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Article

Golden Hull: A Potential Biomarker for Assessing Seed Aging Tolerance in Rice

by
Jing Ye
1,†,
Chengjing Wang
2,†,
Ling Chen
3,4,
Rongrong Zhai
1,
Mingming Wu
1,
Yanting Lu
1,
Faming Yu
1,
Xiaoming Zhang
1,
Guofu Zhu
1,* and
Shenghai Ye
1,5,*
1
Institute of Crop and Nuclear Technology Utilization, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
2
Laboratory of Seed Science and Technology, Guangdong Key Laboratory of Plant Molecular Breeding, State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, South China Agricultural University, Guangzhou 510642, China
3
National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya 572025, China
4
Research and Development Center of Rice Cropping Technology, China National Rice Research Institute, Hangzhou 310006, China
5
State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-Products, Hangzhou 310021, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agronomy 2024, 14(10), 2357; https://doi.org/10.3390/agronomy14102357
Submission received: 3 September 2024 / Revised: 6 October 2024 / Accepted: 11 October 2024 / Published: 12 October 2024
(This article belongs to the Special Issue Innovative Research on Rice Breeding and Genetics)
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Abstract

:
Seed aging is a complex process that involves various physiological and biochemical changes leading to a decline in seed viability during storage. However, the specific biomarkers for assessing the degree of seed aging in rice remain elusive. In this study, we isolated a golden hull mutant, gh15, from the indica rice Z15 by employing a radiation mutagenesis technique. Compared with the wild type (WT) Z15, the mutant gh15 displayed a golden hue in the hull, stem, and internodes, while no significant differences were observed in the key agronomic traits. A genetic analysis showed that the gh15 phenotype is regulated by a single recessive gene, which possibly encodes cinnamyl alcohol dehydrogenase OsCAD2. Significant differences of seed aging tolerance were observed between gh15 and WT after six months of natural storage and artificial aging treatment, with gh15 exhibiting a markedly lower aging tolerance compared to the WT. Haplotype assays indicated that the Hap2 of OsCAD2 was significantly associated with the dark coloration of the hull and lower seed aging tolerance. The molecular marker of OsCAD2 associated with seed color was explored in rice. These findings demonstrate that the golden hull serves as a potential biomarker for the rapid assessment of seed aging tolerance in rice.

1. Introduction

Seeds inevitably experience a decline in viability over the course of storage because of the aging process. This natural seed aging is significantly influenced by factors such as storage temperature and moisture levels [1]. Thus, seed aging tolerance is a crucial trait for both agricultural productivity and the conservation of crop germplasm [2]. Rice (Oryza sativa) stands as one of the world’s most significant crops, playing a pivotal role in global food security and agricultural economies. Rice varieties exhibit a range of seed aging characteristics that are influenced by a combination of genetic factors and environmental conditions [3]. Employing a precise assessment method to screen seed-aging-resistant varieties offers a cost-effective strategy for the development of rice cultivars with enhanced seed longevity.
Seed aging is a complex process that involves various physiological and biochemical changes during storage. The variation of metabolites in seeds can be used as a biomarker for assessing the degree of seed aging in plants to some extent. For example, the concentration of galactinol in fully matured and desiccated seeds serves as a reliable bioindicator of seed aging, particularly within the Brassicaceae and tomato [4]. The levels of inositol galactosides are recognized as a key biomarker of seed vigor in Arabidopsis, cabbage, and tomato [4]. Meanwhile, raffinose has been identified as a significant factor associated with the tolerance to seed aging in maize [5]. In rice, the primary cause of the decline in seed vigor is attributed to the progressive accumulation of oxidative damage during seed storage [6]. Antioxidants, such as ascorbic acid, vitamin E, and glutathione, serve to remove excess free radicals in seeds and also act as biomarkers for assessing seed aging tolerance [7]. Furthermore, the antioxidant enzyme and flavonoid in the seed coat positively contribute to the enhancement of seed aging tolerance [8,9] and might be valuable indicators of the seed aging degree. The specific metabolites within the rice grain hull that influence seed aging remain largely unexplored.
Several golden hull and internode (gh) mutants have been developed in rice, characterized by a distinctive reddish-brown pigmentation in both the hull and internode regions [10,11]. For example, the rice gh2 mutant, characterized by a deficiency in lignin, was identified through a map-based cloning approach. The GH2 gene, which is responsible for this mutation, encodes a cinnamyl alcohol dehydrogenase (CAD) enzyme [11]. CAD catalyzes the terminal step in the monolignol biosynthetic pathway, facilitating the conversion of cinnamyl aldehydes to alcohols, with NADPH serving as the required cofactor [12]. To date, several CADs have been reported to be involved in plant defense responses. For instance, an elevated expression of CAD is expected to stimulate lignin biosynthesis in wheat, thereby augmenting the mechanical strength and lodging resistance of stems [13]. In Arabidopsis, AtCAD7 and AtCAD8 have been correlated with the biosynthesis of compounds involved in plant defense mechanisms [14,15]. Traditionally, rice gh mutants have served as valuable marker genes in rice breeding programs and genetic research [10]. The potential of rice’s golden hull to serve as a biomarker for assessing the degree of seed aging remains largely unexplored.
In this study, we successfully generated a gh15 mutant in rice, which exhibits distinctive golden hulls. The gh15 phenotype was potentially governed by OsCAD2. Interestingly, we revealed that the gh15 mutant plants exhibit reduced tolerance to seed aging compared with the wild-type plants. Furthermore, we demonstrated a correlation between natural variations in the OsCAD2 gene and seed aging tolerance in rice. These findings demonstrate the potential of the golden hull to serve as a biomarker for the rapid assessment of seed aging tolerance in rice.

