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26 pages, 3134 KiB  
Review
Seed Storability in Forest Trees: Research Progress and Future Perspectives
by Hao Cai, Jun Shao and Yongbao Shen
Forests 2025, 16(3), 467; https://doi.org/10.3390/f16030467 - 6 Mar 2025
Viewed by 78
Abstract
The long-term storage of forest tree seeds holds critical significance for ecological restoration, forest resource conservation, and the sustainable development of forestry. In the context of plant biodiversity conservation, enhancing seed storability to achieve efficient utilization has garnered widespread attention. Seed storability, as [...] Read more.
The long-term storage of forest tree seeds holds critical significance for ecological restoration, forest resource conservation, and the sustainable development of forestry. In the context of plant biodiversity conservation, enhancing seed storability to achieve efficient utilization has garnered widespread attention. Seed storability, as a complex quantitative trait, is influenced by the combined effects of intrinsic seed characteristics and external environmental factors. The complexity of this issue presents significant challenges in maintaining seed longevity, particularly in the conservation of seeds from endangered species. This review discusses the essential factors affecting seed storability and the main causes of seed aging. It emphasizes the roles of molecular mechanisms, including raffinose family oligosaccharide (RFO), heat shock protein (HSP), late embryogenesis abundant (LEA) proteins, seed storage proteins (SSPs), and hormonal regulation, in modulating seed storability. Additionally, the evaluation criteria and methodologies for assessing seed storability are elaborated. The review highlights future research challenges, aiming to provide a comprehensive scientific foundation and practical guidance to improve seed storability. This will offer theoretical support for the sustainable management of forest resources. Full article
(This article belongs to the Section Genetics and Molecular Biology)
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<p>Key factors influencing seed storability. This figure was summarized and modified according to Zhou et al. [<a href="#B7-forests-16-00467" class="html-bibr">7</a>] and Choudhary et al. [<a href="#B33-forests-16-00467" class="html-bibr">33</a>].</p>
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<p>Major reactions that generate and eliminate ROS occur during seed aging under humid conditions (RH above 60%). I, NADH dehydrogenase; II, succinate dehydrogenase; III, cytochrome bc<sub>1</sub> complex; VI, cytochrome c oxidase; V, ATP synthase. The figure visualization was created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p>
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<p>Lipid peroxidation during seed aging under humid conditions (RH above 60%). The figure visualization was created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p>
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<p>Conceptual diagram of molecular regulatory mechanisms to enhance seed storability. The figure visualization was created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p>
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<p>Endogenous hormonal pathways regulating seed storability.</p>
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<p>Past practices and future innovations in enhancing seed longevity and storability.</p>
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27 pages, 7271 KiB  
Article
Cultivars and Their Developmental Phases Interact with Temperature Fluctuations to Modulate Growth, Productivity and Seed Tuber Physiology of Potatoes (Solanum tuberosum L.)
by Morgan D. Southern, Mohan G. N. Kumar and Jacob M. Blauer
Plants 2025, 14(5), 750; https://doi.org/10.3390/plants14050750 - 1 Mar 2025
Viewed by 204
Abstract
In view of raising concerns of climate change, the impact of temperature on potato (Solanum tuberosum L.) growth and productivity was investigated by planting at different times to expose plants to natural variations in air and soil temperatures. Over two seasons with [...] Read more.
In view of raising concerns of climate change, the impact of temperature on potato (Solanum tuberosum L.) growth and productivity was investigated by planting at different times to expose plants to natural variations in air and soil temperatures. Over two seasons with differing temperature patterns, emergence, stem and tuber numbers, tuber size distribution, yield, processing quality, and seed tuber behavior were analyzed. Postharvest, tubers from each planting were stored and replanted to assess temperature carryover effects. Generally, delayed plantings increased the average number of stems per plant (37%) but did not alter the tuber numbers per plant. Early (18 April) and mid-season (9 May) plantings produced higher yields, while late planting (30 May) reduced total yield (42%), US No. 1 yield (48%), and tuber numbers (34%). Moreover, the storage period influenced subsequent stems per plant more than the prior-year temperature conditions. Optimal productivity was achieved by planting during cooler establishment temperatures, followed by warmer tuberization and relatively cooler bulking temperatures. Diurnal temperature variations and growing degree days had minimal effects on stems per plant, whereas storage duration (chronological age) and temperature significantly impacted physiological aging. These findings help growers optimize planting times to enhance tuber storability and yield to improve end use. Full article
(This article belongs to the Special Issue Potato Production: From Quality Formation to Stress Tolerance)
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<p>Effect of staggered planting (see <a href="#plants-14-00750-t001" class="html-table">Table 1</a> for planting dates) on sprout emergence, and crop growth and development. The experiment was laid out in a split-plot completely randomized block design with <span class="html-italic">cvs.</span> Russet Norkotah and Shepody (not distinguishable in the picture due to infield plot randomization). The 2022 developmental stages of early, mid-, and late plantings at 54, 33, and 11 DAP, respectively, are presented in (<b>A</b>). In contrast to mid- and late plantings, the early plantings developed a significant vine growth, and in combination with cooler spring weather of 2022, contributed to cooler soil temperatures, coinciding with tuberization. Photo (<b>B</b>) shows the crop as in (<b>A</b>) at 141 (early), 120 (mid), and 98 (late) DAP. (Note: Photo (<b>B</b>), 7 days after tuber harvest for the early planting, the day vines were mechanically removed for the mid-planting, and the actively growing vines for the late planting).</p>
