="html-italic">v</span>) PEG. The color scale represents the fold change normalized by log2 converted data. Blue shows down-regulated genes and red shows up-regulated genes.</p>
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19 pages, 9703 KiB  
Article
Physiological and RNA-Seq Analyses on Exogenous Strigolactones Alleviating Drought by Improving Antioxidation and Photosynthesis in Wheat (Triticum aestivum L.)
by Miao Song, Naiyue Hu, Sumei Zhou, Songxin Xie, Jian Yang, Wenqi Ma, Zhengkai Teng, Wenxian Liang, Chunyan Wang, Mingna Bu, Shuo Zhang, Xiwen Yang and Dexian He
Antioxidants 2023, 12(10), 1884; https://doi.org/10.3390/antiox12101884 - 20 Oct 2023
Cited by 5 | Viewed by 2267
Abstract
Drought poses a significant challenge to global wheat production, and the application of exogenous phytohormones offers a convenient approach to enhancing drought tolerance of wheat. However, little is known about the molecular mechanism by which strigolactones (SLs), newly discovered phytohormones, alleviate drought stress [...] Read more.
Drought poses a significant challenge to global wheat production, and the application of exogenous phytohormones offers a convenient approach to enhancing drought tolerance of wheat. However, little is known about the molecular mechanism by which strigolactones (SLs), newly discovered phytohormones, alleviate drought stress in wheat. Therefore, this study is aimed at elucidating the physiological and molecular mechanisms operating in wheat and gaining insights into the specific role of SLs in ameliorating responses to the stress. The results showed that SLs application upregulated the expression of genes associated with the antioxidant defense system (Fe/Mn-SOD, PER1, PER22, SPC4, CAT2, APX1, APX7, GSTU6, GST4, GOR, GRXC1, and GRXC15), chlorophyll biogenesis (CHLH, and CPX), light-harvesting chlorophyll A-B binding proteins (WHAB1.6, and LHC Ib-21), electron transfer (PNSL2), E3 ubiquitin-protein ligase (BB, CHIP, and RHY1A), heat stress transcription factor (HSFA1, HSFA4D, and HSFC2B), heat shock proteins (HSP23.2, HSP16.9A, HSP17.9A, HSP21, HSP70, HSP70-16, HSP70-17, HSP70-8, HSP90-5, and HSP90-6), DnaJ family members (ATJ1, ATJ3, and DJA6), as well as other chaperones (BAG1, CIP73, CIPB1, and CPN60I). but the expression level of genes involved in chlorophyll degradation (SGR, NOL, PPH, PAO, TIC55, and PTC52) as well as photorespiration (AGT2) was found to be downregulated by SLs priming. As a result, the activities of superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) were enhanced, and chlorophyll content and photosynthetic rate were increased, which indicated the alleviation of drought stress in wheat. These findings demonstrated that SLs alleviate drought stress by promoting photosynthesis through enhancing chlorophyll levels, and by facilitating ROS scavenging through modulation of the antioxidant system. The study advances understandings of the molecular mechanism underlying SLs-mediated drought alleviation and provides valuable insights for implementing sustainable farming practice under water restriction. Full article
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<p>Phenotype, leaf relative water content and leaf relative saturation deficit in different treatments plants. (<b>a</b>) the photos showed the water deficit phenotype of Zhoumai 28 and Luohan 22 under normal conditions (CK), drought stress in soil (T1), and drought stress in soil with SLs priming (T2) after 5 d of drought stress; the scale bar indicates 10 cm; (<b>b</b>) leaf relative water content in different treatments; (<b>c</b>) leaf relative saturation deficit in different treatments. V1 represents Zhoumai 28, and V2 represents Luohan 22. R1 indicates 1 d after rehydration treatment. Different letters indicate significant difference at <span class="html-italic">p</span> &lt; 0.05 according to one-way ANOVA followed by Duncan’s test. Data indicate mean ± SD (<span class="html-italic">n</span> = 3).</p>
