Frontiers in Plant Science最新文献

Introduction: Chromatin accessibility is broadly implicated in plant abiotic stress responses; nevertheless, its role under cold stress in upland cotton (Gossypium hirsutum) remains largely unexplored.

Methods: Here, we integrated the transcriptomic, metabolomic, and ATAC-seq profiles of a cold-tolerant line, Xinluzao 52 (X52), and a cold-sensitive line, Dai 4554 (D4554), which were sampled before (0 h) and after (6 h) cold treatment.

Results: Compared with the respective 0-h controls, the 6-h cold exposure group had specifically enriched differentially expressed genes (DEGs) related to the fatty acid metabolism pathway in X52, while no comparable enrichment was observed in D4554. Among all the DEGs from comparison groups D4554-C vs. X52-C, D4554-C vs. D4554-T, D4554-T vs. X52-T, and X52-C vs. X52-T, a total of 3, 338 differentially expressed transcription factors (TFs) were identified, of which the MYB, bHLH, NAC, and WRKY families were predominated. Coexpression analysis partitioned these TFs into nine modules and identified 24 hub TFs. Metabolomic profiling revealed that fatty acids accounted for ~10% of the differentially expressed metabolites (DEMs), and eight of the nine TF coexpression modules were strongly correlated with fatty acid pathway metabolites (|r| > 0.9, P < 0.01). ATAC-seq detected 92, 356 differentially accessible regions (DARs) in X52 (0 h vs. 6 h). Genes linked to these DARs were significantly enriched for DNA-binding and DNA-templated transcription functions. In addition, DAR-linked genes were annotated to lipid metabolism. Notably, the DARs were enriched for binding motifs of bHLH-, bZIP-, AP2-, and C2H2-type TFs. In summary, we elucidate a chromatin accessibility-TF-enzyme gene-fatty acid metabolite regulatory network and highlight the possible chromatin-mediated transcriptional control of fatty acid metabolism during the adaptation to cold stress in cotton, offering a new perspective on the molecular basis of cold tolerance in upland cotton.

Introduction: Plant lesion segmentation aims to delineate disease regions at the pixel level to support early diagnosis, severity assessment, and targeted intervention in precision agriculture. However, the task remains challenging due to large variations in lesion scale-ranging from minute incipient spots to coalesced regions-and ambiguous, low-contrast boundaries that blend into healthy tissue.

Methods: We present GARDEN, a Gradient-guided boundary-Aware Region-Driven Edge-refiNement network that unifies multi-scale context modeling with selective long-range boundary refinement. Our approach integrates a Multi-Scale Context Aggregation (MSCA) module to harvest contextual cues across diverse receptive fields, forming scale-consistent lesion priors to improve sensitivity to tiny lesions. Additionally, we introduce a Boundary-aware Selective Scanning (BASS) module conditioned on a Gradient-Guided Boundary Predictor (GGBP). This module produces an explicit boundary prior to steer a Mamba-based 2D selective scan, allocating long-range reasoning to boundary-uncertain pixels while relying on local evidence in confident interiors.

Results: Validated across two public plant disease datasets, GARDEN achieves state-of-the-art results on both overlap and boundary metrics. Specifically, the model demonstrates pronounced gains on small lesions and boundary-ambiguous cases. Qualitative results further show sharper contours and reduced spurious responses to illumination and viewpoint changes compared to existing methods.

Discussion: By coupling scale robustness with boundary precision in a single architecture, GARDEN delivers accurate and reliable plant lesion segmentation. This method effectively addresses key challenges in the field, offering a robust solution for automated disease analysis under challenging real-world conditions.

Introduction: Lipid-transfer proteins (LTPs) are a class of small, alkaline proteins that bind and transport various lipid molecules, including fatty acids, phospholipids, glycolipids, and steroids, between phospholipid bilayers. They play crucial roles in signal transduction, stress tolerance, and plant growth and development.

Methods: In this study, based on pan-genomic data, we identified 107 LTP family members across nine diploid cotton species, comprising 45 core, 43 variable, and 19 specific genes. Synteny and selection pressure analyses clarified the evolutionary relationships among these genes, while structural variation analyses revealed that although structural variants altered gene structures, domains, and cis-acting elements, they did not significantly affect gene expression.