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<p>Air and soil temperatures during a 134 day cropping period (April–October) of 2021 and 2022 at Othello, WA. Note the differential exposure of developmental stages (I, II, III, and IV representing emergence, tuberization, bulking, and tuber maturity, respectively) to air and soil temperatures due to staggered planting (see <a href="#plants-14-00750-t001" class="html-table">Table 1</a> for early, mid-, and late planting dates). Temperatures were monitored daily in duplicate every 15 min for the entire cropping period. Daily averages are presented. The growing season of 2021 experienced higher soil and air temperatures than that of 2022, especially in the earlier months of the cropping season.</p>
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<p>Air and soil temperatures during a 134 day cropping period (April–October) of 2021 (<b>A</b>,<b>C</b>) and 2022 (<b>B</b>,<b>D</b>) at Othello, WA. Note the differential exposure of developmental stages (I, II, III, and IV representing emergence, tuberization, bulking, and tuber maturity, respectively) to air and soil temperatures due to staggered planting. Temperatures were monitored daily in duplicate every 15 min for the entire cropping period. Air temperatures above 30 °C and soil temperatures above 20 °C (threshold for tuberization/bulking) are presented. The growing season of 2021 experienced temperatures as high as 45.5 °C (heat dome) that coincided with tuberization (stage II) of early and mid-plantings.</p>
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<p>Diurnal changes in air and soil temperatures during the cropping seasons of 2021 and 2022 at Othello, WA. Note the yearly variation in the air and soil temperatures (day/night) during tuberization and bulking of early (E), mid- (M),- and late (L) plantings. Temperatures were monitored daily every 15 min during the entire crop growth period. Average values during tuberization and bulking are presented. Early and mid-plantings of 2021 experienced higher soil and air temperatures during tuberization (day and night) than that of 2022 plantings. However, during bulking, the mid and late plantings of 2021 experienced lower temperatures (air and soil) irrespective of day or night (see <a href="#plants-14-00750-t002" class="html-table">Table 2</a> for the duration and hours of day and night during potato production).</p>
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<p>Total number of growing degree days (GDD) during the growth stages of early, mid-, and late plantings (● 2021, ○ 2022). Air and soil temperatures monitored daily every 15 min (in duplicate), and the daily averages were converted to GDDs (see materials and methods). The sum of GDDs derived from air and soil temperatures are presented. GDDs computed individually on air and soil temperatures showed similar trends. At 120 days after planting, the vines were killed, and tubers harvested after 14 days of skin-set. The GDDs increased linearly with the advancing growing period. During 2021, the number GDDs were higher, and rate of increase surpassed that of 2022, irrespective of planting time.</p>
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<p>The effect of storage period and planting time the average number of stems per plant (● Russet Norkotah, ○ Shepody). The chronological ages of tubers at planting were 210 (early planting), 231 (mid-planting) and 253 (late planting) days at a constant temperature (4 °C). The stems were counted 60 days after planting. Stem numbers were positively correlated with advancing storage period irrespective of the cultivar or growing year (2021/2022).</p>
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<p>Polygonal diagrams showing the effects of planting time (see <a href="#plants-14-00750-t001" class="html-table">Table 1</a>) on the contribution of size categories to the total tuber yield (percent). Each axis represents a specified tuber weight class. The shape of the polygon depicts the effect of planting time on tuber size distribution. Note the differential response of Russet Norkotah (<b>A</b>) and Shepody (<b>B</b>) and the effect of growing season (2021 vs. 2022) on tuber size profile. In contrast to Shepody, tuber size distributions were less affected by seasonal variation in temperature in Russet Norkotah. The higher temperatures during 2021 increased the proportion of larger tubers (&gt;397 g) specifically in Shepody. Statistical significance of planting time and cultivar on total yield and the contribution of tuber size categories are presented in <a href="#plants-14-00750-t004" class="html-table">Table 4</a>, <a href="#plants-14-00750-t005" class="html-table">Table 5</a> and <a href="#plants-14-00750-t006" class="html-table">Table 6</a>.</p>
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<p>The effect of storage period and temperature on the average number of stems per plant. The tubers of Russet Norkotah and Shepody harvested from early, mid-, and late plantings (<a href="#plants-14-00750-t001" class="html-table">Table 1</a>) in the year 2021 and 2022 were subjected to accelerated aging at 12 °C (10 days) or at 32 °C (21 days). Following this aging treatment, the tubers were held at 4 °C until planting in the field. The tubers accumulated 80 degree days at 12 °C (control) and 600 degree days at 32 °C. Tubers were planted in the subsequent years of 2021 and 2022 (2022 and 2023). Stem numbers increased linearly with storage period in controls irrespective of the cultivar or growing year. Both Russet Norkotah and Shepody tubers harvested in 2021 and 2022 responded to accelerated aging (600 DD) with increased stems per plant when planted the following year (2022 and 2023, respectively).</p>
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19 pages, 4170 KiB  
Review
Current Trends and Future Prospects in Onion Production, Supply, and Demand in South Korea: A Comprehensive Review
by Muhammad Imran, Hajeong Kang, Sang-Gu Lee, Eun-Ha Kim, Hyun-Min Park and Seon-Woo Oh
Sustainability 2025, 17(3), 837; https://doi.org/10.3390/su17030837 - 21 Jan 2025
Viewed by 1004
Abstract
Onion cultivation in South Korea faces a range of interconnected challenges, shaped by fluctuating supply and demand dynamics, the dominance of imported seed varieties, and the growing issue of fungal pathogens affecting stored onions. In recent years, significant shifts occurred within the onion [...] Read more.