2. Materials and Methods

2.1. Plant Materials

The rice gh15 mutant used in this study was initially identified through γ-ray mutagenesis using 60Co radiation in the indica line Z15 (a stable breeding material). The gh15 mutant was crossed with its parental line Z15 to generate the F2 mapping population for genetic analysis. The plants were grown at the Zhejiang Academy of Agricultural Sciences (Hangzhou, Zhejiang Province, China; latitude 30°18′41.2″ N, longitude 120°11′42.5″ E) according to local agricultural practices. The seeds were harvested at the full-ripe stage for subsequent use.

2.2. Construction of Bulked DNA Pools for Sequencing

Two DNA pools for sequencing were assembled by selecting individuals with pronounced extreme phenotypes from a population of F2 segregating plants. Among 255 F2 segregating population, 50 individuals displaying the wild-type grain color were categorized into the WT pool, while another 50 individuals yielding golden grains were assigned to the MT pool. The total genomic DNA was meticulously extracted from the young leaves of both WT and MT and the two pools of F2 segregating individuals utilizing the sodium dodecyl sulfate (SDS) method. Subsequently, libraries were constructed and sequenced following the manufacturer’s protocol for the Illumina HiSeq platform [16] at BGI Corporation (https://www.bgi.com/, accessed on 3 May 2024).

2.3. Analysis of Mixed Pool Sequencing Data

The quality of the original paired-end reads, acquired through sequencing, was meticulously assessed, and reads of low quality were subsequently filtered out. The resulting clean reads were then aligned and mapped onto the MSU7 reference genome [17]. The Genome Analysis Toolkit (GATK) was employed to detect single nucleotide polymorphisms (SNPs) and insertions/deletions (InDels) [18], followed by the utilization of SnpEff for the functional annotation of these genetic variants [19]. The SNP index and InDel index calculations were performed to pinpoint candidate genomic regions potentially associated with variations in grain color [20].

2.4. Seed Aging Treatment

The seeds were subjected to a natural aging test, being stored under room conditions with an average temperature fluctuation ranging from 3 °C to 30 °C and an average humidity of 70% over a two-year period from July 2022 to July 2024. Meanwhile, artificial seed aging was induced by immersing the seeds in water at 58 °C for 1 h. Three biological replicates were performed.

2.5. Seed Germination

A total of 30 seeds from each replicate were sown on moistened filter paper within Petri dishes and incubated at a constant temperature of 25 °C for 10 days. The germinated seeds were counted every day. Finally, the traits, including germination percentage and seedling percentage, were calculated. Three biological replicates were performed.

2.6. Nucleotide Diversity and Haplotype Assays

The nucleotide diversity (Pi) within a 100 kb flanking region of OsCAD2 and the haplotype network were ascertained utilizing data from the ECOGEMS database (https://venyao.xyz/ECOGEMS/, accessed on 5 May 2024) [21]. The haplotypes of the candidate genes were determined using the databases from http://www.ricediversity.org/ (accessed on 5 May 2024) [22]. Only haplotypes represented by more than ten accessions were considered.

