Plant Photosynthesis Research Articles

Exploring Halophiles for Reclamation of Saline Soils: Biotechnological Interventions for Sustainable Agriculture.

Soil salinization is a major constraint on agricultural productivity, particularly in arid and semi-arid regions where limited rainfall cannot wash salts from plant root zones. This leads to disruptions in water uptake, ion balance, photosynthesis, respiration, nutrient absorption, hormone regulation and rhizosphere microbiome disturbances in plants. Chemical and biological methods can help mitigate soil salinity, but biological approaches, like using halophytes and salt-tolerant microorganisms, are preferred for environmental sustainability. Halophytes, however, represent only about 1% of flora and are habitat specific, so halophilic plant growth-promoting (PGP) microbes have emerged as a key eco-friendly solution. Halophilic PGP bacteria have shown promise in remediating saline soils, enhancing fertility and boosting crop resilience by inducing salinity tolerance (IST) and promoting plant growth traits. In the era of modern agriculture where chemical inputs are at their peak of application rendering the soil infertile, halophilic PGP bacteria represent a promising, sustainable approach to support food security, aligning with Sustainable Development Goals for zero hunger.

Protection of Photosynthesis by UVR8 and Cryptochromes in Arabidopsis Under Blue and UV Radiation.

Photosynthesis in plants is negatively affected by high light intensity and UV radiation. The photoreceptors UV RESISTANCE LOCUS 8 (UVR8) and CRYPTOCHROMES (CRYs) mediate perception and acclimation of plants to UV-B/UV-A2 (290-340 nm) and UV-A1/blue light (350-500 nm), respectively. However, their roles in photoprotection of photosynthesis across different wavebands of the spectrum remain unclear. Using chlorophyll fluorescence and LED lighting we studied the roles of UVR8 and CRYs in maintaining photosynthetic capacity in Arabidopsis exposed to UV-B, UV-A1, and blue light. Analysis of quantum yield of Photosystem II, nonphotochemical quenching, and LHCII phosphorylation demonstrated that CRYs preserve photosynthetic performance in plants exposed to UV-B, UV-A1, and blue light. UVR8 and CRYs exhibit partially redundant functions in maintaining photosynthetic activity under UV-B, UV-A1, and blue light, and in preventing photodamage under high UV-A1 irradiance. Impaired UVR8 and CRY signalling reduced epidermal flavonol accumulation in leaves, which further compromised photoprotection. These findings provide valuable insights into how UV and blue light perception contribute to photoprotection, with broad implications for plant performance both in natural and managed environments.

Hyperspectral Estimation of Tea Leaf Chlorophyll Content Based on Stacking Models

Chlorophyll is an essential pigment for photosynthesis in tea plants, and fluctuations in its content directly impact the growth and developmental processes of tea trees, thereby influencing the final quality of the tea. Therefore, achieving rapid and non-destructive real-time monitoring of leaf chlorophyll content (LCC) is beneficial for precise management in tea plantations. In this study, derivative transformations were first applied to preprocess the tea hyperspectral data, followed by the use of the Stable Competitive Adaptive Reweighted Sampling (SCARS) algorithm for feature variable selection. Finally, multiple individual machine learning models and stacking models were constructed to estimate tea LCC based on hyperspectral data, with a particular emphasis on analyzing how the selection of base models and meta-models affects the predictive performance of the stacking models. The results indicate that derivative processing enhances the sensitivity of hyperspectral data to tea LCC; furthermore, compared with individual machine learning models, the stacking models demonstrate superior predictive accuracy and generalization ability. Among the 17 constructed stacking configurations, when the meta-model is fixed, the predictive performance of the stacking model improves continuously with an increase in the number and accuracy of the base models and with a decrease in the structural similarity among the selected base models. Therefore, when constructing stacking models, the base model combination should comprise various models with minimal structural similarity while ensuring robust predictive performance, and the meta-model should be chosen as a simple linear or nonlinear model.

Estimating canopy leaf angle from leaf to ecosystem scale: a novel deep learning approach using unmanned aerial vehicle imagery.