Onion cultivation in South Korea faces a range of interconnected challenges, shaped by fluctuating supply and demand dynamics, the dominance of imported seed varieties, and the growing issue of fungal pathogens affecting stored onions. In recent years, significant shifts occurred within the onion industry, such as export volumes in 2023 declining to 106 tons compared to 99,506 tons in 2022, while import volumes surged to 113,902 tons to meet domestic demand through the Tariff Rate Quota (TRQ) system. Concurrently, domestic production onion supply in 2023 estimates a total of 1.347 million tons, a 5.2% increase compared to the previous year, due to a 6.3% rise in domestic production. Despite this growth, South Korea’s onion seed market remains heavily dependent on imports, particularly from Japan, underscoring the need for the development of competitive domestic cultivars. Furthermore, environmental conditions such as microclimates in regions like Muan have proven to be critical, as they produce onions with superior nutritional profiles and storability. However, fungal diseases pose persistent threats to storage, resulting in substantial economic losses. However, the country’s reliance on imported varieties and the climate’s effects on cultivation call for more investment in domestic breeding programs and adaptive farming practices. To address these challenges, this review synthesizes historical data, current trends, and the future prospects of onion production, supply, and demand in South Korea. Comprehensive strategies are proposed, including the promotion of adaptive farming practices, investment in domestic breeding programs, and enhanced storage techniques to mitigate fungal pathogens. This work emphasizes the importance of integrated efforts among policymakers, researchers, and industry stakeholders to improve productivity, reduce reliance on imports, and secure a sustainable future for the South Korean onion industry. The findings offer actionable insights for enhancing market competitiveness and achieving agricultural sustainability. Full article
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<p>Current trend in onion production in South Korea. Data are citied from [<a href="#B13-sustainability-17-00837" class="html-bibr">13</a>,<a href="#B30-sustainability-17-00837" class="html-bibr">30</a>].</p>
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<p>Onion Cultivation Area, unit are presented as ha. Data are citied from [<a href="#B13-sustainability-17-00837" class="html-bibr">13</a>,<a href="#B30-sustainability-17-00837" class="html-bibr">30</a>].</p>
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<p>Onion Cultivation Area by province, unit are presented as ha. Data are citrated from [<a href="#B13-sustainability-17-00837" class="html-bibr">13</a>,<a href="#B30-sustainability-17-00837" class="html-bibr">30</a>].</p>
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<p>Import and Export trend of onion in South Korea, Data are citied from [<a href="#B13-sustainability-17-00837" class="html-bibr">13</a>,<a href="#B30-sustainability-17-00837" class="html-bibr">30</a>].</p>
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<p>Representation of control strategies to improve onion long-term storage.</p>
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<p>Environmental effects on onion cultivation and nutrient composition in South Korea. The blue arrow indicate the factor influence onion quality, the orange arrow indicate changes related to specific condition, red arrow indicate decrease or negative impact on onion quality, green arrow indicate improve in onion quality.</p>
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12 pages, 2410 KiB  
Article
Seedling Growth and Systemic Uptake of Liquid Vermicompost-Coated Seeds in Organic Pumpkin (Cucurbita sp.)
by Wissanee Pola and Sukanya Aiamla-or
Horticulturae 2025, 11(1), 58; https://doi.org/10.3390/horticulturae11010058 - 8 Jan 2025
Viewed by 474
Abstract
Liquid vermicompost (LVC) is one of the organic ingredients for improving plant growth. This study aims to investigate the impact of the application of LVC coating formulations in distinct ratios on seeding emergence, seedling growth parameters, and nitrogen content as well as the [...] Read more.
