Dynamic Responses of Germination Characteristics and Antioxidant Systems to Alfalfa (Medicago sativa) Seed Aging Based on Transcriptome

doi:10.21203/rs.3.rs-5155226/v1

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Dynamic Responses of Germination Characteristics and Antioxidant Systems to Alfalfa (Medicago sativa) Seed Aging Based on Transcriptome

https://doi.org/10.21203/rs.3.rs-5155226/v1

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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.

Agricultural Engineering

Alfalfa

Seed aging

Reactive oxygen species

Seed vigor

Germination characteristics

Antioxidant system

Seed vigor is a crucial agronomic trait, typically encompassing seed germination and the capacity to maintain this vigor during storage, and holds significant importance for seed quality and genetic resource preservation (Taylor, 2020). The vigor level of seeds reaches peak after maturation, but inevitable deterioration occurs during the storage process and leads to a decline in seed vigor (Murthy et al., 2003). Seed aging, as an accumulative, irreversible and unstoppable process, poses significant obstacles to crop production and germplasm preservation (Wang et al., 2015). Although the mechanisms behind seed aging are still undergoing thorough investigation, reactive oxygen species (ROS) are recognized as the primary contributor to this process (Ratajczak et al., 2015). According to the free radical theory of aging, the generation and quantity of ROS plays a pivotal role in the advancement of seed aging (Yin et al., 2016). Mitochondria serve as the primary sites for ROS generation, thereby participating in the regulation of ROS balance. Throughout the storage of seeds, the accumulation of ROS within mitochondria triggers damage to lipids, mtDNA and proteins (Bailly et al., 2008). These interconnected alterations result in the eventual loss of seed vigor. To mitigate the harmful effects of seed aging, it is important to employ appropriate storage conditions, develop new preservation techniques and methods. Additionally, in-depth research into the physiological and molecular mechanisms of seed aging can provide a better understanding and management for this process, ultimately preserving seed quality and genetic resources. However, the understanding of regulatory mechanisms and molecular networks governing seed aging is still limited.

In recent years, significant progress in genomics and transcriptomics has unveiled novel perspectives on the molecular intricacies of seed aging. Leveraging multi-omics analysis, researchers have been able to pinpoint genes associated with seed vitality or resilience to aging and unravel the underlying molecular mechanisms. Gu et al. (2019) employed genomic and proteomic approaches, utilizing two-dimensional fluorescence difference in gel electrophoresis (2D-DIGE), to successfully identify 165 differentially expressed proteins (DEPs) in germinated seeds of Brassica napus with high and low oil content. During the germination process of seeds with varying oil content, significant alterations in 31 unique genes were observed, with 13 of these genes located within the confidence interval of germination-related quantitative trait loci (QTLs). These genes potentially played a pivotal role in regulating seed vigor. Peng et al. (Peng et al., 2022) employed a whole-genome association analysis to identify a major quantitative trait locus (QTL) qSP3 that controlled seedling emergence percentage. They demonstrated that this locus encoded the cupin domain protein OsCDP3.10, which had a function in synthesizing a 52 kDa globulin. As compared to the wild type, rice (Oryza sativa) mutants of OsCDP3.10 displayed a significant reduction in seed vigor, and these mutant seeds exhibited a lack of 52 kDa globulin accumulation, resulting in a significant decrease in amino acid content during early germination. Furthermore, it was further substantiated that the OsCDP3.10 gene might regulate seed vigor by influencing amino acid accumulation during the seed germination process, leading to the stimulation of hydrogen peroxide (H₂O₂) production. Wang et al. (2022b) utilized high-throughput transcriptomics and broad-spectrum metabolomics techniques to identify several transcription factors, including bZIP23 and bZIP42. Transgenic rice experiments demonstrated that bZIP23 and bZIP42 positively regulated seed vigor. Additionally, the study identified the PER1A, a genetic factor encoding a peroxidase enzyme, which positively regulated seed vigor by scavenging ROS within the seeds. bZIP23 and bZIP42 could directly bind to the promoter region of PER1A and activate its transcriptional expression. Moreover, physiological data indicated that bZIP23 and PER1A played important roles in scavenging ROS in the body. This study unveiled a novel mechanism for enhancing rice seed vigor through the clearance of ROS and provided valuable targets for further improvement of crop-related agronomic traits. Numerous studies found that protein repair L-isoaspartyl methyltransferase 1 (OsPIMT1) (Wei et al., 2015), OsGRETCHENHAGEN3-2 (OsGH3-2) (Yuan et al., 2021), acetaldehyde dehydrogenase 7 (OsALDH7) (Shin et al., 2009) and Lipoxygenase 3 (OsLOX3) (Xu et al., 2015) played pivotal roles in regulating rice seed longevity and seed-related processes through distinct pathways. Furthermore, Kaur et al. (2015) discovered that OsHSP18.2 was a protein responsive to aging, and its function as a molecular chaperone likely served to protect and stabilize cellular proteins during seed maturation, desiccation and aging by limiting the accumulation of ROS, thereby enhancing seed vigor, long-term viability and seedling establishment. Although there were much more studies made in recent years for seed aging research, a notable portion of these studies had primarily concentrated on model plant, horticulture and crop seeds. Relatively less attention has been dedicated to investigating the mitochondrial antioxidant and energy production mechanisms during seed aging.

