Frontiers in Plant Science最新文献

Global climate change, characterized by increased frequency and intensity of extreme temperature events, poses significant challenges to plant survival and crop productivity. While considerable research has elucidated plant responses to temperature stress, the molecular mechanisms, particularly those involved in temperature sensing, remain incompletely understood. Thermosensors in plants play a crucial role in translating temperature signals into cellular responses, initiating the downstream signaling cascades that govern adaptive processes. This review highlights recent advances in the identification and classification of plant thermosensors, exploring their physiological roles and the biochemical mechanisms by which they sense temperature changes. We also address the challenges in thermosensor discovery and discuss emerging strategies to uncover novel thermosensory mechanisms, with implications for improving plant resilience to temperature stress in the face of a rapidly changing climate.

This study examined the effects of nitrogen (N) application rates and weak light treatment post anthesis on the grain yield and starch physicochemical characteristics of soft wheat. The soft wheat varieties Quanmai 725 (QM725) and Yangmai 15 (YM15) were used as study materials under field conditions, and the experiments were conducted during 2022-2023. During the grain filling stage (7-35 days post anthesis), three shading levels were set: 10% shading (S1), 20% shading (S2) and 30% shading (S3), with natural light conditions used as the control (CK). In 2023-2024, two N application rates (120 kg/hm² [N1] and 180 kg/hm² [N2]) and the abovementioned three shading treatments for each N application rate were set during the filling stage. The effects of weak light treatment post anthesis on the grain yield and yield components of soft wheat were analyzed. Moreover, the mitigation effects of different N application rates on the grain yield and starch physicochemical characteristics of wheat were examined. The results showed that N application increased wheat yield and yield components as well as the content of starch and its components, whereas weak light treatment decreased these parameters under the same N application rate. Under N1 and N2 conditions, weak light treatment post anthesis significantly reduced the volume, surface area percentage and number of B-type starch granules (particle size ≤10 μm) and increased those of A-type starch granules (particle size >10 μm). Enhanced N application rates significantly improved the gelatinization characteristics and thermodynamic characteristics of wheat starch. Under the same conditions of N1 and N2, weak light treatment significantly reduced the gelatinization characteristics of wheat starch, such as peak viscosity, trough viscosity and final viscosity. Although the enthalpy of wheat starch was increased, its onset temperature, peak temperature and end temperature were significantly reduced, which affected the quality of wheat grains and eventually led to a decrease in wheat yield. However, enhanced N application rates increased the grain yield and starch physicochemical characteristics of wheat. Under the same N application rate, weak light treatment post anthesis reduced the content of starch and its components in wheat grains, which in turn affected the wheat grain weight. The effect was more pronounced in wheat B-type starch granules than in A-type starch granules.

Introduction: The openness grading of fresh-cut roses relies heavily on manual work, which can be inefficient and inconsistent.

Methods: In this study, an improved YOLOv8s model is proposed for openness grading in conjunction with a newly developed automatic grading machine for fresh-cut roses. The model identifies unopened inner petals and classifies openness into five levels: degree 1, degree 2, degree 3, degree 4, and deformity. To enhance detection accuracy while reducing the model complexity and computation, the backbone network of YOLOv8s is replaced by MobileNetV3. Additionally, an Efficient Multi-scale Attention (EMA) module is introduced to enhance focus on critical features, and a Wise-IoU loss function is incorporated to accelerate convergence.

Results: Field experiments revealed that the openness predictions made by the automatic fresh-cut roses grader had errors of 6.9%, 9.1%, 10.0%, 6.5%, and 12.6%, respectively, compared to manual predictions.

Discussion: Therefore, the improved YOLOv8s-F model effectively meets the requirements of fresh-cut rose openness grading.

Introduction: Plant height is an important agronomic trait that not only affects crop yield but is also related to crop resistance to abiotic and biotic stresses.

Methods: In this study, we analyzed the differentially expressed genes (DEGs) and differentially accumulated metabolites (DAMs) between Brazilian banana and local dwarf banana (Df19) through transcriptomics and metabolomics, and combined morphological differences and endogenous hormone content to analyze and discuss themolecular mechanisms controlling banana height.