Liquid vermicompost (LVC) is one of the organic ingredients for improving plant growth. This study aims to investigate the impact of the application of LVC coating formulations in distinct ratios on seeding emergence, seedling growth parameters, and nitrogen content as well as the systemic uptake characteristics in seedlings. Coating formulations contained gum arabic (GA) mixed with 5–15% of LVC and were applied to pumpkin seeds and compared to non-coated seeds. All samples were stored under cold and ambient conditions for 3 months to evaluate the performance of the coating. Results showed no statistical distinctions in the percentage of seedling emergence. Nevertheless, the 5LVC-GA in the organic formulation significantly increased shoot length, seedling growth rate (SGR), seedling vigor index (SVI), and nitrogen content (%) in the coated seedlings. Additionally, the evaluation of seedling uptake was achieved using rhodamine B as a fluorescent tracer which was diluted in the organic formulation. This explored the transportation of the treatment within a seedling. Therefore, the application of an optimum concentration of 5LVC-GA treatment can improve seedling growth and nitrogen accumulation. This could be confirmed with fluorescence imaging of translocation to seedling organs. However, seed storability declines over three months, emphasizing the need for better coatings and packaging solutions. Full article
(This article belongs to the Section Propagation and Seeds)
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<p>Seedling emergence (<b>A</b>), seedling dry weight (<b>B</b>), seedling vigor index (<b>C</b>), seedling growth rate (<b>D</b>), root length (<b>E</b>), and shoot length (<b>F</b>) of the organic coated pumpkin seeds as stored in cold and ambient conditions for 3 months. The different letters above the graph (a–d) are significantly different within a duration of storage according to Duncan’s multiple-range test (<span class="html-italic">p</span> &lt; 0.05), ns = not significant.</p>
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<p>Reduction (%) of the coated pumpkin seed qualities after storage under cold and ambient conditions for 3 months compared to each initial value; (<b>A</b>) seedling emergence, (<b>B</b>) dry weight (DW), (<b>C</b>) seedling vigor index (SVI), (<b>D</b>) seedling growth rate (SGR), (<b>E</b>) root length, and (<b>F</b>) shoot length.</p>
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<p>Nitrogen content (%) in the 10 days-old seedlings of the coated pumpkin seeds after coating processes. The different letters above the graph (a–d) are significantly different within a duration of storage according to Duncan’s multiple-range test (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Appearance of the pumpkin seedlings after coating and germinating under a greenhouse condition for 10 days (<b>A</b>), the treatment uptake in seedling organs of the non-coated and the coated seeds as shown in the transport tissue (TT) of the plant organs including seed, root, shoot, and leaf, using Fluoro-Mini (<b>B</b>), and a fluorescence microscopy (<b>C</b>).</p>
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15 pages, 3039 KiB  
Article
Comparative Metabolic Analysis of Different Indica Rice Varieties Associated with Seed Storability
by Fangxi Wu, Yidong Wei, Yongsheng Zhu, Xi Luo, Wei He, Yingheng Wang, Qiuhua Cai, Huaan Xie, Guosheng Xie and Jianfu Zhang
Metabolites 2025, 15(1), 19; https://doi.org/10.3390/metabo15010019 - 5 Jan 2025
Viewed by 573
Abstract
Seed storability is a crucial agronomic trait and indispensable for the safe storage of rice seeds and grains. Nevertheless, the metabolite mechanisms governing Indica rice seed storability under natural conditions are still poorly understood. Methods: Therefore, the seed storage tolerance of global rice [...] Read more.
Seed storability is a crucial agronomic trait and indispensable for the safe storage of rice seeds and grains. Nevertheless, the metabolite mechanisms governing Indica rice seed storability under natural conditions are still poorly understood. Methods: Therefore, the seed storage tolerance of global rice core germplasms stored for two years under natural aging conditions were identified, and two extreme groups with different seed storabilities from the Indica rice group were analyzed using the UPLC-MS/MS metabolomic strategy. Results: Our results proved that the different rice core accessions showed significant variability in storage tolerance, and the metabolite analysis of the two Indica rice pools exhibited different levels of storability. A total of 103 differentially accumulated metabolites (DAMs) between the two pools were obtained, of which 38 were up-regulated and 65 were down-regulated, respectively. Further analysis disclosed that the aging-resistant rice accessions had higher accumulation levels of flavonoids, terpenoids, phenolic acids, organic acids, lignans, and coumarins while exhibiting lower levels of lipids and alkaloids compared to the storage-sensitive rice accessions. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis indicated that several biosynthesis pathways were involved in the observed metabolite differences, including alpha-linolenic acid metabolism, butanoate metabolism, and propanoate metabolism. Notably, inhibition of the linolenic acid metabolic pathway could enhance seed storability. Additionally, increased accumulations of organic acids, such as succinic acid, D-malic acid, and methylmalonic acid, in the butanoate and propanoate metabolisms were identified as a beneficial factor for seed storage. Conclusions: These new findings will deepen our understanding of the underlying mechanisms governing rice storability. Full article
(This article belongs to the Special Issue Metabolic Responses of Seeds Development and Germination)
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<p>Disparity in the seed storability of the whole population of 375 rice core accessions and four groups from 47 different countries. (<b>A</b>) Distribution of and variations in seed germination percentages in 375 accessions after natural aging treatment for 24 months. (<b>B</b>) Scatter dot plot illustrating the seed germination percentages in the four rice groups (<span class="html-italic">Basmati</span>, <span class="html-italic">Indica</span>, <span class="html-italic">Aus</span>, and <span class="html-italic">Japonica</span>) with different colored means.</p>
