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.

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.

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.

Seed aging poses a significant challenge to agronomic production and germplasm conservation. Reactive oxygen species (ROS) are highly involved in the aging process. However, dynamic response of germination characteristics and antioxidant system to seed aging are not yet very clear. This study explored the potential physiological mechanisms responsible for the reduced and rapid loss of seed vigor in alfalfa, and identified key genes regulating seed vigor. The germination percentage exhibited a decreased trend with the prolongation of aging duration. From 16 to 32 days of aging, the antioxidant enzyme activities of SOD, POD, CAT, DHAR and MDHAR declined significantly, which lead to the disruption of ROS balance and a significant increase in ROS levels, exacerbating seed aging. Based on transcriptome, 29 differentially expressed genes (DEGs) including SOD1, APX-2 and GST-7 within the ROS scavenging system showed a significantly down-regulated expression trend at aging of 16 and 24 days, indicating the abnormal function of antioxidant metabolism. Furthermore, some related genes including ATPF1B, ATPeF0C-3, NDUFS1, NDUFS3 and ND2 in the mitochondrial ETC exhibited a downturn following seed aging, which would result in the losing of seed vigor. This study has uncovered a significant array of potential target genes within the seed antioxidant system and mitochondrial ETC. These discoveries offer a wider lens for delving into the molecular regulatory mechanisms of seed aging. Further research is crucial to comprehensively elucidate the precise pathways through which these pivotal genes regulate seed vigor.

Abiotic stresses have deleterious effects on seed germination and seedling establishment, leading to significant crop yield losses. Adverse environmental conditions can cause the accumulation of methylglyoxal (MG) within plant cells, which can negatively impact plant growth and development. The glyoxalase system, which consists of the glutathione (GSH)-dependent enzymes glyoxalase I (GLX1) and glyoxalase II (GLX2), as well as the GSH-independent glyoxalase III (GLX3 or DJ-1), plays a crucial role in detoxifying MG. However, genome-wide analysis of glyoxalase genes has not been performed for one of the agricultural important species, oat (Avena sativa). This study identified a total of 26 AsGLX1 genes, including 8 genes encoding Ni²⁺-dependent GLX1s and 2 genes encoding Zn²⁺-dependent GLX1s. Additionally, 14 AsGLX2 genes were identified, of which 3 genes encoded proteins with both lactamase B and hydroxyacylglutathione hydrolase C-terminal domains and potential catalytic activity, and 15 AsGLX3 genes encoding proteins containing double DJ-1 domains. The domain architecture of the three gene families strongly correlates with the clades observed in the phylogenetic trees. The AsGLX1, AsGLX2, and AsGLX3 genes were evenly distributed in the A, C, and D subgenomes, and gene duplication of AsGLX1 and AsGLX3 genes resulted from tandem duplications. Besides the core cis-elements, hormone responsive elements dominated the promoter regions of the glyoxalase genes, and stress responsive elements were also frequently observed. The subcellular localization of glyoxalases was predicted to be primarily in the cytoplasm, chloroplasts, and mitochondria, with a few presents in the nucleus, which is consistent with their tissue-specific expression. The highest expression levels were observed in leaves and seeds, indicating that these genes may play important roles in maintaining leaf function and ensuring seed vigor. Moreover, based on in silico predication and expression pattern analysis, AsGLX1-7A, AsGLX2-5D, AsDJ-1-5D, AsGLX1-3D2, and AsGLX1-2A were suggested as promising candidate genes for improving stress resistance or seed vigor in oat. Overall, the identification and analysis of the glyoxalase gene families in this study can provide new strategies for improving oat stress resistance and seed vigor.

Smooth bromegrass (Bromus inermis) is a perennial, high-quality forage grass. However, its seed yield is influenced by agronomic practices, climatic conditions, and the growing year. The rapid and effective prediction of seed yield can assist growers in making informed production decisions and reducing agricultural risks. Our field trial design followed a completely randomized block design with four blocks and three nitrogen levels (0, 100, and 200 kg·N·ha⁻¹) during 2022 and 2023. Data on the remote vegetation index (RVI), the normalized difference vegetation index (NDVI), the leaf nitrogen content (LNC), and the leaf area index (LAI) were collected at heading, anthesis, and milk stages. Multiple linear regression (MLR), support vector machine (SVM), and random forest (RF) regression models were utilized to predict seed yield. In 2022, the results indicated that nitrogen application provided a sufficiently large range of variation of seed yield (ranging from 45.79 to 379.45 kg ha⁻¹). Correlation analysis showed that the indices of the RVI, the NDVI, the LNC, and the LAI in 2022 presented significant positive correlation with seed yield, and the highest correlation coefficient was observed at the heading stage. The data from 2022 were utilized to formulate a predictive model for seed yield. The results suggested that utilizing data from the heading stage produced the best prediction performance. SVM and RF outperformed MLR in prediction, with RF demonstrating the highest performance (R² = 0.75, RMSE = 51.93 kg ha⁻¹, MAE = 29.43 kg ha⁻¹, and MAPE = 0.17). Notably, the accuracy of predicting seed yield for the year 2023 using this model had decreased. Feature importance analysis of the RF model revealed that LNC was a crucial indicator for predicting smooth bromegrass seed yield. Further studies with an expanded dataset and integration of weather data are needed to improve the accuracy and generalizability of the model and adaptability for the growing year.