Alfalfa (Medicago sativa), a perennial herbaceous plant, is one of the most widely distributed leguminous forage crops globally and holds the largest cultivation all over the world. It plays a crucial role in the development of grassland animal husbandry and ecological environment construction (Xue et al., 2023). Furthermore, high-temperature and high-humidity environments can accelerate seed deterioration, reducing their utility (Song et al., 2023). Therefore, investigating the physiological and molecular regulatory mechanisms during alfalfa seed aging is very important for improving seed quality and achieving uniform seedling emergence in the field. Hence, the aims of this study are: (a) to investigate the physiological response dynamic patterns of antioxidant systems during seed aging; (b) to identify key genes in oxidative phosphorylation pathway and antioxidant systems that respond to seed aging based on transcriptome sequencing; (c) to propose an antioxidant response model for alfalfa seed aged for different levels. This study will enhance our understanding for the aging process, ultimately providing valuable insights for the maintaining of alfalfa seed vigor.

2.1. Preparation of Seeds

The alfalfa (cv Zhongmu No. 1) seeds were collected in 2019 from the Forage Seed Propagation Station. The station is located in Jiuquan city, Gansu province, China, specifically at coordinates 37 N, 98.30 E, at an elevation of 1480 meters. In June 2020, the initial test revealed a seed sample with a 7% moisture content (based on fresh weight), accompanied by a 93% germination percentage (GP), and 7% hard seed percentage (HP). Subsequent experiments were involved acquiring seeds of consistent size and plumpness through the use of a sieve.

2.2. Aging Treatment

Seeds were submitted to a seed aging treatment following the established methodology outlined in Xia et al. (2020)'s study. Initially, seeds adjusted to 10% moisture content were sealed in aluminum foil bags (0.12 × 0.17 m², approximately 40 g per bag) and placed in a water bath at 45℃. Sampling commenced every four days. The germination percentage decreased to 80% by the 8th day and dropped to 0% by the 32nd day. This process generated seeds treated from day 0 to day 32 with varying germination percentages, labeled as A0 (Control), A4, A8, A12, A16, A20, A24, A28, and A32 for subsequent tests.

2.3. Germination Parameters Tests

Germination tests aligned with the Rules for International Seed Testing Association (2020), employing four replicates, each consisting of around 100 seeds. Seeds were evenly spread on three layers of filter paper within a 10 cm diameter petri dish and placed in a 20℃ constant-temperature light incubator (8 hours light/16 hours dark, PPFD = 121 µmol m^− 2s^− 1) with distilled water (Deng et al., 2010). For germination assessment, seeds were considered germinated if they developed into normal seedlings. The germination percentage (GP) was calculated based on the percentage of normal seedlings by the 10th day. Seeds with primary roots of at least 2 mm in length were monitored daily until the 10th day to calculate the mean germination time (MGT) using the Equation:

$$\:\text{MGT=}\frac{\sum\:\text{N}\text{T}}{\sum\:\text{N}}$$

The equation uses 'T' to represent the days since germination began, and 'N' to denote the number of seeds that sprouted on day 'T' (Ellis, 1981).

2.4. Physiological Analysis

The seeds underwent aging treatments for 0, 4, 8, 12, 16, 20, 24, 28, and 32 days. After 24 h of imbibition, the seeds were ground into powder with liquid nitrogen for the analysis of H₂O₂, malondialdehyde (MDA) content, non-enzymatic antioxidants, and antioxidant enzyme activities.

To measure MDA, the samples were homogenized in 5% (w/v) trichloroacetic acid (TCA) and mixed with an equal volume of 0.5% thiobarbituric acid. This mixture was incubated at 95°C for 30 min, then centrifuged at 16,000× g for 20 min. Absorbance at 532 nm and 600 nm was measured to calculate MDA content (Luo et al., 2023). H₂O₂ levels were determined by homogenizing samples in 0.1% (w/v) TCA in an ice bath, centrifuging at 12,000× g for 15 min, and mixing 0.5 mL of the supernatant with 10 mM phosphate buffer (pH 7.0) and 1 mL of 1 M KI. The absorbance was recorded at 390 nm (Kurek et al., 2019).

For protein extraction, 0.2 g of seeds were ground in liquid nitrogen and suspended in homogenization buffer (50 mM phosphate buffer, pH 7.0, with 1.0 mM EDTA and 1% PVP). After centrifugation at 12,000× g for 20 min at 4°C, the supernatants were used for enzyme activity assays (Sun et al., 2022). SOD activity was measured by its inhibition of nitroblue tetrazolium photochemical reduction, with 50% inhibition defined as one enzyme unit (Sun et al., 2022). CAT activity was assessed by the decrease in absorbance at 240 nm due to H₂O₂ degradation (Luo et al., 2023). POD activity was determined using guaiacol and hydrogen peroxide substrates, measuring absorbance at 470 nm (Sun et al., 2022). GR activity was evaluated by the decrease in absorbance at 340 nm due to NADPH oxidation, MDHAR activity by NADH oxidation at 340 nm, APX activity by the decrease in absorbance at 290 nm due to AsA consumption, and DHAR activity by the increase in absorbance at 265 nm (Luo et al., 2023). Protein concentrations were determined by the Bradford method (Gu et al., 2019).