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<p>Analysis of metabolite profiles in AR and AS pools. (<b>A</b>) Classification of 1098 identified metabolites. (<b>B</b>) Scatter plot from the PCA model representing different rice storage pools. The abscissa PC1 and ordinate PC2 represent scores of the first and second principal components, respectively. (<b>C</b>) Overall clustering heatmaps of all differentially accumulated metabolites from the two pools. Each scatter represents a sample, with the color and shape indicating different groups.</p>
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<p>Identification of differentially accumulated metabolites (DAMs) in AR and AS pools. (<b>A</b>) OPLS-DA permutation plot for two different <span class="html-italic">Indica</span> rice storage pools. (<b>B</b>) Score plot generated from OPLS-DA for two different <span class="html-italic">Indica</span> rice storage pools. (<b>C</b>) Volcano plots depicting the expression levels of DAMs for two different <span class="html-italic">Indica</span> rice storage pools. (<b>D</b>) Various types of DAMs were identified in different <span class="html-italic">Indica</span> rice storage pools. (<b>E</b>) Overall clustering heatmap displaying DAMs for two different <span class="html-italic">Indica</span> rice storage pools. Each scatter represents a sample, with color and shape indicating different <span class="html-italic">Indica</span> rice groups, respectively.</p>
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<p>Identification of differentially accumulated metabolites (DAMs) in AR and AS pools. (<b>A</b>) OPLS-DA permutation plot for two different <span class="html-italic">Indica</span> rice storage pools. (<b>B</b>) Score plot generated from OPLS-DA for two different <span class="html-italic">Indica</span> rice storage pools. (<b>C</b>) Volcano plots depicting the expression levels of DAMs for two different <span class="html-italic">Indica</span> rice storage pools. (<b>D</b>) Various types of DAMs were identified in different <span class="html-italic">Indica</span> rice storage pools. (<b>E</b>) Overall clustering heatmap displaying DAMs for two different <span class="html-italic">Indica</span> rice storage pools. Each scatter represents a sample, with color and shape indicating different <span class="html-italic">Indica</span> rice groups, respectively.</p>
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<p>Identification of differentially accumulated metabolites (DAMs) in AR and AS pools. (<b>A</b>) OPLS-DA permutation plot for two different <span class="html-italic">Indica</span> rice storage pools. (<b>B</b>) Score plot generated from OPLS-DA for two different <span class="html-italic">Indica</span> rice storage pools. (<b>C</b>) Volcano plots depicting the expression levels of DAMs for two different <span class="html-italic">Indica</span> rice storage pools. (<b>D</b>) Various types of DAMs were identified in different <span class="html-italic">Indica</span> rice storage pools. (<b>E</b>) Overall clustering heatmap displaying DAMs for two different <span class="html-italic">Indica</span> rice storage pools. Each scatter represents a sample, with color and shape indicating different <span class="html-italic">Indica</span> rice groups, respectively.</p>
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<p>The KEGG pathway enrichment analysis of DAMs in AR and AS pools. (<b>A</b>) Bubble chart of the KEGG pathway. (<b>B</b>) Metabolite pathway of alpha-linolenic acid metabolism. (<b>C</b>) Metabolite pathway of butanoate and propanoate metabolism. The up-regulated DAMs are highlighted in red, while the down-regulated ones are indicated in green.</p>
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<p>The KEGG pathway enrichment analysis of DAMs in AR and AS pools. (<b>A</b>) Bubble chart of the KEGG pathway. (<b>B</b>) Metabolite pathway of alpha-linolenic acid metabolism. (<b>C</b>) Metabolite pathway of butanoate and propanoate metabolism. The up-regulated DAMs are highlighted in red, while the down-regulated ones are indicated in green.</p>
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18 pages, 2414 KiB  
Article
Quercetin and Rutin as Tools to Enhance Antioxidant Profiles and Post-Priming Seed Storability in Medicago truncatula
by Shraddha Shridhar Gaonkar, Federico Sincinelli, Alma Balestrazzi and Andrea Pagano
Agriculture 2024, 14(5), 738; https://doi.org/10.3390/agriculture14050738 - 9 May 2024
Cited by 1 | Viewed by 1504
Abstract
Seed priming is routinely applied to improve germination rates and seedling establishment, but the decrease in longevity observed in primed seeds constitutes a major drawback that compromises long-term storability. The optimization of priming protocols able to preserve primed seeds from aging processes represents [...] Read more.
Seed priming is routinely applied to improve germination rates and seedling establishment, but the decrease in longevity observed in primed seeds constitutes a major drawback that compromises long-term storability. The optimization of priming protocols able to preserve primed seeds from aging processes represents a promising route to expand the scope of seed priming. The present work explores this possibility in the model legume Medicago truncatula by testing the effectiveness of quercetin- and rutin-supplemented seed priming at improving the response to subsequent artificial aging. In comparison with a non-supplemented hydropriming protocol, supplementation with quercetin or rutin was able to mitigate the effects of post-priming aging by increasing germination percentage and speed, improving seed viability and seedling phenotype, with consistent correlations with a decrease in the levels of reactive oxygen species and an increase in antioxidant potential. The results suggest that quercetin and rutin can reduce the effects of post-priming aging by improving the seed antioxidant profiles. The present work provides novel information to explore the physiological changes associated with seed priming and aging, with possible outcomes for the development of tailored vigorization protocols able to overcome the storability constrains associated with post-priming aging processes. Full article