Leaf angle distribution (LAD) impacts plant photosynthesis, water use efficiency, and ecosystem primary productivity, which are crucial for understanding surface energy balance and climate change responses. Traditional LAD measurement methods are time-consuming and often limited to individual sites, hindering effective data acquisition at the ecosystem scale and complicating the modeling of canopy LAD variations. We present a deep learning approach that is more affordable, efficient,automated, and less labor-intensive than traditional methods for estimating LAD. The method uses unmanned aerial vehicle images processed with structure-from-motion point cloud algorithms and the Mask Region-based convolutional neural network. Validation at the single-leaf scale using manual measurements acrossthree plant species confirmed high accuracy of the proposed method (Pachira glabra: R2 = 0.87, RMSE = 7.61°; Ficus elastica: R2 = 0.91, RMSE = 6.72°; Schefflera macrostachya: R2 = 0.85, RMSE = 5.67°). Employing this method, we efficiently measured leaf angles for 57 032 leaves within a 30 m × 30 m plot, revealing distinct LAD among four representative tree species: Melodinus suaveolens (mean inclination angle 34.79°), Daphniphyllum calycinum (31.22°), Endospermum chinense (25.40°), and Tetracera sarmentosa (30.37°). The method can efficiently estimate LAD across scales, providing critical structural information of vegetation canopy for ecosystem modeling, including species-specific leaf strategies and their effects on light interception and photosynthesis in diverse forests.

Comprehensive analysis of the effects on photosynthesis and energy balance in tomato leaves under magnesium deficiency.

Comprehensive analysis of the effects on photosynthesis and energy balance in tomato leaves under magnesium deficiency.

Effect of goji berry rootstock grafting on growth and physiological metabolism of tomato under high-temperature stress.

Effect of goji berry rootstock grafting on growth and physiological metabolism of tomato under high-temperature stress.

Root-zone oxygen supply mitigates waterlogging stress in tomato by enhancing root growth, photosynthetic performance, and antioxidant capacity.

Root-zone oxygen supply mitigates waterlogging stress in tomato by enhancing root growth, photosynthetic performance, and antioxidant capacity.

Biosynthesized Fe-C-dots nanozymes modulate growth, physiological and phytochemical peculiarity to improve saline-alkaline stress tolerance in wheat.

Biosynthesized Fe-C-dots nanozymes modulate growth, physiological and phytochemical peculiarity to improve saline-alkaline stress tolerance in wheat.

Phosphate-modified biochar attenuates cadmium availability in contaminated soil and reduces its transfer to tomato fruits.

Phosphate-modified biochar attenuates cadmium availability in contaminated soil and reduces its transfer to tomato fruits.

Bio-Inspired Optimization Through Photosynthesis: A Novel Approach for Balancing Exploration and Exploitation in Complex Systems

This paper presents the Photosynthesis-Inspired Optimization (PSIO) algorithm, an innovative metaheuristic that emulates the adaptive efficiency of plant photosynthesis. Inspired by chlorophyll’s dual role in light absorption and energy conversion, PSIO operates through two core mechanisms: the Photo-Intensification Mechanism, which focuses on refining high-potential solutions, and the Photosynthetic Pathway Diversification, which promotes thorough exploration of the solution space. These interconnected strategies enable PSIO to maintain a dynamic balance between exploration and exploitation, reducing the likelihood of premature convergence - a common challenge in complex optimization problems. Additionally, the algorithm incorporates an adaptive adjustment mechanism, akin to the photoprotective responses in plants, enhancing its flexibility and robustness across various optimization scenarios. The effectiveness of PSIO is validated through extensive benchmarking, consistently outperforming conventional metaheuristics in both convergence speed and solution quality. More specifically, PSIO consistently achieves lower total transmission costs with improvements ranging from $10\%$ to $25\%$ for small and large-scale problems.

Proteomic analysis of oat (Avena sativa L.) under drought stress using tandem mass tag labeling.