The contents of AsA, DHA, GSH, and GSSG were determined using the commercial assay kits of ASA-2A-W, DHA-2-W, GSH-2-W, and GSSG-2-W, respectively, procured from Suzhou Comin Biotechnology Co., Ltd., China. All measurements were carried out according to the kit instructions. Samples (0.1 g) were extracted in 1 mL of the respective extraction solutions per the manufacturer's instructions (http://www.cominbio.com/, accessed on 26 December 2022). AsA was extracted with acetic acid solution, and DHA, GSH, and GSSG with metaphosphoric acid solution. AsA content was determined by its reaction with Fast Blue B salt, measured at 420 nm. DHA content was assessed by its reduction to ASA with dithiothreitol (DTT), measured at 265 nm. GSH content was based on its sequential oxidation by DTNB and reduction by NADPH in the presence of GR, with measurements at 412 nm. GSSG content was determined using 2VP as a GSH masking agent.

2.5. Transcriptome analysis

2.5.1 RNA extraction, cDNA construction and differentially expressed genes identification

Seeds subjected to varying durations of aging treatment (0, 8, 16 and 24 days) were collected after a 24-hour imbibition, and weighing 0.1 g were pulverized. Total RNA was extracted using a kit (Magen Biotech Co. Ltd). RNA integrity was assessed using the Impen nanophotometer and the Agilent 2100 bioanalyzer, with RNA samples possessing an RNA integrity number (RIN) ≥ 5 selected for RNA-SEQ library construction. A paired-end library was prepared using an Illumina kit and sequenced on an Illumina HiSeq X Ten platform. Read trimming was performed using Trim Galore (https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/) and the trimmed reads were aligned to the alfalfa (cv Zhongmu No1) reference (https://figshare.com/articles/dataset/Medicago_sativa_genome_and_annotation_files/12623960) via a hierarchical indexing program for transcript concatenation. Read summarization was performed using Feature Counts. Differential expression genes (DEGs) analysis of transcripts was carried out for A8/A0, A16/A8, and A24/A16, employing the likelihood ratio test in DESeq2 and quasi-likelihood method in Edge R. The significance level (p-value) was adjusted using the Benjamini-Hochberg method. Transcripts were identified as differentially expressed when the false discovery rate detected by Edge R and DESeq2 was < 0.01.

2.5.2 GO and KEGG pathway enrichment analysis

GO enrichment analysis of the DEGs was performed using the GOseq R packages, employing the Wallenius non-central hyper-geometric distribution. This approach was effectively adjusted for any bias stemming from gene length in the DEGs (Young et al., 2010). Additionally, the KEGG database serves as a comprehensive resource for comprehending the higher-level functionalities of biological systems, spanning from molecular-level insights to larger-scale datasets obtained through genome sequencing and advanced experimental technologies (accessible via http://www.genome.jp/kegg/). To assess the statistical enrichment of DEGs within KEGG pathways, the KOBAS software (https://www.hospitalitytechexpo.co.uk/exhibitors/kobas/) was utilized.

2.5.3 Weighted Correlation Network Analysis (WGCNA)

WGCNA, conducted using the WGCNA package (v1.72-1) in R (Langfelder and Horvath, 2008), involved co-expression analysis across 12 samples. This analysis sorted DEGs into 9 modules via dynamic tree cutting, each module containing a minimum of 30 genes. Some modules exhibited similarities, prompting the use of a 0.25 similarity threshold for calculating inter-module correlations. The correlation between each module and seed aging stress was computed, and genes with varying expression levels were allocated to specific modules. These co-expression modules were characterized by their total connectivity, intramodular connectivity (functional connectivity), module membership (kME), and kME p-values of DEGs. In the visual representation, each node symbolizes a gene linked to multiple others, with the node size corresponding to the number of connected genes. Associations between module feature genes and different seed aging stages or enzyme activity levels were estimated to understand module-trait relationships.

2.6. RT-qPCR Validation

After a 24-hour imbibition, aging seeds were collected for total RNA isolation, utilizing the Quick RNA Isolation Kit (Huayueyang Biotech Co., Ltd., China). Subsequently, the reverse transcription assay was executed using the SuperMix for qPCR Kit (TransGen Biotech, China). For qRT-PCR analysis, the CFX96 Real-Time System (Bio-Rad, Hercules, CA, USA) was employed, with the MsACTIN gene serving as the internal control. The qRT-PCR cycle program involved an initial step at 95°C for 3 minutes, followed by 40 cycles of 95°C for 15 seconds and 60°C for 30 seconds. Transcript levels of genes were determined using the 2^−ΔΔCt method (Livak and Schmittgen, 2001). The outcomes were presented using GraphPad Prism version 8.0 (accessible via https://www.graphpad.com/, retrieved on March 11, 2022). Details of the primers used for each gene can be found in Table S1.

2.7. Data Analysis and Figure Construction

Statistical analyses were conducted using SPSS Statistics 22 to identify significant differences between aging treatments, employing ANOVA and Duncan's test. The relationships among 31 indicators were assessed through Pearson’s correlation coefficients, and a correlation heatmap was generated using R packages “corrplot” and “pheatmap”. The data for the 31 indicators were transformed into Z-scores using SPSS Statistics 22 to illustrate response patterns. Means and standard errors were computed, and GraphPad Prism version 9.0 was utilized to construct bar charts and line charts. Additionally, UPGMA and Euclidean distance-based cluster analyses were performed using PAST version 3.02.