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<p>Overview of the experimental system to compare the effects of accelerated aging on <span class="html-italic">Medicago truncatula</span> seeds after hydropriming, quercetin-priming or rutin-priming. UP, unprimed control conditions; HP, hydropriming; QP, quercetin priming; RP, rutin priming; UA, unaged control conditions; AA, artificial aging.</p>
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<p>Germination performance of <span class="html-italic">Medicago truncatula</span> seeds subjected to hydropriming, quercetin-priming or rutin-priming followed by accelerated aging. (<b>a</b>) Germinability percentage. (<b>b</b>) T<sub>50</sub>. UP, unprimed control conditions; HP, hydropriming; QP, quercetin-priming; RP, rutin-priming; UA, unaged control conditions; AA, artificial aging. T<sub>50</sub>; time (h) to reach 50% of final germinants. Means without a common letter are significantly (<span class="html-italic">p</span>-value &lt; 0.05) different as analyzed by two-way ANOVA and Duncan test.</p>
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<p>Viability of <span class="html-italic">Medicago truncatula</span> seed following hydropriming, quercetin-supplemented priming or rutin-supplemented priming combined with accelerated aging. (<b>a</b>) Seed viability percentage assessed with TTC staining. (<b>b</b>) Representative pictures of viable (row numbers 1 to 5) and non-viable (row numbers 6 to 10) seeds as assessed by TCC assay for each treatment category. UP, unprimed control conditions; HP, hydropriming; QP, quercetin-priming; RP, rutin-priming; UA, unaged control conditions; AA, artificial aging. Means without a common letter are significantly (<span class="html-italic">p</span>-value &lt; 0.05) different as analyzed by two-way ANOVA and Duncan test. The letters referring to different comparison series are indicated with different colors.</p>
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<p>Phenotype of <span class="html-italic">Medicago truncatula</span> seedlings following hydropriming, quercetin-supplemented priming or rutin-supplemented priming combined with accelerated aging. (<b>a</b>) Seedling phenotype percentage. (<b>b</b>) Representative pictures of normal (top rows) and aberrant (bottom rows) seedling morphology for each treatment category. UP, unprimed control conditions; HP, hydropriming; QP, quercetin-priming; RP, rutin-priming; UA, unaged control conditions; AA, artificial aging. Means without a common letter are significantly (<span class="html-italic">p</span>-value &lt; 0.05) different as analyzed by two-way ANOVA and Duncan test. The letters referring to different comparison series are indicated with different colors.</p>
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<p>Antioxidant potential, phenolic compounds, and specific antioxidant activity of <span class="html-italic">Medicago truncatula</span> seeds subjected to hydropriming, quercetin-priming or rutin-priming followed by accelerated aging. (<b>a</b>) Antioxidant potential assessed by DPPH assay. (<b>b</b>) Content in phenolic compounds assessed by Folin–Ciocalteu assay. (<b>c</b>) Specific antioxidant activity calculated from DPPH and Folin–Ciocalteu data. (<b>d</b>) ROS detection by DCF-DA assay. UP, unprimed control conditions; HP, hydropriming; QP, quercetin priming; RP, rutin priming; UA, unaged control conditions; AA, artificial aging. Means without a common letter are significantly (<span class="html-italic">p</span>-value &lt; 0.05) different as analyzed by two-way ANOVA and Duncan test. AAE, ascorbic acid equivalents; GAE, gallic acid equivalents; SAA, specific antioxidant activity; FW, fresh weight. RFU, relative fluorescence units.</p>
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<p>Overview of the results of Pearson’s correlation analysis and principal component analysis. (<b>a</b>) Pearson’s correlation analysis of the results obtained from <span class="html-italic">Medicago truncatula</span> seeds subjected to hydropriming, quercetin-priming or rutin-priming followed by accelerated aging. The correlation coefficients are indicated. The statistical significance of the Pearson’s correlations is indicated by asterisks (* <span class="html-italic">p</span>-value &lt; 0.05, ** <span class="html-italic">p</span>-value &lt; 0.01, *** <span class="html-italic">p</span>-value &lt; 0.001). NA, not applicable. ROS, reactive oxygen species as assessed by DCF-DA assay. Antiox., antioxidant potential as assessed by DPPH assay. Phenol., content in total phenolic compounds as assessed by Folin–Ciocalteu assay. SSA, specific antioxidant activity. TTC, seed viability percentage as assessed by TTC assay. G, germinability. PV, peak value. T<sub>50</sub>, time required to reach 50% of final germination. MGT, mean germination time. Norm., percentage of normal seedlings. Aber., percentage of aberrant seedlings. NG, percentage of non-germinant seeds. (<b>b</b>) Two-dimensional score plot of the principal component analysis of the results obtained from <span class="html-italic">M. truncatula</span> seeds subjected to hydropriming, quercetin-priming or rutin-priming followed by accelerated aging. UP, unprimed control conditions; HP, hydropriming; QP, quercetin priming; RP, rutin priming; UA, unaged control conditions; AA, artificial aging; PC, principal component.</p>
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17 pages, 10041 KiB  
Article
Transcriptomic Profiling of Two Rice Thermo-Sensitive Genic Male Sterile Lines with Contrasting Seed Storability after Artificial Accelerated Aging Treatment
by Fan Li, Hongbing Ye, Yingfeng Wang, Jieqiang Zhou, Guilian Zhang, Xiong Liu, Xuedan Lu, Feng Wang, Qiuhong Chen, Guihua Chen, Yunhua Xiao, Wenbang Tang and Huabing Deng
Plants 2024, 13(7), 945; https://doi.org/10.3390/plants13070945 - 25 Mar 2024
Viewed by 1506
Abstract
Seed storability has a significant impact on seed vitality and is a crucial genetic factor in maintaining seed value during storage. In this study, RNA sequencing was used to analyze the seed transcriptomes of two rice thermo-sensitive genic male sterile (TGMS) lines, S1146S [...] Read more.