Results: Sequencing data showed that a total of 2851 DEGs and 1037 DAMs were detected between Brazilian banana and local dwarf banana (Df19). The main differential biological pathways of DEGs involve plant hormone signaling transduction, Cutin, suberin and wax biosynthesis, phenylpropanoid biosynthesis, mitogen-activated protein kinase (MAPK) signaling pathway in plants, amino sugar and nucleotide sugar metabolism, etc. DAMs were mainly enriched in ATP binding cassette (ABC) transporters, amino and nucleotide sugar metabolism, glycerophospholipid metabolism, lysine degradation, and phenylalanine metabolism.

Discussion: Our analysis results indicate that banana plant height is the result of the synergistic effects of hormones such as abscisic acid (ABA), gibberellic acid (GA3), indole-3-acetic acid (IAA), jasmonic acid (JA), brassinosteroids (BR) and other plant hormones related to growth. In addition, transcription factors and ABC transporters may also play important regulatory roles in regulating the height of banana plants.

Introduction: Flax (Linum usitatissimum) is an important industrial crop in temperate regions, but fungal diseases, especially those caused by Fusarium oxysporum sp. lini, pose a serious risk. These infections can lead to major crop losses, reducing interest in flax cultivation.

Methods: This study investigated the effects of exogenous spermidine (Spd) on the interactions between flax and Fusarium oxysporum sp. lini. Flax plants treated with either 10 mM or 100 mM Spd were monitored for changes in polyamine levels, gene expression, and hydrogen peroxide (H₂O₂) content following infection.

Results and discussion: Notably, plants treated with 10 mM Spd showed enhanced resistance, exhibiting better phenotypic health and lower fungal murein levels, especially in shoots. Chitinase expression in these plants remained similar to or lower than control levels, suggesting minimal additional defence activation was required. Additionally, a marked ROS burst occurred two days post-infection, followed by redox balance restoration, indicating a controlled defence response. These results suggest that moderate Spd treatment improves flax resilience against fusarium wilt while avoiding excessive defence activation, highlighting Spd's potential for sustainable crop protection strategies.

Macadamia integrifolia, a perennial evergreen crop valued for its nutritious nuts, also exhibits a diverse range of inflorescence colors that possess both ornamental and biological significance. Despite the economic importance of macadamia, the molecular mechanisms regulating flower coloration remain understudied. This study employed a combination of metabolomic and biochemical approaches to analyze metabolites present in inflorescences from 11 Macadamia cultivars, representing distinct color phenotypes. A total of 787 metabolites were identified through the use of ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), the majority of which were phenolic acids, flavonoids, and flavonols. Principal component analysis and clustering yielded a classification of the samples into three major flower color groups. The differential metabolites were found to be enriched in pathways such as flavonoid, flavonol, and phenylpropanoid biosynthesis, which have been demonstrated to be key contributors to color variation. Moreover, weighted gene co-expression network analysis (WGCNA) identified metabolite modules that were strongly associated with specific flower colors. This revealed that key compounds, including kaempferol, quercetin derivatives, and anthocyanins, were the primary drivers of pigmentation. This study provides a comprehensive framework for understanding the genetic, biochemical, and environmental factors influencing macadamia flower color. These findings contribute to the theoretical understanding of macadamia reproductive biology and have practical implications for molecular breeding, ornamental enhancement, and optimizing pollinator attraction to improve crop yield and ecological sustainability.