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<p>Photosynthetic performance in different treatments plants. (<b>a</b>) net photosynthetic rate; (<b>b</b>) transpiration rate; (<b>c</b>) stomatal conductance; (<b>d</b>) intercellular CO<sub>2</sub> concentration. V1 represents Zhoumai 28, and V2 represents Luohan 22. R1 indicates 1 d after rehydration treatment. Different letters indicate significant difference at <span class="html-italic">p</span> &lt; 0.05 according to one-way ANOVA followed by Duncan’s test. Data indicate mean ± SD (<span class="html-italic">n</span> = 3).</p>
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<p>Antioxidant enzyme activity and H<sub>2</sub>O<sub>2</sub> content in leaves under different treatments. (<b>a</b>) SOD activity; (<b>b</b>) POD activity; (<b>c</b>) CAT activity; (<b>d</b>) H<sub>2</sub>O<sub>2</sub> content. V1 represents Zhoumai 28, and V2 represents Luohan 22. R1 indicates 1 d after rehydration treatment. Different letters indicate significant difference at <span class="html-italic">p</span> &lt; 0.05 according to one-way ANOVA followed by Duncan’s test. Data indicate mean ± SD (<span class="html-italic">n</span> = 3).</p>
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<p>GO and KEGG enrichment of 62 DEGs. (<b>a</b>) sankey dot plot of GO analysis based on biological process; (<b>b</b>) sankey dot plot of KEGG analysis. The dot size was based on the gene count enriched in the pathway, and the color of the dot showed the pathway enrichment significance. All enrichment results were selected with the significance threshold “<span class="html-italic">p</span> &lt; 0.05”.</p>
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<p>KEGG and GO enrichment of blue module by WGCNA. (<b>a</b>) KEGG enrichment analysis of blue module; (<b>b</b>) GO enrichment based on biological process of blue module; (<b>c</b>) GO enrichment based on molecular function of blue module; (<b>d</b>) GO enrichment based on cellular component of blue module. The dot size was based on the gene count enriched in the pathway, and the color of the dot showed the pathway enrichment significance. All enrichment results were selected with the significance threshold “<span class="html-italic">p</span> &lt; 0.05”.</p>
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<p>Expression differences of DEGs related to antioxidants in blue module based on WGCNA. (<b>a</b>) heatmap of the expression patterns of DEGs. FPKM values were normalized by Z-score. (<b>b</b>) qRT-PCR validation of key antioxidants genes. A, B, C, and D in coordinate maenad different sampling times, A = after water stress 0 d, B = after water stress 2 d, C = after water stress 5 d, D = rehydration for 1 d. R1 indicates 1 d after rehydration treatment. Different letters indicate significant difference at <span class="html-italic">p</span> &lt; 0.05 according to one-way ANOVA followed by Duncan’s test.</p>
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<p>Expression differences of DEGs related to chlorophyll metabolism and photosynthesis in blue module based on WGCNA. (<b>a</b>) heatmap of the expression patterns of DEGs. FPKM values were normalized by Z-score. (<b>b</b>) qRT-PCR validation of key chlorophyll metabolism and photosynthesis genes. A, B, C, and D in coordinate maenad different sampling times, A = after water stress 0 d, B = after water stress 2 d, C = after water stress 5 d, D = rehydration for 1 d. R1 indicates 1 d after rehydration treatment. Different letters indicate significant difference at <span class="html-italic">p</span> &lt; 0.05 according to one-way ANOVA followed by Duncan’s test.</p>
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<p>Expression differences of DEGs related to repair protein misfolding in blue module based on WGCNA. (<b>a</b>) heatmap of the expression patterns of DEGs. FPKM values were normalized by Z-score. (<b>b</b>) qRT-PCR validation of key repair protein misfolding genes. A, B, C, and D in coordinate maenad different sampling times, A = after water stress 0 d, B = after water stress 2 d, C = after water stress 5 d, D = rehydration for 1 d. R1 indicates 1 d after rehydration treatment. Different letters indicate significant difference at <span class="html-italic">p</span> &lt; 0.05 according to one-way ANOVA followed by Duncan’s test.</p>
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<p>Schematic illustration of a hypothetical mechanism for SLs-mediated drought alleviation in wheat. Note: the bold orange arrows indicate upregulated genes; the bold gray arrows indicate downregulated genes; thin orange arrows indicate increase; thin gray arrows indicate decrease.</p>