Drought is a major abiotic stress that limits oat growth. This study investigated the phenotypic, physiological, and proteomic differences between drought-resistant (Grain King [G]) and drought-susceptible (XiYue [X]) oat varieties under drought stress (soil water content of 15% ± 5% of field water-holding capacity) and normal conditions (soil water content of 75% ± 5% of field water-holding capacity). Phenotypic analysis showed that plant height, aboveground biomass, and underground biomass decreased under drought stress in both varieties, with variety X exhibiting a greater reduction. Physiological analysis revealed increased malondialdehyde content, soluble sugar (SS) content, and superoxide dismutase (SOD) and peroxidase (POD) activities in both varieties under drought stress, though variety X showed smaller increases. Proteomic analysis identified 151 differentially accumulated proteins (DAPs) in variety G and 792 in variety X. Further analyses showed that the DAPs in variety G, which were highly correlated with POD and SOD activities and SS content, were primarily involved in energy metabolism, protein translation, RNA processing, amino acid metabolism, and protein folding. Conversely, in variety X, the DAPs were mainly associated with RNA processing, protein stabilization, plant photosynthesis, intracellular signal transduction, and protein folding. Further analysis suggested that variety G significantly upregulated proteases related to photosynthesis, catalysts involved in citrulline synthesis, temperature-induced lipid transport proteins, fibrillin proteins linked to stress tolerance signal transduction and response, and shearing factors involved in mRNA shearing-proteins that were not significantly upregulated in variety X. These proteins may play essential roles in protecting oats from drought stress. Overall, this research elucidates the drought resistance mechanisms of different oat varieties at the protein level.

Evaluating photosynthetic models and their potency in assessing plant responses to changing oxygen concentrations: a comparative analysis of A n-C a and A n-C i curves in Lolium perenne and Triticum aestivum.

Accurate determination of photosynthetic parameters is essential for understanding how plants respond to environmental changes. In this study, we evaluated the performance of the Farquhar-von Caemmerer-Berry (FvCB) model and introduced a novel model to fit photosynthetic rates against ambient CO2 concentration (A n -C a) and intercellular CO2 concentration (A n -C i) curves for Lolium perenne and Triticum aestivum under 2% and 21% O2 conditions. We observed significant discrepancies in the FvCB model's fitting capacity for A n -C a and A n -C a curves across different oxygen regimes, particularly in estimates of key parameters such as the maximum carboxylation rate (V cmax), the day respiratory rate (R day), and the maximum electron transport rate for carbon assimilation (J A-max). Notably, under 2% and 21% O2 conditions, the values of V cmax and R day derived from A n -C a curves using the FvCB model were 46.98%, 44.37%, 46.63%, and 37.66% lower than those from A n -C i curves for L. perenne, and 47.10%, 44.30%, 47.03%, and 37.36% lower for T. aestivum, respectively. These results highlight that the FvCB model yields significantly different V cmax and R day values when fitting A n -C a versus A n -C i curves for these two C3 plants. In contrast, the novel model demonstrated superior fitting capabilities for both A n -C a and A n -C i curves under 2% and 21% O2 conditions, achieving high determination coefficients (R 2≥ 0.989). Key parameters such as the maximum net photosynthetic rate (A max) and the CO2 compensation point (Γ) in the presence of R day, showed no significant differences across oxygen concentrations. However, the apparent photorespiratory rate (R pa0) and photorespiratory rate (R p0) derived from A n -C i curves consistently exceeded those from A n -C a curves for both plant species. Furthermore, R pa0 values derived from A n -C a curves closely matched observed values, suggesting that A n -C a curves more accurately reflect the physiological state of plants, particularly for estimating photorespiratory rates. This study underscores the importance of selecting appropriate CO2-response curves to investigate plant photosynthesis and photorespiration under diverse environmental conditions, thereby ensuring a more accurate understanding of plant responses to changing environments.

High-Photon-Harvesting Nanophotofertilizers for Plant Growth Multiregulation.

Nanophotoresponsive technology has emerged as a promising way to enhance plant photosynthesis, but it faces limitations in light absorption and electron transfer efficiency. This study presents a photoresponsive nanosystem, LDNPs@Fe,Cu-CDs, combining lanthanide-doped nanoparticles (LDNPs) and Fe/Cu dual single-atom-doped carbon dots (CDs). Fabricated via hydrothermal synthesis, the nanosystem can regulate plant growth through light absorption, photothermal effects, photoelectron generation, and photocatalysis. Using a simple surface spraying method, the LDNPs@Fe,Cu-CDs can be absorbed by leaves and transported into N. benthamiana. LDNPs@Fe,Cu-CDs can harvest both near-infrared and ultraviolet light for photosynthesis and promote electron transfer in the photosynthetic chain by 33.2%. The nanosystem increased chlorophyll levels by 28.4% and enhanced photosynthesis by 67.5%. Additionally, it can alleviate the limitations of reactive oxygen species and cold environments, improving plant growth. The wet and dry weight of N. benthamiana were increased by 57.7% and 50.5%, respectively. LDNPs@Fe,Cu-CDs show great potential as a "nanophotofertilizer" for agricultural applications.