3.1. Effect of Aging Treatment on Seed Germination Characteristics

Through the process of seed aging from 0 to 32nd day, a range of seed samples with distinct vigor levels was obtained, ranging from an initial germination percentage of 93–0%. After a 4-day aging, a conspicuous and precipitous decline was observed in seed germination percentages and potential (Fig. 1A, B). The MGT exhibited an increasing trend in correspondence with the prolongation of aging duration (Fig. 1C). Simultaneously, there was a gradual reduction in the hard seed percentages as aging time extended (Fig. 1D). In addition, with the extension of aging time, the seedling length and root length of alfalfa seedlings decreased gradually (Fig. 1E). In summary, the aging treatment resulted in a diminution of the alfalfa seeds vigor, primarily manifested through a decreased germination percentage (GP) and germination potential.

3.2. Physiological Changes during Seed Aging

As the duration of the aging treatment prolonged, a progressive elevation in H₂O₂ content was noted, characterized by an abrupt initial surge in content from 4 to 16 days of aging. This trend revealed substantial variations among the treatments, reaching statistical significance (p < 0.05). From 24 to 32 days of aging, H₂O₂ content increased at a slower rate, and there was no significant difference between 28 days and 32 days of aging (Fig. 2A). The MDA content displayed a trend of initial decline followed by an ascent with the prolongation of aging duration. From 4 to 12 days of aging, MDA content decreased, but after 16 days aging, it gradually began to increase, reaching its peak at 28 days aging (Fig. 2B).

The content of AsA displayed an upward trend from 0 to 4 days of aging; however, it gradually declined after 8 days aging. The content of DHA decreased gradually from 0 day to 8 days, remained at higher levels from 12 days to 32 days, and was significantly (p < 0.05) lower at 8 days (Fig. 2C, D). The AsA/DHA ratio declined by approximately five folds from 8 days to 12 days (Fig. 2E). The content of GSH showed a gradual increase trend, whereas its oxidized form, GSSG, displayed a decreasing trend, notably declining in GSSG content after 16 days. Interestingly, the ratio of GSH/GSSG increased with the prolongation of aging duration and remained at higher levels in the 28 days and 32 days aging (Fig. 2F, G, H).

With the prolongation of aging duration, the activities of SOD, CAT, MDHAR, APX and DHAR exhibited a pattern of initial elevation followed by a subsequent decline. The activities of SOD and CAT reached their maximum at 16 days aging, showing significant differences (p < 0.05) compared to 0 day, after which they sharply decreased (Fig. 2I, K). The activity of POD declined significantly (p < 0.05) with the aging from 0 day to 32 days (Fig. 2J). However, the activity of APX increased significantly (p < 0.05) from 0 day to 12 days (Fig. 2L). GR, MDHAR and DHAR activities played the roles for the regeneration of GSH and AsA in the AsA-GSH cycle. The activities of GR increased with the prolongation of aging (Fig. 2M). The activities of MDHAR initially decreased and then increased with the prolongation of aging (Fig. 2O). In contrast, the activity of DHAR gradually increased and then slowly decreased after 16 days aging (Fig. 2N).

3.3. Correlation Analysis and Change Patterns of 31 Indicators

To elucidate the correlations among germination indicators, ROS and MDA content, antioxidant content, antioxidant enzyme activity, and the expression levels of related genes, a correlation analysis was conducted on 31 indices, including germination percentage (GP), mean germination time (MGT), hard seed percentage (HSP); activities of APX, DHAR, SOD, CAT, POD, and GR; contents of GSH, GSSG, AsA, DHA, MDA, and H₂O₂; AsA/DHA and GSH/GSSG ratios; expression levels of MsMDHAR, MsGR, MsDHAR, MsMn-SOD, MsFe-SOD, MsTEL2, MsTERT, MsPOT1a, MsCu/Zn-SOD, MsCAT, and MsAPX; relative telomere length (TRL) and relative telomerase activity (RTA). The results indicated a significant positive correlation between GP and the activities of GR and POD, and a significant negative correlation between GP and MGT, ROS, MDA, and DHA content (Fig. 3).

Based on 31 indexes, hierarchical clustering was employed to categorize nine samples of alfalfa aging seeds into four groups. The first group, labeled as Group N, exclusively comprised control seed samples (GP = 93%). The second group, designated as Group H, included A4 and A8 samples, which exhibited high vigor (GP ranging from 90–87%). The third group, Group M, encompassed A12 and A16 samples, manifesting moderate vigor levels (GP ranging from 60–29%). The fourth group, Group L, included A20, A24, A28 and A32 samples, indicating lower vigor (GP ranging from 18–0%) (Fig. 4).

3.4. Global Analysis of the Time-Course Transcriptome Data from Different Samples

To elucidate the time course of the transition in gene expression on exposure to different seed aging days, we tracked changes in mRNA abundance by RNA-seq at time points (0, 8, 16 and 24 d) after seed aging. After filtering and quality control, a total of 2.05 million clean reads were obtained from the 12 samples, with clean reads exhibiting Q20 and Q30 values exceeding 97% and 93%, respectively. Assembly of the clean reads resulted in a total of 53,363 unigenes, which were subsequently subjected to further analysis. We identified 8,545 differentially expressed genes (DEGs).