Results: Expression profiling further demonstrated that LTP genes exhibited distinct spatiotemporal expression patterns in cotton ovules and roots at different developmental stages.

Discussion: Overall, these findings highlight both conserved and divergent evolutionary patterns of the LTP family among diploid cotton species, providing new insights into their functional diversification, adaptive evolution, and potential involvement in cotton fiber development and stress responses.

Anthracnose is one of the primary diseases leading to quality deterioration in lychee. Traditional manual grading methods suffer from low efficiency and high subjectivity. To achieve rapid, non-destructive detection and intelligent grading of lychee anthracnose, while addressing the challenge of balancing high accuracy and lightweight design in detection models, this study proposes a lightweight improved model named LycheeGuard-Lite based on the YOLOv12 framework. By introducing the C3k2_Light module reconstructed with depthwise separable convolutions, a dual-path C2PSA attention mechanism (position-channel dual-path attention), and the wConv2D weighted convolution strategy, the model enhances lesion feature extraction capability while reducing computational complexity.Evaluation was performed on a self-built dataset comprising 14, 576 images of two dominant lychee varieties ('Feizixiao' and 'Baitangying') collected under multiple lighting conditions and annotated with three severity levels (Mild, Moderate, Severe). The results demonstrate that the model maintains 99.4% mAP50 detection accuracy while reducing its number of parameters to 2.19M (a 12.8% decrease) and computational cost to 4.1 GFLOPs (a 29.3% reduction).This research provides a lightweight and deployable algorithmic foundation for automated lychee disease recognition and intelligent grading, offering practical engineering value for post-harvest fruit sorting and quality management.

Introduction: Diameter at breast height (DBH) is a key parameter for assessing tree growth, carbon storage, and ecological functions. Traditional ground surveys are inefficient, labor-intensive, and terrain-limited, making them unsuitable for large-scale monitoring. Airborne LiDAR, as an advanced remote sensing tool, provides an efficient and non-destructive method for DBH estimation. However, most existing LiDAR-based models overlook the influence of genotype differences, limiting prediction accuracy.

Methods: In this study, we used data from 2,899 Catalpa bungei trees of different genotypes to develop a nonlinear mixed-effects (NLME) model that incorporates genotype as a random effect. This approach improved model generalizability by using LiDAR-derived tree height (LH) and LiDAR-derived crown diameter (LCD) as core predictors. Multiple sampling strategies were also evaluated to assess their impact on model performance.

Results: The results showed that, considering genotype effects, the proposed NLME model outperformed both traditional regression models and dummy-variable models (R² = 0.8624, RMSE = 1.1330, TRE = 3.9555), demonstrating the important role of genotype differences in improving model accuracy. Random sampling further improved prediction accuracy while effectively reducing measurement costs.

Discussion: This research introduces a new framework for integrating genotype variability into DBH prediction models and offers valuable insights for future LiDAR-based studies in genetically heterogeneous plantations. The findings provide technical support for forest management and ecosystem monitoring, as well as a methodological foundation for predicting tree growth under varying site and genetic conditions.

Soil salinization is an abiotic stress that hinders crop growth, agricultural productivity, and environmental protection. In this study, alfalfa (Medicago sativa) and tall fescue (Festuca arundinacea) were sown in seven inter-cropping ratios, with monocultures as controls to explore the effects of inter-cropping grasses on yield, water-soluble salt content, pH, and total nitrogen in saline-alkali land, and to establish whether inter-cropping can alleviate salinity and alkalinity. In addition, this study aimed to screen and identify the best alfalfa and tall fescue inter-cropping ratio. The results revealed that (1) Alfalfa and tall fescue had the best productivity and the highest crude protein content at an inter-cropping ratio of M6F4, M7F3, and M8F2, respectively. Besides, inter-cropping improved the land-use efficiency of saline land by altering the plant stem-leaf ratio to adapt to the resource competition. (2) Alfalfa and tall fescue inter-cropping at M3F7, M4F6, and M7F3 decreased the 21% soil salt and 7.8% pH and increased the 34.7% total nitrogen content. (3) Correlation analysis revealed significant correlations among soil salt content, pH, nitrogen, inter-cropping yield, stem-leaf ratio, and plant competition rate. These findings indicate that inter-cropping alfalfa and tall fescue in the ratio M6F4, M7F3, and M8F2 best improves the utilization efficiency of saline land.