Seed storability has a significant impact on seed vitality and is a crucial genetic factor in maintaining seed value during storage. In this study, RNA sequencing was used to analyze the seed transcriptomes of two rice thermo-sensitive genic male sterile (TGMS) lines, S1146S (storage-tolerant) and SD26S (storage-susceptible), with 0 and 7 days of artificial accelerated aging treatment. In total, 2658 and 1523 differentially expressed genes (DEGs) were identified in S1146S and SD26S, respectively. Among these DEGs, 729 (G1) exhibited similar regulation patterns in both lines, while 1924 DEGs (G2) were specific to S1146S, 789 DEGs (G3) were specific to SD26S, and 5 DEGs (G4) were specific to contrary differential expression levels. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that “translation”, “ribosome”, “oxidative phosphorylation”, “ATP-dependent activity”, “intracellular protein transport”, and “regulation of DNA-templated transcription” were significantly enriched during seed aging. Several genes, like Os01g0971400, Os01g0937200, Os03g0276500, Os05g0328632, and Os07g0214300, associated with seed storability were identified in G4. Core genes Os03g0100100 (OsPMEI12), Os03g0320900 (V2), Os02g0494000, Os02g0152800, and Os03g0710500 (OsBiP2) were identified in protein–protein interaction (PPI) networks. Seed vitality genes, MKKK62 (Os01g0699600), OsFbx352 (Os10g0127900), FSE6 (Os05g0540000), and RAmy3E (Os08g0473600), related to seed storability were identified. Overall, these results provide novel perspectives for studying the molecular response and related genes of different-storability rice TGMS lines under artificial aging conditions. They also provide new ideas for studying the storability of hybrid rice. Full article
(This article belongs to the Special Issue Seed Protective Mechanisms)
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<p>Seed storability of 9 thermo-sensitive genic male sterile (TGMS) lines. (<b>A</b>) The final germination percentage of 9 TGMS lines on the 14th day in standard germination experiments. (<b>B</b>) The decrease in gemination percentage (GP), (<b>C</b>) germination index (GI), and (<b>D</b>) vitality index (VI) of 9 TGMS lines at 14 d artificial accelerated aging treatment. Different letters indicate statistical differences between samples using ANOVA test, <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Statistical analysis of differentially expressed genes (DEGs) in S1146S and SD26S. The relationship between fold change and false discovery rate (FDR) of DEGs in (<b>A</b>) S1146S and (<b>B</b>) SD26S is shown by volcano plots. The blue dots indicate down-regulated DEGs, the orange dots indicate up-regulated DEGs, and the grey dots indicate not sig. (<b>C</b>) Venn diagram comparison of total DEGs in S1146S and SD26S. (<b>D</b>) Venn diagram demonstrating the overlap of DEGs in S1146S and SD26S. Specifically, G1 represents the DEGs with similar expression in both S1146S and SD26S, G2 represents the DEGs specific to S1146S, G3 represents the DEGs specific to SD26S, and G4 represents DEGs that were oppositely expressed in S1146S and SD26S.</p>
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<p>Functional annotation of DEGs in G1, G2, G3, and G4. (<b>A</b>–<b>C</b>) Gene Ontology (GO) annotation of DEGs in G1, G2, and G3. The horizontal axis represents the gene ratio of DEGs annotated to each specific function, and the vertical axis represents the functional annotation information. (<b>D</b>) Comparison of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment in G1, G2, G3, and G4. The dot color represents the <span class="html-italic">p</span>-value; the smaller the <span class="html-italic">p</span>-value, the closer the color is to red. The dot size reflects the relative number of DEGs related to each pathway.</p>
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<p>Key pathways related to seed aging in S1146S and SD26S. Heatmap depicting the expression levels of the DEGs enriched in the (<b>A</b>) “ATP-dependent activity” term, (<b>B</b>) “Intracellular protein transport” term, and (<b>C</b>) “regulation of DNA-templated transcription” term. The log<sub>2</sub> fold change (log<sub>2</sub> FC) values of all samples were used to construct the heatmap. The gene expression levels are represented by a blue to red color spectrum, indicating low to high expression, respectively.</p>
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<p>Protein–protein interaction (PPI) analysis of G1, G2, and G3 DEGs. PPI networks for G1 (<b>A</b>), G2 (<b>C</b>), and G3 (<b>E</b>) DEGs were constructed from the STRING database. Genes are shown as dots, and their relationships are shown as lines. The top 20 DEGs in the interaction network were calculated based on their degree, with darker colors indicating greater degrees. The log<sub>2</sub> FC of the top 20 DEGs in the G1 (<b>B</b>), G2 (<b>D</b>), and G3 (<b>F</b>) PPI networks are represented in the heatmaps. The number indicates the log<sub>2</sub> FC value of the DEGs at the corresponding time point.</p>
