Plant Stress最新文献

Soil salinization is a widespread abiotic stress in China and one of the most critical factors affecting agricultural production and food security. γ-Aminobutyric acid (GABA) is a non-protein amino acid widely found in vertebrates, plants, and microorganisms, regulating the nervous system as well as plant defense systems. Mulberry is rich in GABA and exhibits extensive adaptability to various environments. In this study, we explored the possibility of alleviating salinity and alkalinity stress in mulberry seedlings using GABA and elucidating the intrinsic mechanisms by which GABA enhances salt-alkaline tolerance in mulberry trees through transcriptomic sequencing analysis. The results showed that 1 mM exogenous GABA enhanced the activities of mulberry seeds under saline-alkali stress, significantly increased the activities of POD and CAT (P < 0.01), reduced the level of reactive oxygen species and the content of malondialdehyde in mulberry seedlings and facilitated the growth and development of adventitious roots of mulberry. Transcriptomic analysis showed that GABA promoted the development and growth of adventitious roots of mulberry under saline-alkali stress by regulating the synthesis and modification of the cell wall, phytohormone signal transduction, and the conversion of starch and monosaccharides. Furthermore, the expression of pathogen pattern recognition receptors on the cell membrane of mulberry root system cells increased, enhancing the defense ability of mulberry root system cells. It is suggested that MYB, PME, SBT, EXP, DIR, POD, and the ARR family of transcription factors could be used as the target genes for further in-depth research.

Soil salinity represents a significant environmental stressor that impairs crop production and yield. A wide range of treatments have been used to reduce the effects of salinity in plants. Among the treatments used, functionalized nanoparticles (NPs) have shown great results. In light of recently demonstrated beneficial effects of chitosan-melatonin nanoparticles (CTS-Mel NPs) and chitosan-salicylic acid nanoparticles (CTS-SA NPs) in mitigating the deleterious consequences of major stress factors and boosting secondary metabolite biosynthesis, the current study aimed to investigate their potential as seed priming coatings to enhance plant performance under both control and salinity stress conditions. For this purpose, CTS (0.1% w/v), Mel (50 µM), SA (0.5 mM), CTS-Mel NPs and CTS-SA NPs were applied as seed priming agents on corn salad (Valerianella locusta) seeds, and subsequently key morphophysiological and biochemical properties were assayed under salinity conditions (0, 30 and 60 mM NaCl). Accordingly, salinity stress caused significant reduction in fresh and dry weights (FW and DW) of leaves, chl a, total chl, SPAD and enhancement in content of proline, phenolics, MDA, as well as protein content, and activities of SOD and CAT antioxidant enzymes. Concerning the phenolic compounds analyzed, salinity stress did not affect the dominant phenolic constituents with the exception of naringine. Regarding the protective effects of the various priming treatments, the adverse effects of salinity stress were ameliorated with the application of most of the applied treatments, and with CTS-Mel NPs in particular, by enhancing biomass, pigments, total phenols, protein, SOD and CAT antioxidant enzymatic activities, as well as the content of some dominant constituents of phenolic profile. CTS-Mel NPs enhanced chlorogenic acid, naringine, o-coumaric acid and catechin hydrate under both control and salinity conditions. Overall, CTS-Mel NPs outperformed CTS-SA NPs as a seed priming coating and could potentially be widely introduced as an innovative, sustainable approach to mitigate the effects of salinity and other abiotic stress conditions in crop plants.

Heat stress severely impacts wheat production by altering morpho-physiological traits, disrupting cellular physiological and biochemical attributes, and ultimately affecting the genetic makeup of the plant. Heat affects the thermosensitive traits of the vegetative and reproductive stages of wheat. Therefore, it is imperative to employ precise and expedite trait-based phenotyping as well genomics tools and crop breeding approaches to develop heat tolerant wheat cultivars. While trait-based breeding has been a time-consuming approach, it faces numerous challenges due to the labour-intensive, expensive, less accurate, environment-specific, and time-consuming process of screening, particularly for large numbers of genotypes. Nevertheless, recent breakthroughs in functional phenotyping, a platform that offers valuable insights into the dynamic responses of plants to heat stress. Conversely, functional genomics investigates genetic and epigenetic systems to identify and pinpoint gene variations related to specific traits. Therefore, this review summarizes heat stress effects on wheat at morphological, physiological and biochemical levels. Further, we highlight the potential of functional phenotyping that can rapidly detect wheat's physiological aspects in response to hot spells. We then finally highlight cutting-edge breeding strategies for enhancing heat tolerance in wheat, emphasizing an integrated approach that combines phenomics and genomics tools.