2.7. Marker Development

One CAPS marker was developed from comparisons of the original or CAPS length between different colored populations of indica and japonica according to the data published at http://www.ncbi.nlm.nih.gov (accessed on 6 June 2024). PCR genotyping was performed by PCR using the following primers: forward primer: AACTGATAAGTCCTCACCTACCC; reverse primer: AATTTTGGAGCCGGCAGT. The PCR was conducted with an initial step of 98 °C incubation for 30 s, followed by 30 cycles of 98 °C for 10 s, 58 °C for 5 s, and 72 °C for 1 s, with a final extension at 72 °C for 1 min. Following the digestion of the PCR product with the restriction enzyme Hae III, the sample was subsequently analyzed using agarose gel electrophoresis to visualize the results.

2.8. Data Analysis

Significant differences between the samples were compared using Student’s t-test or an analysis of variance (ANOVA) test.

3. Results

3.1. Characterization of the gh15 Mutant

The gh15 mutant plants showed similar agronomy phenotypes, such as plant height, effective tiller, panicle length, grain length, grain width, and grain thickness, compared with the wild type (WT) Z15 under normal cultivated condition (Figure 1a–g). However, gh15 exhibited an obvious reddish-brown pigment in the hulls, internodes, and stems at the heading stage (Figure 1h). In the two F2 populations, the ratio of plants exhibiting the WT phenotype to those displaying the gh15 mutant phenotype was observed to be 3:1 (Figure 1i). This suggests that the gh15 phenotype is regulated by a single recessive gene.

3.2. OsCAD2 Putatively Responsible for gh15 Phenotype

To identify the candidate gene associated with the gh15 phenotype, sequencing was conducted for both parental lines and the two pools (WT pool and MT pool), achieving an average sequencing coverage depth of 50-fold. The SNPs and InDels, which were homozygous in each parent and displayed polymorphism between the parents, were selected for bulked segregant analysis sequencing (BSA-seq) analysis. Euclidean distance (ED) values were used to identify the candidate quantitative trait loci (QTL) regions, and one obviously significant peak in the ED distribution was detected on Chromosome 2 (Figure 2a). In this region, the top 1% values of the ED4 calculation results are considered significant results and are annotated accordingly. OsCAD2 has previously been shown to be involved in the phenotypes of golden hull and internode characteristics [11]. The specific deleted nucleotides are highlighted in Figure 2b, where next to the last exon near the 3′ end of the OsCAD2 sequence, the deletion of two nucleotides were CT (Figure 2b). The candidate gene, OsCAD2, was chosen to be sequenced, and OsCAD2 underwent a frameshift mutation in its amino acid sequence due to the deletion of two nucleotides in gh15 compared with WT (Figure 2c). These data indicate that the OsCAD2 might be the candidate gene regulating the gh15 phenotype in rice.

3.3. gh15 Involved in the Regulation of Seed Aging Tolerance

Under non-aged conditions, the gh15 mutant displayed a germination performance that was comparable to the WT (Figure 3a,b). However, the gh15 mutant exhibited reduced tolerance to the artificial aging treatment. After the artificial aging treatment, the gh15 mutant showed a drastic reduction in seedling percentage when compared to the WT plants (Figure 3c,d). Similarly, after two years of natural storage, the gh15 mutant experienced significant seed aging, while this was not observed to the same extent in the WT. The seedling percentage was significantly reduced in the gh15 mutant compared with WT after natural storage (Figure 3e,f). These results indicate that gh15 is involved in the regulation of seed aging tolerance in rice.

3.4. Natural Variation of OsCAD2 in Rice

To investigate the evolutionary history of OsCAD2, a haplotype network of OsCAD2 haplotypes was constructed (https://venyao.xyz/ECOGEMS/, accessed on 5 May 2024). The majority of haplotypes in indica accessions originate from Oryza rufipogon I and II (Or-I and Or-III) (Figure 4a). Natural variation assays showed that the nucleotide diversity (Pi) value of OsCAD2 is significantly lower in both indica and japonica compared with that of wild rice (Figure 4b). The distribution of haplotypes was further investigated in rice accessions. Interestingly, Hap1 was mainly observed in the japonica accessions, whereas virtually all the indica accessions were found to contain Hap2 (Figure 4c).