The spread of cultivated grapevine from primary centers of origin is inevitably accompanied by the range expansion of its pathogens, including viruses. A limited number of wild Vitis vinifera L. ssp. sylvestris (Gmelin) Hegi populations have survived in the centers of grapevine domestication and can be used for comprehensive studies. We analyzed 50 grapevines collected in protected areas of the Black Sea region, which belong to the Caucasian domestication center. Based on genotyping of grapevines using simple sequence repeats as DNA markers, we determined the phylogenetic placement of V. vinifera ssp. sylvestris from the Black Sea region compared to cultivated and wild grapevines of the world. Using high-throughput sequencing of total RNA, we obtained the viromes of these grapevines. Ten viruses and one viroid were identified. The most common viruses detected were Vitis cryptic virus, grapevine rupestris stem pitting-associated virus, grapevine Pinot gris virus, and grapevine virus T. Among the economically significant viruses, we identified grapevine leafroll-associated virus 1 and grapevine virus A. A total of 91 complete or nearly complete virus genomes and one viroid genome were assembled, and phylogenetic analysis was performed. Two novel (+) ssRNA viruses were discovered, tentatively named Abrau grapevine-associated virus in the order Hepelivirales and Taurida grapevine-associated virus in the order Picornavirales. It is important to comprehensively consider the phylogeography of both viruses and their plant hosts. This is the first study that simultaneously addresses the population genetics of V. vinifera ssp. sylvestris from the Caucasian domestication center and its viruses.

The plant cell wall (CW) is more than a structural barrier; it serves as the first line of defence against pathogens and environmental stresses. During pathogen attacks or physical damage, fragments of the CW, known as CW-derived Damage-Associated Molecular Patterns (CW-DAMPs), are released. These molecular signals play a critical role in activating the plant's immune responses. Among CW-DAMPs, oligogalacturonides (OGs), fragments derived from the breakdown of pectin, are some of the most well-studied. This review highlights recent advances in understanding the functional and signalling roles of OGs, beginning with their formation through enzymatic CW degradation during pathogen invasion or mechanical injury. We discuss how OGs perception triggers intracellular signalling pathways that enhance plant defence and regulate interactions with microbes. Given that excessive OG levels can negatively impact growth and development, we also examine the regulatory mechanisms plants use to fine-tune their responses, avoiding immune overactivation or hyper- immunity. As natural immune modulators, OGs (and more generally CW-DAMPs), offer a promising, sustainable alternative to chemical pesticides by enhancing crop resilience without harming the environment. By strengthening plant defences and supporting eco-friendly agricultural practices, OGs hold great potential for advancing resilient and sustainable farming systems.

Introduction: In the context of intelligent strawberry cultivation, achieving multi-stage detection and yield estimation for strawberry fruits throughout their full growth cycle is essential for advancing intelligent management of greenhouse strawberries. Addressing the high rates of missed and false detections in existing object detection algorithms under complex backgrounds and dense multi-target scenarios, this paper proposes an improved multi-stage detection algorithm RLK-YOLOv8 for greenhouse strawberries. The proposed algorithm, an enhancement of YOLOv8, leverages the benefits of large kernel convolutions alongside a multi-stage detection approach.

Method: RLK-YOLOv8 incorporates several improvements based on the original YOLOv8 model. Firstly, it utilizes the large kernel convolution network RepLKNet as the backbone to enhance the extraction of features from targets and complex backgrounds. Secondly, RepNCSPELAN4 is introduced as the neck network to achieve bidirectional multi-scale feature fusion, thereby improving detection capability in dense target scenarios. DynamicHead is also employed to dynamically adjust the weight distribution in target detection, further enhancing the model's accuracy in recognizing strawberries at different growth stages. Finally, PolyLoss is adopted as the loss function, which effectively improve the localization accuracy of bounding boxes and accelerating model convergence.

Results: The experimental results indicate that RLK-YOLOv8 achieved a mAP of 95.4% in the strawberry full growth cycle detection task, with a precision and F1-score of 95.4% and 0.903, respectively. Compared to the baseline YOLOv8, the proposed algorithm demonstrates a 3.3% improvement in detection accuracy under complex backgrounds and dense multi-target scenarios.

Discussion: The RLK-YOLOv8 exhibits outstanding performance in strawberry multi-stage detection and yield estimation tasks, validating the effectiveness of integrating large kernel convolutions and multi-scale feature fusion strategies. The proposed algorithm has demonstrated significant improvements in detection performance across various environments and scenarios.