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14 pages, 4512 KiB  
Article
Effect of Drought on Photosynthesis of Trees and Shrubs in Habitat Corridors
by Josef Urban, Marie Matoušková, William Robb, Boleslav Jelínek and Luboš Úradníček
Forests 2023, 14(8), 1521; https://doi.org/10.3390/f14081521 - 26 Jul 2023
Cited by 2 | Viewed by 1758
Abstract
Drought and high evapotranspiration demands can jeopardise trees and shrubs in windbreaks and habitat corridors, where they are more exposed to the effects of extreme weather than in the forest. This study utilised chlorophyll fluorescence to assess how the leaf-level physiological processes of [...] Read more.
Drought and high evapotranspiration demands can jeopardise trees and shrubs in windbreaks and habitat corridors, where they are more exposed to the effects of extreme weather than in the forest. This study utilised chlorophyll fluorescence to assess how the leaf-level physiological processes of 13 woody species typically planted in Czech habitat corridors responded to the effects of naturally occurring drought and their ability to recover after rain. Linear electron flow (LEF) responded only weakly to the drought, indicating high levels of photorespiration. Trees and shrubs increased the proportion of energy which was dissipated in a harmless way (ΦNPQ) during drought and decreased the proportion of energy dissipated through non-regulated processes (ΦNO). In this way, they reduced processes potentially leading to the production of reactive oxygen species. All species except Tilia cordata Mill. maintained high ΦNPQ even after its release from drought. Tilia cordata was potentially the most susceptible tree to drought due to its low LEF and high ΦNO. The most drought-resistant tree species appeared to be Acer campestre L. and shrubs such as Prunus spinosa L., Viburnum lantana L, and Crataegus monogyna L. These shrubs may be planted at the sunny edges of habitat corridors. The woody species identified as resistant to drought in habitat corridors may also be considered resistant in a warming climate or suitable for planting in the urban environment which is generally warmer and drier than in a forest. Full article
(This article belongs to the Special Issue Advances in Tree Physiology and Ecology under Drought Stress)
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<p>Weather at the research plot in 2022. Upper panel: monthly means of air temperature and vapor pressure deficit (<span class="html-italic">VPD</span>). Lower panel: monthly sums of precipitation and the reference evapotranspiration.</p>
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<p>Volumetric soil moisture at the research plot. Three blue arrows indicate the days of measurement of photosynthesis.</p>
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<p>The relative concentration of chlorophyll in the leaves of trees and shrubs. Different letters indicate that the values of SPAD are significantly different. The box indicates the interquartile interval, where 50% of the data is found. The vertical line in the box indicates median. Data points outside this interval are represented as black points on the graph and considered potential outliers.</p>
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<p>The dependence of linear electron transport in PSII on the photosynthetically active radiation. The points are showing measured values, lines are modelled according to Equation (7) above.</p>
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<p>The portion of energy lost as non-photochemical quenching (<span class="html-italic">Φ</span><sub>NPQ</sub>). Points show measured values. Lines show modelled predictions and coloured bands show 95% confidence intervals.</p>
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<p>The portion of energy lost in a range of non-regulated processes (<span class="html-italic">Φ</span><sub>NO</sub>). Points show measured values. Lines show modelled predictions and coloured bands show 95% confidence intervals.</p>
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