Seed aging is a major challenge for food security, agronomic production, and germplasm conservation, and reactive oxygen species (ROS) and methylglyoxal (MG) are highly involved in the aging process. However, the regulatory mechanisms controlling the abundance of ROS and MG are not well characterized. To characterize dynamic response of antioxidant and glyoxalase systems during seed aging, oat (Avena sativa L.) aged seeds with a range of germination percentages were used to explore physiological parameters, biochemical parameters and relevant gene expression. A reference transcriptome based on PacBio sequencing generated 67,184 non-redundant full-length transcripts, with 59,050 annotated. Subsequently, eleven seed samples were used to investigate the dynamic response of respiration, ROS and MG accumulation, antioxidant enzymes and glyoxalase activity, and associated genes expression. The 48 indicators with high correlation coefficients were divided into six major response patterns, and were used for placing eleven seed samples into four groups, i.e., non-aged (Group N), higher vigor (Group H), medium vigor (Group M), and lower vigor (Group L). Finally, we proposed a putative model for aging response and self-detoxification mechanisms based on the four groups representing different aging levels. In addition, the outcomes of the study suggested the dysfunction of antioxidant and glyoxalase system, and the accumulation of ROS and MG definitely contribute to oat seed aging.
Multispectral imaging (MSI) has become a new fast and non-destructive detection method in seed identification. Previous research has usually focused on single models in MSI data analysis, which always employed all features and increased the risk to efficiency and that of system cost. In this study, we developed a stacking ensemble learning (SEL) model for successfully identifying a single seed of sickle alfalfa (Medicago falcata), hybrid alfalfa (M. varia), and alfalfa (M. sativa). SEL adopted a three-layer structure, i.e., level 0 with principal component analysis (PCA), linear discriminant analysis (LDA), and quadratic discriminant analysis (QDA) as models of dimensionality reduction and feature extraction (DRFE); level 1 with support vector machine (SVM), multiple logistic regression (MLR), generalized linear models with elastic net regularization (GLMNET), and eXtreme Gradient Boosting (XGBoost) as basic learners; and level 3 with XGBoost as meta-learner. We confirmed that the values of overall accuracy, kappa, precision, sensitivity, specificity, and sensitivity in the SEL model were all significantly higher than those in basic models alone, based on both spectral features and a combination of morphological and spectral features. Furthermore, we also developed a feature filtering process and successfully selected 5 optimal features out of 33 ones, which corresponded to the contents of chlorophyll, anthocyanin, fat, and moisture in seeds. Our SEL model in MSI data analysis provided a new way for seed identification, and the feature filter process potentially could be used widely for development of a low-cost and narrow-channel sensor.
Agronomic practices improve seed yield by regulating seed yield components, and the relationship between seed yield and seed yield components is still unclear in smooth bromegrass (Bromus inermis). To optimize seed production and yield in smooth bromegrass, a five-year field trial was designed with split-split-plot to study the combined effects of row spacing (30, 45, 60, and 75 cm), phosphorus (0, 60, 90, and 120 kg P ha−1) and nitrogen (0 and 100 kg N ha−1) on seed yield and seed yield components including fertile tillers m−2 (FTs), spikelets per fertile tiller (SFT), florets per spikelet (FS), and seeds per spikelet (SS). The results showed that FTs as a key factor had a positive effect to seed yield with the biggest pathway coefficient, while SS had a negative effect. Meanwhile, an interaction effect between FTs and SS was observed. FS and SS were increased with phosphorus application under the condition of sufficient nitrogen. In addition, sufficient precipitation at the non-growing season resulted in more FTs in the next year in rain-fed regions. Therefore, the optimum seed yield of smooth bromegrass can be obtained with row spacing (45 cm), nitrogen (100 kg N ha−1), and phosphorus application (60 kg P ha−1).
Background Medicago sativa L. ‘Qingshui’ is a valuable rhizomatous forage germplasm resource. We previously crossed Qingshui with the high-yielding Medicago sativa L. ‘WL168’ and obtained novel rhizomatous hybrid strains (RSA-01, RSA-02, and RSA-03). Telomere dynamics are more accurate predictors of survival and mortality than chronological age. Based on telomere analyses, we aimed to identify alfalfa varieties with increased stamina and longevity for the establishment of artificial grazing grasslands. Methods In this study, we performed longitudinal analysis of telomerase activity and relative telomere length in five alfalfa varieties (Qingshui, WL168, RSA-01, RSA-02, and RSA-03) at the age of 1 year and 5 years to examine the relationship among telomerase activity, rate of change in relative telomere length, and longevity. We further aimed to evaluate the longevity of the examined varieties. Telomerase activity and relative telomere length were measured using enzyme-linked immunosorbent assay and real-time polymerase chain reaction, respectively. Results We observed significant differences in telomerase activity between plants aged 1 year and those aged 5 years in all varieties except WL168, and the rate of change in telomerase activity does not differ reliably with age. As telomerase activity and relative telomere length are complex phenomena, further studies examining the molecular mechanisms of telomere-related proteins are needed. Relative telomere lengths of Qingshui, WL168, RSA-01, RSA-02, and RSA-03 in plants aged 5 years were higher than those aged 1 year by 11.41, 11.24, 9.21, 10.23, and 11.41, respectively. Relative telomere length of alfalfa tended to increase with age. Accordingly, alfalfa varieties can be classified according to rate of change in relative telomere length as long-lived (Qingshui, WL168, and RSA-03), medium-lived (RSA-02) and short-lived (RSA-01). The differences in relative telomere length distances of Qingshui, WL168, RSA-01, RSA-02, and RSA-03 between plants aged 1 and 5 years were 10.40, 13.02, 12.22, 11.22, and 13.25, respectively. The largest difference in relative telomere length was found between Qingshui and RSA-02 at 2.20. Our findings demonstrated that relative telomere length in alfalfa is influenced by genetic variation and age, with age exerting a greater effect.