Low freezing tolerance threatens the survival and productivity of perennial ryegrass under northern climate. In this study, we aimed to identify transcriptional changes in plants subjected to low and freezing temperatures as well as to elucidate differences between tolerant and sensitive genotypes. Response to freezing stress was evaluated in a panel of 160 perennial ryegrass genotypes by measuring electrolyte leakage after exposure to -12 °C and -14 °C for 24 h. Two tolerant and two sensitive genotypes were selected for the transcriptome analysis. Crown tissue samples were collected at six treatments: before the start of cold acclimation (control point), at the start of acclimation, after one week of acclimation, after three weeks of acclimation, after freezing at -5 °C and freezing at -10 °C. A total of 11,125 differentially expressed genes (DEGs) were identified in the sensitive and 12,937 DEGs in the tolerant genotypes, when comparing the control vs. each of the acclimation and freezing treatments, as well as the end of acclimation vs. freezing treatments. Among the identified DEGs 3323 were unique to the sensitive genotypes, 5135 were unique to the tolerant genotypes and 7802 were shared. Genes upregulated during cold acclimation and freezing stress were linked to the MAPK signalling pathway, circadian rhythm, starch and sucrose metabolism, plant-pathogen interaction, carbon fixation, alpha-linoleic acid metabolism, carotenoid metabolism, glyoxylate and dicarboxylate metabolism pathways. Downregulated genes were linked to ATP-dependent chromatin remodelling, fatty acid elongation and DNA replication. The downregulation of fatty acid elongation and glutathione metabolism DEGs could indicate that the studied genotypes respond to cold stress in a novel or not yet well-characterized manner.

Heat stress is a critical environmental factor that adversely affects crop productivity. With the increasing frequency and intensity of heat waves and extreme weather events, heat stress has become a challenge for wheat production, which is one of the most important cereal crops. To sustain wheat production under heat stress conditions, there is an urgent need to develop high-yielding, heat-tolerant wheat varieties. This requires characterizing the genetic and physiological mechanisms underlying heat tolerance, as well as developing efficient phenotyping methods to evaluate a large number of wheat genotypes under heat stress field conditions. In this study, we used 184 wheat genotypes that were sown at two times of sowing (TOS), i.e., optimal sowing as TOS1 and late sowing as TOS2, with higher temperatures faced by plants during heading and grain filling in TOS2. We used a combination of physiological traits, multispectral vegetative indices (VIs) derived from aerial imagery and machine learning approaches to effectively differentiate wheat genotypes for heat tolerance and susceptibility. The response of wheat genotypes to heat stress was delineated as being susceptible, moderate, and tolerant using the stress susceptibility index, percentage loss, and tolerance index. Different VIs varied significantly between the two TOS. The decline in VIs during anthesis and post-anthesis was minimal in heat tolerant genotypes compared to susceptible genotypes under TOS2. We classified the stress severity and yield using VIs with a machine learning approach. A model was created with a random forest classifier (RFC) trained to categorize genotypes based on the stress susceptibility index using Python libraries. The PCA was utilized to reduce dimensionality, and five principal components explaining 99 % of the variability were employed as input for developing the model. The RFC model achieved an accuracy of 64 % and excelled in recognizing crops under extreme stress, with a recall rate of 0.87 and an F1 score of 0.77 for the susceptible class. The model had high precision metrics, with values of 0.69, 0.42, and 0.80 for the susceptible, moderate, and tolerant classes, respectively. Our results suggest that multispectral-driven phenotypic traits can be used by breeders to select and develop wheat varieties tolerant to heat stress.

Agriculture provides basic livelihood for a large section of world's population. It is the oldest economic activity in India, with two third of Indian population involved in crop production. India is second largest producer of rice and biggest exporter globally, with rice which is most common staple crop consumed in country. However, there are several challenges for paddy production including small production yield, soil quality, seed quality, huge volume of water needed and biotic stress. Of these, biotic stress drastically affects yield and susceptibility to other diseases in paddy production. It is caused by pathogens such as bacteria, viruses, fungi, nematodes, all of which severely affect growth and productivity of paddy crop. To mitigate these challenges, infected crops are identified, detected, classified, categorized, and prevented according to their respective suffering disease by using conventional methods which are not effective and efficient for growth of paddy crop. Thus, use of artificial intelligence (AI) and a smart agriculture-based Internet of Things (IoT) platform could be effective for detecting the biotic stresses in very less time or online mode. For this, deep learning, and convolutional neural networks (CNN) multi-structured layer approach were used for diagnosing disease in rice plants. Different models and classifiers of CNN were used for detecting disease by processing high-spectral images and using logistic and mathematical formulation methods for classification of biotic paddy crop stresses. Continuous monitoring of stages of infection in paddy crop can be achieved using real-time data. Thus, use of AI has made diagnosing paddy crop diseases much easier and more efficient.