3.5. Elite Haplotype of OsCAD2 Associated with Seed Aging Tolerance

To identify the elite haplotype of OsCAD2 associated with seed aging tolerance, each of the three accessions containing Hap1 or Hap2 were randomly selected for artificial aging treatment (Figure 5a). Hap1 was identified as the elite haplotype associated with a light grain color but high aging tolerance. The varieties containing Hap1 and Hap2 did not differ in germination and seedling percentage under the non-aged condition (Figure 5b–d). However, the varieties containing Hap1 had higher germination and seedling percentages compared with the varieties containing Hap2 after the artificial aging treatment (Figure 5e–g). These results suggest that OsCAD2 is a putative biomarker for assessing seed aging tolerance.

3.6. Development of a Molecular Marker for OsCAD2

To further reveal the application of OsCAD2 on the selection of accessions, the development of a marker for OsCAD2 was conducted. The sequences of OsCAD2 in varieties with light and dark colors were analyzed (Figure 6a), and an SNP change was found that created a difference in the Hae III enzyme cutting site (Figure 6b). Based on these results, we developed a marker for OsCAD2 that can be used to identify accessions with light color and aging tolerance for future breeding (Figure 6c).

4. Discussion

It is common for rice seeds to be exposed to high temperatures and humid conditions during storage, which can lead to the process of seed aging, negatively impacting their viability. Thus, the rapid identification of seed aging tolerance is essential for maintaining seed quality and viability throughout storage [23]. Rice gh mutants have long been used as distinctive morphological markers in genetic and breeding studies [24,25]. It has been reported that the OsCAD2 gene is responsible for the golden hull and internode characteristics in the gh2 mutant [11]. In this study, we isolated a novel gh15 mutant in rice that exhibits pigmentation in the hull, stem, and internode regions. Through genetic analysis, we assumed that OsCAD2 is a likely candidate gene for the golden hull phenotype observed in the gh15 mutant, as suggested by previous studies [10,11]. Interestingly, we observed that the gh15 mutant had lower tolerance to seed aging compared with the WT. This finding, coupled with the identification of the pigmented hull phenotype, offers significant insights and serves as a valuable indicator for assessing seed aging tolerance in rice.
It has been reported that OsCAD2 exhibits constitutive expression across all stages of rice development. Furthermore, its expression levels have been observed to increase in response to various biotic and abiotic stresses, such as M. grisea, Xoo and Xoc infections, and UV and cold treatments [26]. In this study, we revealed that OsCAD2 may be involved in the seed aging tolerance of rice. CAD catalyzes the final step of the phenylpropanoid pathway, which plays a pivotal role in the deposition of lignin within the secondary cell walls. Additionally, CAD contributes to plant defense mechanisms by facilitating stress-induced lignification and inducing the synthesis of phenolic compounds [27,28,29]. The relationship between lignin and seed storability is intricately linked to the regulation of the cell wall structure [30]. Lignin, a robust phenolic polymer, enhances the cell wall’s structural integrity and resistance to degradation, thereby affecting the penetration of external moisture and oxygen to the embryo during seed storage, which is essential for maintaining seed vitality during storage [30]. Thus, the OsCAD2 gene might be a promising candidate associated with seed aging tolerance and warrants further investigation.
Metabolites have significant potential use as biomarkers for the identification of seed aging tolerance. For example, the galactinol content within the mature seeds serves as a biomarker for seed longevity in Brassicaceae and tomato [4]. A recent study has indicated that the absolute levels of galactose and gluconic acid in rice seeds could serve as potential candidate markers for assessing seed aging [23]. In this study, to determine whether the natural variations in the OsCAD2 gene are associated with differences in seed aging tolerance, we conducted an analysis of the allelic diversity of OsCAD2. The Hap2 showed a significant negative correlation with seed aging tolerance and was predominantly found in rice accessions characterized by a golden hull. Variation in the promoter region resulted in differences in the seed viability of different haplotypes after artificial aging, and Hap1 was identified as an elite haplotype associated with a light grain color and high aging tolerance. However, whether the variant in the intron region influences seed vigor needs further study. The genetic relationships between the natural variation of OsCAD2 with seed aging tolerance await further investigation. Several elite accessions were identified that have the better seed aging tolerance coupled with a lighter hull coloration. These findings suggest that the hull color serves as a potential biomarker for the rapid assessment of seed aging tolerance in rice. Future studies should improve the reliability of rapid testing seed aging tolerance based on the grain color in other crops.
In conclusion, this study has demonstrated that the rice gh15 mutant significantly impacts the tolerance to seed aging. The OsCAD2 gene is a potential candidate that regulates the golden hull phenotype of gh15, and it is likely to be involved in the defense responses to seed aging stress. The natural allelic variation in the OsCAD2 gene provides insights into how grain color is associated with seed aging tolerance in rice. This understanding facilitates the improvement of seed aging tolerance through the application of gene editing technologies and genomic tools. The potential use of grain color as a biomarker for identifying seed aging tolerance in other crops merits further investigation in the future.