Specifically, 1274 genes showed upregulation, and 1580 genes exhibited downregulation in A16_vs_A8 group. However, 583 genes were upregulated, 496 genes were downregulated in A24_vs_A16 group (Fig. 5A). A Venn diagram revealed that there were 20 commonly differentially expressed genes shared among the A8_vs_A0, A16_vs_A8 and A24_vs_A16 treatment groups (Fig. 5B).

3.5. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes Pathway analysis

The GO enrichment analysis of DEGs revealed distinct patterns across experimental groups. In the A8_vs_A0 comparison, among the 1022 identified GO terms, 162 were significantly enriched. Notable terms included response to oxidative stress (GO:0006979), carbohydrate metabolic process (GO:0005975), and oxidoreductase activity (GO:0016491) (Fig. 6A, Supplementary Table S2). For the A16_vs_A8 group, 90 enriched GO terms were observed, highlighting processes such as oxidoreductase activity (GO:0016491), regulation of protein modification (GO:0031399), DNA directed RNA polymerase complex (GO:0006366), and phospholipid catabolic process (GO:0009395) (Fig. 6B, Supplementary Table S3). Lastly, in the A24_vs_A16 group, 19 significantly enriched GO terms were identified, including DNA-templated transcription (GO:0006353), regulation of cytokinesis (GO:0032465), and beta-galactosidase complex (GO:0004565) (Fig. 6C, Supplementary Table S4).

DEGs showed distinct pathway enrichments across experimental groups in the KEGG analysis. In the A8_vs_A0 comparison, notable enrichments were observed in pathways like linoleic acid metabolism and photosynthesis-antenna proteins (Fig. 6D). For the A16_vs_A8 group, significant enrichments were identified in pathways such as oxidative phosphorylation, glycerolipid metabolism, RNA degradation, limonene, and pinene degradation (Fig. 6E). Finally, in the A24_vs_A16 group, DEGs exhibited significant enrichments in pathways including flavonoid biosynthesis, glutathione metabolism, and fatty acid degradation (Fig. 6F).

3.6. Analyzing Gene Expression Related to Oxidative Phosphorylation Pathway

Eukaryotic mitochondria play a crucial role in energy metabolism, influencing various physiological and pathological processes such as free radical generation, cell apoptosis, and aging. Within this study, 30 differentially expressed genes (DEGs) associated with the oxidative phosphorylation pathway during alfalfa seed aging were identified (Fig. 7, Supplementary Table S5). Among these genes, ATPase, comprising 12 genes of three distinct types, showed a significant increase in overall expression after 8 days of aging. However, this trend notably declined at the A16 treatment stage. Additionally, four types of NADH Ubiquinone oxidoreductase chain and seven types of NADH dehydrogenase (ubiquinone) Fe-S genes, such as NDUFS1, NDUFS2, NDUFS3, NDUFS5, ND2, and ND5, were identified in complex I of the mitochondrial electron transport chain (ETC). Their expression generally declined with seed aging.

3.7. Analyzing Gene Expression Related to the Antioxidant Pathway

Throughout seed storage, the accumulation of reactive oxygen species (ROS) induces lipid damage, DNA and protein degradation, resulting in decreased germination percentages and reduced seed vigor. Higher plants primarily employ the AsA-GSH cycle, GPX pathway, CAT pathway, and peroxiredoxin/thioredoxin (PrxR/Trx) pathway as ROS scavenging mechanisms (Fig. 8A). Consequently, DEGs associated with these pathways were identified (Fig. 8B, Supplementary Table S6). A total of 29 DEGs were found within the ROS scavenging system, including 4 CAT, 2 APX, 9 POD, 5 SOD, 1 GPX, and 8 GST genes. Among them, the expression of 2 CAT genes decreased, while the other 2 CAT genes increased, reflecting an overall downward trend. Additionally, the aging process led to decreased expression in 9 POD genes. Lipoxygenase (LOX), a pivotal enzyme in lipid peroxidation, exhibited significant downregulation in A16 and A24. The majority of upregulated GST genes following aging in alfalfa seeds, combined with the decreased expression of most LOX genes, indicated the antioxidant system's partial mitigation of lipid peroxidation, contributing to the observed increase in MDA content. The majority of increased expression of GST genes and decreased expression of LOX genes further supported the antioxidant system's role in alleviating lipid peroxidation, aligning with the significant changes in MDA content. Notably, the expression level alterations in CAT genes were aligned with the previously mentioned physiological indicators.

3.8. WGCNA Analysis of DEGs through Co-Expression Network

WGCNA was employed to delve into the connections between modules and target traits, identifying core genes in the network. Gene expression data from 12 samples underwent WGCNA analysis, focusing on 2,946 retained genes after excluding low-expressed genes, revealing pivotal genes responsive to seed aging. The constructed co-expression network associated with aging responses involved nine modules, each represented by a distinct color and varying in size-ranging from the smallest (violet, 46 genes) to the largest (darkgrey, 1260 genes), with the pink module housing 130 genes. Correlation analysis revealed robust associations between specific modules and aging-related physiological indicators (Fig. 9A). For instance, the darkgrey module displayed a high correlation with APX and DHAR, while the pink module exhibited strong correlations with POD, H₂O₂, GP, and MGT (correlation > 0.8, p < 0.001) (Fig. 9B). Functional enrichment analysis of the two core modules unveiled significant enrichments: the darkgrey module (1260 DEGs) showed notable enrichment in dioxygenase activity, hydrolase activity, and acyltransferase activity (Fig. 9C), while the pink module (130 DEGs) displayed significant enrichments in hydroquinone: oxygen oxidoreductase activity, glycosyltransferase activity, and other processes (Fig. 9D).