Silica nanoparticles (SiO₂-NPs) have been demonstrated to alleviate the adverse impacts of salt or low temperature on crop growth, especially for individual stress. The aim of this study was to elucidate the regulatory effect of SiO₂-NPs on plant performance under combined salt and low-temperature stress. Therefore, a phytotron experiment was performed to explore the effects of SiO₂-NPs application (0, 50, 100, 200 mg L⁻¹) on the plant growth, ionic content, antioxidant activities, photosynthetic parameters, and osmoregulator concentrations of cotton seedlings subjected to the combined stress of salinity (50, 100, and 150 mmol L⁻¹ NaCl) and low temperature (day and night temperatures of 15 and 10 °C). The results indicated that the combinatorial stress strongly decreased the plant height and leaf area of cotton seedlings, and obviously suppressed the aboveground biomass by 10.26 %, 11.42 %, and 15.70 % with the increase in salinity. While SiO₂-NPs application significantly increased the plant growth, photosynthetic rate, transpiration rate, stomatal conductance, superoxide dismutase, catalase and glutathione reductase activities, leaf water potential, K⁺, and proline contents, and reduced the Na⁺ content and Na⁺/K⁺ ratio of cotton seedlings under the combinatorial stress. However, the effects of SiO₂-NPs on reduced glutathione, total soluble sugar and protein content, and peroxidase activity did not exhibit a clear pattern. The aboveground biomass of cotton seedlings subjected to the combinatorial stress was closely correlated with the Na⁺/K⁺ ratio, Na⁺ content, K⁺ content, proline content, SOD activity, and CAT activity, indicating that SiO₂-NPs could alleviate the suppression of combinatorial stress on cotton seedling growth by decreasing the Na⁺/K⁺ ratio and increasing the antioxidant capacity.

Enhancing plant adaptation to low input conditions is a fundamental goal for implementing sustainable agriculture. In the present study, two eggplant (Solanum melongena) accessions (MEL1 and MEL5), two introgression lines (ILs) derived from eggplant wild relatives S. dasyphyllum (IL-M1-D1) and S. insanum (IL-M5-I9), and a heterozygous version of this last IL (IL^Het-M5-I9), along with hybrids among them were evaluated under low N (LN) conditions. IL-M1-D1 carries an introgressed fragment of 4.9 Mb in homozygosis from S. dasyphyllum on chromosome 2, while IL-M5-I9 and IL^Het-M5-I9 carry an introgression of 21.5 Mb on chromosome 9 in homozygosis and heterozygosis, respectively, from S. insanum. Multiple quantitative trait loci (QTLs) for several traits of interest were associated with both introgressions under LN conditions in a previous study with segregating advanced backcrosses. Here we evaluated the performance of these materials for 22 agronomic and developmental traits under low N fertilization (LN) conditions. Hybrids with the ILs enabled the study of genetic background effects on QTLs expression. The materials evaluated showed a significant phenotypic variation, particularly within hybrids segregating for the introgression from S. insanum in chromosome 9. Statistical analysis revealed no significant differences among hybrids carrying or not the introgression on chromosome 2 of S. dasyphyllum, and only slight differences were observed between the IL-M1-D1 and its recurrent parent S. melongena MEL1, suggesting a limited impact of this introgression on chromosome 2 on the phenotype variation. However, the differences observed between IL-M5-I9 and its recurrent parent S. melongena MEL5, together with the association between genotypic and phenotypic variation in hybrids segregating for this introgression, allowed the identification of 13 QTLs on chromosome 9. These results successfully validated the previously identified QTLs for flavonol content in leaves, nitrogen balanced index, fruit mean weight, and nitrogen content in leaves and, also revealed nine new QTLs associated with the introgressed genomic region in chromosome 9. This study emphasizes the influence of environmental conditions, genotypes, and genetic backgrounds on the phenotypic expression of eggplant QTLs introgressed from wild relatives and highlights the importance of QTL validation. These findings contribute valuable insights for developing new eggplant cultivars for a more sustainable agriculture, particularly with adaptation to LN conditions.