Author Contributions

Conceptualization, S.Y., G.Z. and X.Z.; methodology, S.Y., X.Z. and M.W.; software, C.W., L.C., R.Z. and F.Y.; validation, J.Y., G.Z. and M.W.; formal analysis, J.Y., M.W. and C.W.; investigation, Y.L., F.Y. and M.W.; resources, F.Y. and S.Y.; data curation, J.Y. and F.Y.; writing—original draft preparation, C.W., L.C., R.Z. and S.Y.; writing—review and editing, J.Y., Y.L. and S.Y.; visualization, C.W. and R.Z.; supervision, X.Z.; funding acquisition, S.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Hainan Province Science and Technology Special Fund, China (ZDYF2023XDNY086, S.Y.); the Project of Sanya Yazhou Bay Science and Technology City, China (SCKJ-JYRC-2022-87, L.C.); the Major Science and Technology Project for Breeding of New Rice Varieties in Zhejiang Province (2021C02063-5, S.Y.); and the National Rice Industry Technology System (2024CARS-01-87, S.Y.).

Data Availability Statement

The data presented in this study are available upon request from the corresponding author.

Acknowledgments

We are grateful to the editor and the anonymous reviewers for their insightful comments, which helped us to improve our paper substantially.

Conflicts of Interest

The authors declare no conflicts of interest.