BcAS2 Regulates Leaf Adaxial Polarity Development in Non-Heading Chinese Cabbage by Directly Activating BcPHB Transcription.

Leaves are the primary organs for plant photosynthesis, and their flat, symmetric morphology is crucial for plant growth and development. The LBD family transcription factor ASYMMETRIC LEAVES 2 (AS2) plays a central role in the establishment of leaf polarity. In this study, we cloned the BcAS2 gene from the non-heading Chinese cabbage cultivar "NHCC001" and successfully generated overexpression strains through genetic transformation. Phenotypic analysis revealed that overexpression of BcAS2 led to significant upward curling of leaves in non-heading Chinese cabbage. Additionally, we found that the expression of BcPHB, a gene associated with leaf adaxial polarity development, was significantly up-regulated in BcAS2-overexpressing plants compared to controls. This interaction was further confirmed through yeast one-hybridization (Y1H), dual-luciferase reporter assays, and electrophoretic mobility shift assay (EMSA), all of which demonstrated that BcAS2 directly binds to the GATA-motif site of the BcPHB promoter and promotes its transcription. Functional validation via overexpression and silencing of BcPHB confirmed its role in regulating adaxial polarity development in non-heading Chinese cabbage leaves. This study elucidates the molecular mechanism of the BcAS2-BcPHB pathway in regulating leaf polarity in non-heading Chinese cabbage, providing a theoretical foundation for morphological improvement breeding.

Enhancing plant drought tolerance through exogenous nitric oxide: a comprehensive meta-analysis

BackgroundDrought stress severely impacts plant growth and agricultural productivity, necessitating strategies to enhance drought tolerance. This meta-analysis synthesizes data from 48 peer-reviewed studies to evaluate the effects of exogenous nitric oxide (NO) on plant growth, photosynthesis, antioxidant defense, and osmoregulation under drought conditions.ResultsResults show that NO significantly improves shoot length, root length, shoot dry weight, and root dry weight by 66.60%, 29.38%, 26.71%, and 16.17%. Photosynthetic rate, stomatal conductance, intercellular CO₂, Leaf relative water content, total chlorophyll, chlorophyll a and b was also improved by 17.98%, 67.95%, 12.12%, 10.20%, 19.68%, 52.26%, and 39.91%, respectively. Antioxidant enzyme activities, including superoxide dismutase, catalase, peroxidase, and ascorbate peroxidase, were significantly elevated by 13.69%, 22.60%, 16.98%, and 19.33%, respectively. Oxidative stress markers, including hydrogen peroxide, superoxide, and malondialdehyde, were reduced by 18.63%, 22.01%, and 18.22%, respectively. Osmotic regulators, including proline, soluble sugars, and soluble proteins, were significantly increased by 17.01%, 18.34%, and 30.40%. Subgroup analyses reveal that NO's effectiveness is influenced by environmental factors, plant species, and application methods.ConclusionsThis meta-analysis confirms that exogenous NO significantly improves the growth, photosynthetic efficiency, antioxidant defense, and osmotic regulation of plant under drought stress. The heterogeneity of NO's effects under different conditions highlights the importance of improving application methods, concentrations, and environmental conditions. These findings encourage focused research and application strategies to maximize the benefits of NO in enhancing crop resilience, and promoting sustainable agricultural practices.

Comparison of different photosynthetic pathways as tools for agriculture improvement in aquaponics.