The top 10 genes from the darkgrey module and the top 20 genes from the pink module, identified based on their eigengene connectivity (KME) values, were used to construct co-expression subnetworks utilizing Cytoscape_v.3.8.1. Within the darkgrey module, the ribosomal protein L29 (RPL29) gene displayed the highest KME value (Fig. 9E). Furthermore, genes involved in plant hormone biosynthesis, signal transduction, and transcription factors such as SDHA, OPCL, MT2, and Bap31, were identified in the hub gene network. In the pink module, the gene CYCLOPHILIN71 (CYP71) stood out with the highest KME value, alongside genes like HSP70, AP2/ERF, and UFGT7 (Fig. 9F). CYP71 encodes a multi-domain protein pivotal in regulating diverse aspects of plant developmental processes. Its structure comprises peptidyl-prolyl isomerase (PPIase) catalytic activity and WD40 domains.

3.9. Validation of RNA-seq data with the RT-qPCR assay

To authenticate the precision of RNA-seq sequencing, nine DEGs-MsRIO1, MsDCL4, MsPIRL, MsGPT, MsPAT1, MsGST3, MsGH10, MsCYP1A, and MsRPL26-were randomly chosen for RT-qPCR analysis (Fig. 10). Based on the RT-qPCR data presented in Fig. 10, the expression levels of MsRIO1, MsDCL4, MsGPT, MsGST3, and MsRPL26 exhibited a gradual increasing trend with the progression of aging. Conversely, the expression levels of MsPIRL, MsPAT1, MsGH10, and MsCYP1A initially increased and then decreased over time. The FPKM values were compared with the RT-qPCR data. The findings demonstrated concordance between the RT-qPCR and transcriptome data, affirming that the transcriptome data accurately mirrored transcript abundance in this study.

4.1. Dynamic Response of Germination Characteristics and Antioxidant System to Seed Aging in Alfalfa

Seeds with high vigor typically demonstrate notable advantages in germination and subsequent seedling growth. In various studies, artificial aging treatments led to a discernible decline in seed germination percentage, germination potential, germination index, and vigor index. For alfalfa seeds in this study, the germination percentage notably decreased after 12 days of aging and reached zero after 32 days of aging. Similar trends were observed in oat (Avena sativa) seeds, which displayed reduced vigor and germination percentages declining after 24 days of aging, followed by a rapid decrease (Sun et al., 2022). Additionally, aged alfalfa seeds showed a significant increase in mean germination time, consistent with findings in aged dawn redwood (Metasequoia glyptostroboides) seeds following a short artificial aging treatment (Luo et al., 2023). Notably, after a brief aging period of 8 days, the hard seed percentage of aged alfalfa seeds was significantly lower than the control (Fig. 1D). This observation suggests that high-temperature and high-humidity conditions might promote the release of alfalfa seed dormancy. Overall, aging treatments resulted in decreased alfalfa seed vigor, reflected in slower germination percentages, smaller seedlings, and a reduced number of normal seedlings.

Seed aging, a complex physiological and biochemical process, involves numerous reactions (Ratajczak et al., 2019). In this study, as aging progressed, SOD, CAT, MDHAR, APX, and DHAR activities initially increased but later declined. This also might be one of the reasons for the increase of H₂O₂ accumulation in aged alfalfa seed. SOD activity in aged alfalfa seed samples showed significant decrease, which was consistent with studies on mung bean under continuous aging (Nigam et al., 2019). As aging progressed, the H₂O₂ content gradually increased, notably spiking at 4 days of aging, displaying significant differences (p < 0.05) among treatments. CAT played a pivotal role in the antioxidant system, primarily eliminating ROS from plants. Its key function involved clearing H₂O₂, thereby modulating associated signaling pathways. CAT activity predominantly occurred in peroxisomes and glyoxysomes (Ratajczak et al., 2015). In this study, CAT activities peaked at 16 days of aging, significantly differing (p < 0.05) from the control, after which they sharply declined (Fig. 2K). However, APX activities displayed an initial rise followed by a subsequent decline. MDA, a byproduct of lipid peroxidation, and GPX, the primary enzyme responsible for repairing damage caused by lipid peroxidation, served as crucial biological markers for oxidative stress, indicating membrane damage extent under stress conditions. MDA content initially decreased in the early aging stages (from 4 to 12 days) but gradually rose thereafter, reaching its peak at 28 days of aging, signifying severe lipid peroxidation damage during seed aging. The AsA-GSH cycle played a vital role in maintaining membrane protein structure stability during oxidative stress (Li et al., 2021). In alfalfa seeds, the content of AsA displayed an upward trend from 0 to 4 days of aging; however, it gradually declined after 8 days aging. Howere, the content of DHA decreased gradually from 0 day to 8 days, remained at higher levels from 12 days to 32 days, and was significantly (p < 0.05) lower at 8 days (Fig. 2C, D). The AsA/DHA ratio declined by approximately five folds from 8 days to 12 days (Fig. 2E). This indicates that aging leads to a decline in the antioxidant capacity of seeds. Additionally, we found that the content of GSH showed a gradual increase trend, whereas its oxidized form, GSSG, displayed a decreasing trend, notably declining in GSSG content after 16 days. Interestingly, the ratio of GSH/GSSG increased with the prolongation of aging duration and remained at higher levels in the 28 days and 32 days aging. This result is consistent with the findings of Sun et al. (2022) in their study on the aging process of oat (Avena sativa) seeds. The decrease of DHAR enzyme activity significantly reduces the ability of GSH to convert into GSSG.