Advances in optical imaging technology using rapid and non-destructive methods have led to improvements in the efficiency of seed quality detection. Accurately timing the harvest is crucial for maximizing the yield of higher-quality Siberian wild rye seeds by minimizing excessive shattering during harvesting. This research applied integrated optical imaging techniques and machine learning algorithms to develop different models for classifying Siberian wild rye seeds based on different maturity stages and grain positions. The multi-source fusion of morphological, multispectral, and autofluorescence data provided more comprehensive information but also increases the performance requirements of the equipment. Therefore, we employed three filtering algorithms, namely minimal joint mutual information maximization (JMIM), information gain, and Gini impurity, and set up two control methods (feature union and no-filtering) to assess the impact of retaining only 20% of the features on the model performance. Both JMIM and information gain revealed autofluorescence and morphological features (CIELab A, CIELab B, hue and saturation), with these two filtering algorithms showing shorter run times. Furthermore, a strong correlation was observed between shoot length and morphological and autofluorescence spectral features. Machine learning models based on linear discriminant analysis (LDA), random forests (RF) and support vector machines (SVM) showed high performance (>0.78 accuracies) in classifying seeds at different maturity stages. Furthermore, it was found that there was considerable variation in the different grain positions at the maturity stage, and the K-means approach was used to improve the model performance by 5.8%-9.24%. In conclusion, our study demonstrated that feature filtering algorithms combined with machine learning algorithms offer high performance and low cost in identifying seed maturity stages and that the application of k-means techniques for inconsistent maturity improves classification accuracy. Therefore, this technique could be employed classification of seed maturity and superior physiological quality for Siberian wild rye seeds.
Seed aging is always taken as a crucial factor for vigor loss due to delayed seed germination and seedling growth, which limits hay production. Many studies have found that telomeres are closely related to abiotic stress and seed vigor. However, the molecular mechanism of telomeres’ response to abiotic stress, seed vigor, and the maintenance mechanism of plant telomere homeostasis still remain unclear. Alfalfa (Medicago sativa) enjoys the title of “King of Forage”, and is an important protein forage for the dairy industry as planted in the world. This comprehensive investigation was performed to explore the molecular characterization, phylogenetic relationship, and gene expression analysis of MsTERT under abiotic stress and during seed aging in alfalfa. In this study, MsTERT was identified from the ‘Zhongmu 1’ alfalfa genome and encoded a coding sequence (CDS) of 3615 bp in length, consisting of telomerase- RNA-Binding Domain (RBD) and Reverse Transcriptase (RT) domains, 1024 amino acids, an isoelectric point of 9.58, and a relative molecular mass of 138.94 kD. Subcellular localization showed that MsTERT was mainly localized in the nucleus and mitochondria. The results of the expression profile showed that MsTERT was observed to respond to various stress conditions such as salt (100 mmol/L NaCl) and drought (20% PEG 6000). Furthermore, exogenous hormones IAA, ABA, and GA3 showed the potential to affect MsTERT expression. Additionally, MsTERT also responded to seed aging. Our results revealed a marginal but significant association between relative telomere length, MsTERT expression, and seed germination percentage, suggesting that the length of telomeres was shortened, and expression of MsTERT decreased with alfalfa seed aged. These results provide some evidence for the hypothesis of relative telomere length and/or TERT expression serving as biomarkers of seed aging. Although this finding is helpful to offer a new way to elucidate the molecular mechanism of vigor loss in alfalfa seed, further investigation is required to elucidate the molecular mechanism by which the MsTERT gene regulates seed vigor.
Abiotic stress disturbs plant cellular redox homeostasis, inhibiting seed germination and plant growth. This is a crucial limitation to crop yield. Glutathione reductase (GR) is an important component of the ascorbate-glutathione (AsA-GSH) cycle which is involved in multiple plant metabolic processes. In the present study, GRs in A. sativa (AsGRs) were selected to explore their molecular characterization, phylogenetic relationship, and RNA expression changes during seed imbibition under abiotic stress. Seven AsGR genes were identified and mapped on six chromosomes of A, C, and D subgenomes. Phylogenetic analysis and subcellular localization of AsGR proteins divided them into two sub-families, AsGR1 and AsGR2, which were predicted to be mainly located in cytoplasm, mitochondrion, and chloroplast. Cis-elements relevant to stress and hormone responses are distributed in promoter regions of AsGRs. Tissue-specific expression profiling showed that AsGR1 genes were highly expressed in roots, leaves, and seeds, while AsGR2 genes were highly expressed in leaves and seeds. Both AsGR1 and AsGR2 genes showed a decreasing-increasing expression trend during seed germination under non-stress conditions. In addition, their responses to drought, salt, cold, copper, H2O2, and ageing treatments were quite different during seed imbibition. Among the seven AsGR genes, AsGR1-A, AsGR1-C, AsGR2-A, and AsGR2-D responded more significantly, especially under drought, ageing, and H2O2 stress. This study has laid the ground for the functional characterization of GR and the improvement of oat stress tolerance and seed vigor.
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