Economically important Brassica oleracea plants are increasingly used as an alternative to the traditionally used Arabidopsis thaliana as models in plant stress biology. However, the extensive diversity of B. oleracea varieties, belonging to different vegetable forms, is often overlooked. Due to previous results indicating that basal levels of stress parameters (reference baseline values in unstressed plants) are important predictors of stress tolerance, we selected seven varieties to comparatively analyze the basal levels of a wide array of stress parameters, intending to guide future studies. A high variability was observed between the varieties for most parameters, including osmolytes, photosynthetic pigments, and antioxidative parameters. Particular interest was given to specialized metabolites, such as phenolics and glucosinolates, with established links between metabolites and the corresponding biosynthesis gene expression levels. Among all varieties tested, cauliflower exhibited the highest levels of phenolic and other antioxidant parameters, suggesting it may be the most resistant to oxidative stress. Meanwhile, kohlrabi, Brussels sprout, and Savoy cabbage excelled in photosynthetic and glucosinolate-related parameters, indicating higher tolerance to stresses affecting photosynthesis and glucosinolate-driven stress responses. Our results set the ground for future stress application studies to deal with the observed B. oleracea variability accordingly. We concluded that no single parameter alone can be used as a reliable indicator of stress tolerance. Therefore, we recommend that future studies employ a broad range of parameters and varieties to evaluate responses to specific stresses with B. oleracea varieties as promising alternative plant models.

Cold stress significantly hampers plant growth, development, and yield, posing a threat to global food security. This review consolidates our understanding of the physiological, biochemical, and molecular mechanisms that enable plants to tolerate cold stress. Plants employ many strategies to mitigate the negative effects of cold, including osmotic adjustments, boosting antioxidant defences, accumulating osmoprotectants, and regulating cold-responsive genes via transcription factors such as C-repeat binding proteins. The CBF expression-1 C-repeat binding factors cold-regulated (ICE1-CBF-COR) genetic signalling pathway is vital for acclimatisation to low temperatures and boosting cold resistance. Understanding these systems is essential for producing crops capable of thriving in cold environments through breeding and biotechnology. Enhancing crop resistance to cold stress can promote sustainable agriculture and bolster food security amid climate change. This review highlights key findings, methodological limitations, and areas needing further research to support the development of cold-tolerant crop varieties in the face of climate change.

Botrytis cinerea is a necrotrophic fungal pathogen that causes significant crop damage, yet the molecular mechanisms underlying plant defense remain incompletely understood. Here, we identify WRKY45 as a negative regulator of Arabidopsis resistance to B. cinerea through the suppression of JA/ET-mediated defense signaling. Our results show that WRKY45 expression was induced by B. cinerea infection, peaking 48 hours post-inoculation. Loss of WRKY45 function enhanced resistance, while WRKY45 overexpression increased susceptibility and cellular damage, as indicated by elevated electrolyte leakage, higher malondialdehyde levels, and reduced chlorophyll content. RNA-seq analysis identified 1,850 differentially expressed genes in wrky45 mutants, with strong enrichment of JA/ET-responsive pathways. Defense-related genes, including ORA59, PDF1.2, ERF104, and ERF1, were markedly upregulated in wrky45 but suppressed in overexpression lines, as confirmed by qRT-PCR. Electrophoretic mobility shift assays and dual-luciferase assays demonstrated that WRKY45 directly binds to the ORA59 promoter inhibiting its transcription, and represses the expression of PDF1.2, ERF104, and ERF1. Together, these results show that WRKY45 functions as a negative regulator by suppressing the expression of JA/ET-mediated defense genes, thereby modulating plant resistance to B. cinerea.