4.2. Molecular mechanism response of mitochondrial antioxidant system and oxidative phosphorylation to seed aging

The aging process involves a vast array of genetic regulation, orchestrating numerous physiological and biochemical alterations within seeds (Luo et al., 2020). However, as aging progresses, interactions within the gene expression regulation network notably decline. Our study revealed a continuous increase in genes with fregments per kilobase per million (FPKM) values ≤ 0.3 as alfalfa seeds aged. This trend mirrors observations from various studies on seed aging (Wang et al., 2018). Under stress, plants undergo significant transcriptional shifts, with numerous genes experiencing either upregulation or downregulation in response to internal physiological changes. In our investigation, 1274 genes displayed upregulation and 1580 genes showed downregulation in the A16_vs_A8 group. In contrast, 583 genes were upregulated, and 496 genes were downregulated in the A24_vs_A16 group. The observed pattern of DEG changes indicated an initial increase followed by a decrease, with significantly fewer DEGs in the A8_vs_A0 group compared to the A16_vs_A8 group. This suggests that the period between A8 and A16 is critical for the seed's resilience against aging.

Mitochondria, crucial for ATP synthesis in cellular respiration, are major sites for ROS production during seed aging (Navrot et al., 2007). Aging disrupts mitochondrial respiration, involving pathways like COX, AOX, and UCP (Kurek et al., 2019). Cytochrome c release obstructs the mitochondrial ETC, leading to ROS accumulation, cell alterations, and accelerated seed aging (Luo et al., 2023). We observed gradual COX upregulation, indicating hindered electron transport and ROS buildup due to cytochrome c release. The AsA-GSH cycle regulates mitochondrial oxidative balance. Mitochondrial GSH levels rely on cytoplasmic and plastidic synthesis, and mitochondrial GSH depends on transport mechanisms (Chew et al., 2003). Within this study, 30 DEGs associated with the oxidative phosphorylation pathway during alfalfa seed aging were identified. Among these DEGs, according to gene annotation information, we found that there were 12 genes related to ATPase synthesis. The relationship between ATPases and mitochondrial terminal oxidation is quite significant. ATPases, especially ATP synthase, play a crucial role in the process of oxidative phosphorylation, which is integral to mitochondrial terminal oxidation. This relationship involves the coupling of electron transport (via the electron transport chain) and ATP production, making ATPases essential for energy generation in mitochondria.

Mitochondrial GSH levels rely on GR activity, essential for reducing GSH to GSSG. A deficit in mitochondrial GSH can harm mitochondria, impacting thiol protein synthesis and disrupting redox regulation (Schwarzländer and Finkemeier, 2013). AsA plays a role in gene expression, enzyme activity, and cell signaling for redox regulation (Ratajczak et al., 2019). Alterations in mitochondrial AsA synthesis can affect plastid-mitochondria communication (Talla et al., 2011). In this study, while two CAT genes showed decreased expression with seed aging, the remaining two displayed increased expression, indicating an overall downtrend. Downregulation of 2 APX, 9 POD, 5 SOD, 1 GPX, and 8 GST genes confirms the compromised AsA-GSH cycle, leading to ROS accumulation. Elevated ROS triggers membrane lipid peroxidation, compromising cell membrane integrity (Wang et al., 2012). Most LOX genes were significantly downregulated in A16 and A24, correlating with increased ROS content and consequent membrane lipid damage. MDA content consistently rose during seed aging, signaling oxidative damage due to lipid peroxidation. In this study, we identified some potential key genes solely through transcriptome data mining. In future research, we need to perform subcellular localization to confirm the presence of these proteins in the mitochondria, thereby further elucidating their functions.

