Dynamic responses of germination characteristics and antioxidant systems to alfalfa (Medicago sativa) seed aging based on transcriptome.

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.

Telomerase reverse transcriptase, a telomere length maintenance protein in alfalfa (Medicago sativa), confers Arabidopsis thaliana seeds aging tolerance via modulation of telomere length.

Numerous studies have investigated seed aging, with a particular emphasis on the involvement of reactive oxygen species. Reactive oxygen species diffuse into the nucleus and damage telomeres, resulting in loss of genetic integrity. Telomerase reverse transcriptase (TERT) plays an essential role in maintaining plant genomic stability. Genome-wide analyses of TERT genes in alfalfa (Medicago sativa) have not yet been conducted, leaving a gap in our understanding of the mechanisms underlying seed aging associated with TERT genes. In this study, four MsTERT genes were identified in the alfalfa genome. The expression profiles of the four MsTERT genes during seed germination indicated that MS. gene79077 was significantly upregulated by seed aging. Transgenic seeds overexpressing MS. gene79077 in Arabidopsis exhibited enhanced tolerance to seed aging by reducing the levels of H2O2 and increasing telomere length and telomerase activity. Furthermore, transcript profiling of aging-treated Arabidopsis wild-type and overexpressing seeds showed an aging response in genes related to glutathione-dependent detoxification and antioxidant defense pathways. These results revealed that MS. gene79077 conferred Arabidopsis seed-aging tolerance via modulation of antioxidant defense and telomere homeostasis. This study provides a new way to understand stress-responsive MsTERT genes for the potential genetic improvement of seed vigor.

Using Machine Learning Methods Combined with Vegetation Indices and Growth Indicators to Predict Seed Yield of Bromus inermis.

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.