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Agronomy 14 02357 g001
Figure 1. Phenotype of wild type (WT) and gh15 mutant. (a) Representative images of WT and gh15 mutant plants. Comparison of (b) plant height, (c) panicle length, (d) effective tiller, (e) grain length, (f) grain width, and (g) grain thickness between WT and gh15 mutant. Data are presented as means ± SD; n = 10. n.s. stands for not significant. (h) Representative images of internode, stem, and panicle among WT and gh15 mutant at the heading stage. (i) The segregation of the gh15 mutant phenotype in relation to the WT phenotype.
Figure 1. Phenotype of wild type (WT) and gh15 mutant. (a) Representative images of WT and gh15 mutant plants. Comparison of (b) plant height, (c) panicle length, (d) effective tiller, (e) grain length, (f) grain width, and (g) grain thickness between WT and gh15 mutant. Data are presented as means ± SD; n = 10. n.s. stands for not significant. (h) Representative images of internode, stem, and panicle among WT and gh15 mutant at the heading stage. (i) The segregation of the gh15 mutant phenotype in relation to the WT phenotype.
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Figure 2. Putative candidate gene for gh15 phenotype using BSA-seq analysis. (a) Manhattan plot showing the distribution of Euclidean distance (ED) on chromosomes. (b) The putative candidate gene OsCAD2. (c) Comparisons of amino acids of OsCAD2 between WT and gh15 mutant.
Figure 2. Putative candidate gene for gh15 phenotype using BSA-seq analysis. (a) Manhattan plot showing the distribution of Euclidean distance (ED) on chromosomes. (b) The putative candidate gene OsCAD2. (c) Comparisons of amino acids of OsCAD2 between WT and gh15 mutant.
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Figure 3. Comparison of seed aging tolerance between gh15 mutant and wild type. Representative images of seed germination under (a) non-aged, (c) artificial aging (AA), and (e) natural aging (NA) conditions among WT and gh15 mutant plants. Comparison of seedling percentage under (b) non-aged, (d) artificial aging, and (f) natural aging conditions between WT and gh15 mutant plants. Data are presented as means ± SD; n = 3. Significant differences were determined by two-tailed Student’s t-tests (** p < 0.01). n.s. stands for not significant.
Figure 3. Comparison of seed aging tolerance between gh15 mutant and wild type. Representative images of seed germination under (a) non-aged, (c) artificial aging (AA), and (e) natural aging (NA) conditions among WT and gh15 mutant plants. Comparison of seedling percentage under (b) non-aged, (d) artificial aging, and (f) natural aging conditions between WT and gh15 mutant plants. Data are presented as means ± SD; n = 3. Significant differences were determined by two-tailed Student’s t-tests (** p < 0.01). n.s. stands for not significant.
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Figure 4. Natural variation of OsCAD2 in rice accessions. (a) An OsCAD2 haplotype network. The size of the circles is proportional to the number of samples for a given haplotype. (b) Nucleotide diversity (Pi) across a 100 kb region spanning the OsCAD2 gene in wild, indica, and japonica rice. (c) Distribution of haplotypes in rice accessions.
Figure 4. Natural variation of OsCAD2 in rice accessions. (a) An OsCAD2 haplotype network. The size of the circles is proportional to the number of samples for a given haplotype. (b) Nucleotide diversity (Pi) across a 100 kb region spanning the OsCAD2 gene in wild, indica, and japonica rice. (c) Distribution of haplotypes in rice accessions.
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Figure 5. Elite haplotype of OsCAD2 associated with seed aging tolerance in rice. (a) The randomly selected accessions containing Hap1 or Hap2 for artificial aging treatment. Representative images of seed germination under (b) non-aged and (e) artificial aging conditions. Comparison of germination percentage and seedling percentage under (c,d) non-aged and (f,g) artificial aging conditions. Data are presented as means ± SD; n = 3. Significant differences were determined by two-tailed Student’s t-tests (** p < 0.01). n.s. stands for not significant.
Figure 5. Elite haplotype of OsCAD2 associated with seed aging tolerance in rice. (a) The randomly selected accessions containing Hap1 or Hap2 for artificial aging treatment. Representative images of seed germination under (b) non-aged and (e) artificial aging conditions. Comparison of germination percentage and seedling percentage under (c,d) non-aged and (f,g) artificial aging conditions. Data are presented as means ± SD; n = 3. Significant differences were determined by two-tailed Student’s t-tests (** p < 0.01). n.s. stands for not significant.
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Figure 6. The development of a molecular marker for OsCAD2. (a) Comparison of the OsCAD2 sequences in accessions with different colors. (b) An SNP change that created a difference in the Hae III enzyme cutting site. (c) Confirmation of the OsCAD2 marker for identification of accessions with different colors. NSFTV72-IR 8, NSFTV73-IRAT 177, NSFTV218-PI 298967-1, NSFTV296-Dosel, NSFTV228-CA 902/B/2/1, NSFTV325-EMATA A 16-34, NSFTV328-Jamir, NSFTV332-KPF-16.
Figure 6. The development of a molecular marker for OsCAD2. (a) Comparison of the OsCAD2 sequences in accessions with different colors. (b) An SNP change that created a difference in the Hae III enzyme cutting site. (c) Confirmation of the OsCAD2 marker for identification of accessions with different colors. NSFTV72-IR 8, NSFTV73-IRAT 177, NSFTV218-PI 298967-1, NSFTV296-Dosel, NSFTV228-CA 902/B/2/1, NSFTV325-EMATA A 16-34, NSFTV328-Jamir, NSFTV332-KPF-16.
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Ye, J.; Wang, C.; Chen, L.; Zhai, R.; Wu, M.; Lu, Y.; Yu, F.; Zhang, X.; Zhu, G.; Ye, S. Golden Hull: A Potential Biomarker for Assessing Seed Aging Tolerance in Rice. Agronomy 2024, 14, 2357. https://doi.org/10.3390/agronomy14102357

AMA Style

Ye J, Wang C, Chen L, Zhai R, Wu M, Lu Y, Yu F, Zhang X, Zhu G, Ye S. Golden Hull: A Potential Biomarker for Assessing Seed Aging Tolerance in Rice. Agronomy. 2024; 14(10):2357. https://doi.org/10.3390/agronomy14102357

Chicago/Turabian Style

Ye, Jing, Chengjing Wang, Ling Chen, Rongrong Zhai, Mingming Wu, Yanting Lu, Faming Yu, Xiaoming Zhang, Guofu Zhu, and Shenghai Ye. 2024. "Golden Hull: A Potential Biomarker for Assessing Seed Aging Tolerance in Rice" Agronomy 14, no. 10: 2357. https://doi.org/10.3390/agronomy14102357

APA Style

Ye, J., Wang, C., Chen, L., Zhai, R., Wu, M., Lu, Y., Yu, F., Zhang, X., Zhu, G., & Ye, S. (2024). Golden Hull: A Potential Biomarker for Assessing Seed Aging Tolerance in Rice. Agronomy, 14(10), 2357. https://doi.org/10.3390/agronomy14102357

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