4.3. Identification of key genes regulating seed vigor

Weighted Gene Co-Expression Network Analysis stands as a robust methodology extensively utilized in the exploration of intricate physiological mechanisms (Fei et al., 2021). Within these analyses, genes exhibiting heightened connectivity within a module are recognized as central genes, forming the fundamental framework of the network and presumed to wield significant influence over specific physiological processes (Long et al., 2022). In a quest to unravel the intricate interplay between genes and traits, WGCNA amalgamates DEGs and physiological indicators associated with seed aging, thus unveiling nine distinct modules. Among these modules, the darkgreen and pink modules emerged as pivotal components. Heat shock proteins (HSPs), acting as molecular chaperones, assume a critical role in preserving protein stability by engaging in peptide folding to shield against oxidative harm (Qi et al., 2011). The augmentation of stress resilience has been documented in plants through the augmentation of small HSPs (sHSPs). Notably, the introduction of the NnHSP17.5 gene from lotus (Nelumbo nucifera) into Arabidopsis has been observed to enhance both seed germination and seedling heat tolerance, indicating the potential involvement of HSPs in responding to oxidative stress during the germination phase (Garbuz, 2017). Heat shock transcription factor (Hsf) operates as a transcriptional regulator, recognizing heat shock elements within the upstream promoter regions of HSP genes, and subsequently transcribing them following activation induced by heat stress (Zhang et al., 2022). The overexpression of Hsf in Arabidopsis seeds led to heightened expression of HSPs, thereby bolstering the anti-aging capabilities (Prieto-Dapena et al., 2006). Hence, Hsf is dynamically involved in stress response mechanisms by orchestrating the upregulation of Heat Shock Proteins (HSPs). Within the pink module, the expression pattern of HSP70 exhibited a strong correlation with both the abundance and generation rate of H₂O₂. Scholarly investigations have elucidated the pivotal role of Hsf in the context of plant stress responses, primarily attributed to its ability to generate ROS. This generation of ROS facilitates the induction of host defense genes, phosphorylation of protein kinases, activation of transcription factors, and modulation of ion transport systems (Chapman et al., 2019). The multifaceted role of ROS in plant stress response encompasses its function as a dual agent: acting both as a hazardous substance leading to oxidative stress and as a signaling molecule that mediates an array of biological processes (Wang et al., 2022a).

Cytochrome P450 (CYP450) enzymes comprise a superfamily of heme-containing monooxygenases, pivotal in the intricate metabolism of diverse endogenous and exogenous compounds. Research on CYP450 enzymes has witnessed significant progress in recent years (Kumar et al., 2018). CYP450(CYP71) is a heme-containing monooxygenase that catalyzes oxidation, hydroxylation, epoxidation and dealkylation, and is mainly involved in the detoxification pathway in plants (Rasool and Mohamed, 2016). CYP450 enzymes are major players in drug metabolism, and research in pharmacogenomics has revealed significant inter-individual variability in CYP450 activity (Biswas et al., 2023). This variability had implications for individualized drug therapy, with the identification of genetic polymorphisms influencing drug metabolism. Within the pink module, CYP71 surfaced as an integral component within the hub gene network. Moreover, within the hub gene network, a cluster of genes associated with plant hormone biosynthesis, signal transduction, and transcription factors, exemplified by SDHA, OPCL, MT2, and Bap31, were notably identified.

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 and potential exhibited a decreased trend in correspondence 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, and antioxidant AsA content also exhibited a decrease trend, which lead to the disruption of ROS balance and a significant increase in ROS levels, exacerbating seed aging. Based on transcriptome, 29 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 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 mitochondrial antioxidant system and 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.

Author contribution statement

P.M. supervised the experiments. S.S., C.M., and W.M. performed the experiments. S.S. analyzed the date. S.S. wrote the manuscript. All the authors read and approved the final manuscript.

Declaration of competing interest

The authors assert that this research was conducted without any affiliations or financial involvements that might be interpreted as potential conflicts of interest.

Data availability

The data referenced in this study are comprehensively detailed within the article and Supplementary Materials. Additionally, the raw sequencing data utilized for the transcriptome investigation are accessible via the NCBI’s Sequence Read Archive (SRA, https://www.ncbi.nlm.nih.gov/sra), specifically within the BioProject PRJNA1012365. The genomic information pertinent to the Zhongmu No.1 alfalfa variety was sourced from the figshare website (https://figshare.com/articles/dataset/Medicago_sativa_genome_and_annottion_files).

Acknowledgments:

We are grateful to Biomarker Technologies Co., Ltd (Beijing, China) for providing sequencing services. The investigation received financial support from the National Natural Science Foundation of China (32371760) and Key Technology Researches for Seed Propagation of Alfalfa with Saline and Alkaline Tolerance and Drought Resistance (2022ZD0401105).

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Dynamic Responses of Germination Characteristics and Antioxidant Systems to Alfalfa (Medicago sativa) Seed Aging Based on Transcriptome

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Abstract

Figures

1. Introduction

2. Materials and Methods

2.1. Preparation of Seeds

2.2. Aging Treatment

2.3. Germination Parameters Tests

2.4. Physiological Analysis

2.5. Transcriptome analysis

2.5.1 RNA extraction, cDNA construction and differentially expressed genes identification

2.5.2 GO and KEGG pathway enrichment analysis

2.5.3 Weighted Correlation Network Analysis (WGCNA)

2.6. RT-qPCR Validation

2.7. Data Analysis and Figure Construction

3. Results

3.1. Effect of Aging Treatment on Seed Germination Characteristics

3.2. Physiological Changes during Seed Aging

3.3. Correlation Analysis and Change Patterns of 31 Indicators

3.4. Global Analysis of the Time-Course Transcriptome Data from Different Samples

3.5. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes Pathway analysis

3.6. Analyzing Gene Expression Related to Oxidative Phosphorylation Pathway

3.7. Analyzing Gene Expression Related to the Antioxidant Pathway

3.8. WGCNA Analysis of DEGs through Co-Expression Network

3.9. Validation of RNA-seq data with the RT-qPCR assay

4. Discussion

4.1. Dynamic Response of Germination Characteristics and Antioxidant System to Seed Aging in Alfalfa

4.2. Molecular mechanism response of mitochondrial antioxidant system and oxidative phosphorylation to seed aging

4.3. Identification of key genes regulating seed vigor

5. Conclusions

Declarations

References

Additional Declarations

Supplementary Files

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