Plants use photosynthesis to build biomass and different mechanisms of photosynthesis have evolved. The common C3 pathway has several drawbacks, which are a limitation for global agriculture. The C2 pathway has evolved to harness photorespiration to increase CO2 for the Calvin cycle, while allowing stomata to close more often to limit water loss. C2 photosynthesis is a bioengineering target for improving agricultural, but is largely untested due to species rarity. We tested leaf gas exchange, stomatal anatomy and biomass production between arugula species with different photosynthetic pathways (C2 - Diplotaxis tenuifolia and C3 -Eruca sativa) in a sustainable aquaponics system. Aquaponics provide plants with nutrients and water from fish efflux, reducing water and nutrient stress typical of traditional agriculture. We also grew individuals of each species in soil for comparison with aquaponics. We hypothesized that species in aquaponics would increase stomatal conductance, resulting in higher rates of photosynthesis and growth. We also hypothesized that C2 plants would outperform C3 plants in aquaponics compared to soil because of the potential capacity to better optimize stomatal behavior. For both species, increases in stomatal conductance were detected in aquaponics plants. Additionally, Diplotaxis tenuifolia (C2) exhibited increased stomatal density in aquaponics compared to soil, while Eruca sativa (C3) did not. Surprisingly, this did not lead to increased photosynthesis nor biomass improvement in aquaponics plants for either species. More work is needed, including additional leaf anatomical and stoichiometric data, to evaluate the efficacy of using the C2 pathway as a tool to improve agriculture.

Effects of nanoplastics and compound pollutants containing nanoplastics on plants, microorganisms and rhizosphere systems: A review.

Effects of nanoplastics and compound pollutants containing nanoplastics on plants, microorganisms and rhizosphere systems: A review.

Progress on layered double hydroxides as green materials in sustainable agricultural production.

Progress on layered double hydroxides as green materials in sustainable agricultural production.

Karakteristik Morfo-Anatomi serta Kandungan Klorofil dan Kandungan Fitokimia Tanaman Ketul (Bidens pilosa) pada Tempat dengan Intensitas Cahaya yang Berbeda di Arboretum Universitas Padjadjaran

Bidens pilosa is a plant known for its secondary metabolites with therapeutic properties. This study aimed to examine the effects of different light intensities on the morphology, anatomy, chlorophyll content, and secondary metabolite production of B. pilosa in the Arboretum of Universitas Padjadjaran. The shaded and unshaded area had light intensities of 5828 and 32768 lux, respectively. Leaf samples were collected from three different individuals in each area, with three leaves taken per plant. Morphological and anatomical traits such as leaf thickness, leaf area, stomatal density, and chlorophyll content were observed, along with secondary metabolite content. Results showed that plants in the shaded area had thinner leaves (0,19 mm) and lower stomatal density (275,16 cells/mm2) but a larger leaf area (4 cm2) and higher chlorophyll content (29,33 CCI) compared to leaves in the unshaded area, which had thicker leaves (0,213 mm), higher stomatal density (310,83 cells/mm2), a smaller leaf area (2,33 cm2), and lower chlorophyll content (27,27 CCI). Alkaloids, flavonoids, tannins, and saponins were detected in both conditions, with a higher level of alkaloid and tannin in unshaded plants. These findings enhance our understanding of the relationship between light intensity influences plant morphology, photosynthesis, and secondary metabolite production, with potential applications in cultivation and medicinal use.

Physiological, Cytological and Transcriptome Analysis of a Yellow-Green Leaf Mutant in Magnolia sinostellata.

Leaf color mutants serve as excellent models for investigating the metabolic pathways involved in chlorophyll biosynthesis, chloroplast development, and photosynthesis in plants. This study aimed to elucidate the mechanisms underlying color formation in the yellow-green leaf mutant (YL) of Magnolia sinostellata by employing physiological, cytological and transcriptomic analyses to compare the mutant with control plants (wild type Magnolia sinostellata, WT). Physiological assessments revealed a reduction in chlorophyll content, particularly chlorophyll b, alongside an increase in the flavonoid level in YL relative to WT. Cytological examinations indicated the presence of defective chloroplasts within the mesophyll cells of the mutants. Transcriptomic analysis identified 8205 differentially expressed genes, with 4159 upregulated and 4046 downregulated. Genes associated with chlorophyll metabolism, flavonoid metabolism, photosynthesis, and signaling pathways were found to play crucial roles in leaf yellowing. In conclusion, this study delineated the phenotypic, physiological, cytological, and transcriptomic differences between YL and WT leaves, offering novel insights into the mechanisms driving leaf yellowing in Magnolia sinostellata.