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<p>Comparative analysis of the expression levels of genes associated with “seed vitality” between S1146S and SD26S after artificial seed aging treatment for 0 and 7 days. Comparative analysis of (<b>A</b>) <span class="html-italic">MKKK62</span> (<span class="html-italic">Os01g0699600</span>), (<b>B</b>) <span class="html-italic">OsHSP71.1</span> (<span class="html-italic">Os03g0276500</span>), (<b>C</b>) <span class="html-italic">RSR1</span> (<span class="html-italic">Os05g0121600</span>), (<b>D</b>) <span class="html-italic">FSE6</span> (<span class="html-italic">Os05g0540000</span>), (<b>E</b>) <span class="html-italic">OsZIP58</span> (<span class="html-italic">Os07g0182000</span>), (<b>F</b>) <span class="html-italic">RAmy3E</span> (<span class="html-italic">Os08g0473600</span>), (<b>G</b>) <span class="html-italic">OsDSG1</span> (<span class="html-italic">Os09g0434200</span>), (<b>H</b>) <span class="html-italic">OsFbx352</span> (<span class="html-italic">Os10g0127900</span>) expression levels (FPKM: fragments per kilobase of transcript per million mapped reads). The means ± SEM (standard error of the mean) of three independent replicates are presented as data. Statistical significance is denoted as follows: ns (not significant) for <span class="html-italic">p</span> ≥ 0.05, * for <span class="html-italic">p</span> &lt; 0.05, and ** for <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Schematic diagram of the storability regulation in S1146S and SD26S seeds during aging treatment. The ↑ in red indicates up-regulation, while the ↓ in blue indicates down-regulation. The thickness of the line indicates the level of expression, with thicker lines representing higher levels of expression.</p>
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16 pages, 3109 KiB  
Article
OsCSD2 and OsCSD3 Enhance Seed Storability by Modulating Antioxidant Enzymes and Abscisic Acid in Rice
by Xiaohai Zheng, Zhiyang Yuan, Yuye Yu, Sibin Yu and Hanzi He
Plants 2024, 13(2), 310; https://doi.org/10.3390/plants13020310 - 20 Jan 2024
Cited by 5 | Viewed by 1770
Abstract
Seed deterioration during storage poses a significant challenge to rice production, leading to a drastic decline in both edible quality and viability, thereby impacting overall crop yield. This study aimed to address this issue by further investigating candidate genes associated with two previously [...] Read more.
Seed deterioration during storage poses a significant challenge to rice production, leading to a drastic decline in both edible quality and viability, thereby impacting overall crop yield. This study aimed to address this issue by further investigating candidate genes associated with two previously identified QTLs for seed storability through genome association analysis. Among the screened genes, two superoxide dismutase (SOD) genes, OsCSD2 (Copper/zinc Superoxide Dismutase 2) and OsCSD3, were selected for further study. The generation of overexpression and CRISPR/Cas9 mutant transgenic lines revealed that OsCSD2 and OsCSD3 play a positive regulatory role in enhancing rice seed storability. Subsequent exploration of the physiological mechanisms demonstrated that overexpression lines exhibited lower relative electrical conductivity, indicative of reduced cell membrane damage, while knockout lines displayed the opposite trend. Furthermore, the overexpression lines of OsCSD2 and OsCSD3 showed significant increases not only in SOD but also in CAT and POD activities, highlighting an augmented antioxidant system in the transgenic seeds. Additionally, hormone profiling indicated that ABA contributed to the improved seed storability observed in these lines. In summary, these findings provide valuable insights into the regulatory mechanisms of OsCSDs in rice storability, with potential applications for mitigating grain loss and enhancing global food security. Full article
(This article belongs to the Special Issue Seed Protective Mechanisms)
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<p>Expression analyses of the candidate genes of <span class="html-italic">qSL3</span> and <span class="html-italic">qSL7.2</span>. (<b>A</b>) The expression level of genes within the 200 kb range of the peak SNP of <span class="html-italic">qSL3</span> R0306454554AG in seed embryos. The red bar indicated <span class="html-italic">OsCSD2</span>. (<b>B</b>) The expression levels of genes within the 200 kb range of the peak SNP of <span class="html-italic">qSL7.2</span> F0728018966GA in seed embryos. The red bar indicated <span class="html-italic">OsCSD3</span> (<b>C</b>) Germination percentage after artificial aging in ZH11 seeds. (<b>D</b>,<b>E</b>) Relative expression changes of <span class="html-italic">OsCSD2</span> and <span class="html-italic">OsCSD3</span> during artificial aging and imbibed for 0, 24 and 48 h, respectively. Note: Different letters denote significant difference at <span class="html-italic">p</span> &lt; 0.05 by LSD.</p>
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<p>Construction strategy of <span class="html-italic">OsCSD2</span> and <span class="html-italic">OsCSD3</span> overexpression and knockout lines. (<b>A</b>) Schematic diagram of the overexpression vector (pCAMBIA130S) and CRISPR/Cas9 mutant vector (pCXUN_CAS9). (<b>B</b>) The target site editing situation of the CRISPR/Cas9 mutants. (<b>C</b>–<b>F</b>) The relative expression level of the transgenic lines. OX− represents negative control of the overexpression line (the negative plant that did not contain the transgenic element), OX+ represents the positive overexpression line, WT, wild-type plants, MT#1 and MT#2 represents the mutant line generated by CRISPR/Cas9. Note: Different letters denote significant difference at <span class="html-italic">p</span> &lt; 0.05 by LSD.</p>
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<p>Storability of <span class="html-italic">OsCSD2</span> and <span class="html-italic">OsCSD3</span> transgenic lines. (<b>A</b>) Germination percentage of <span class="html-italic">OsCSD2</span> overexpression line. (<b>B</b>) Germination percentage of <span class="html-italic">OsCSD2</span> mutants. (<b>C</b>) Germination percentage of <span class="html-italic">OsCSD3</span> overexpression line. (<b>D</b>) Germination percentage of <span class="html-italic">OsCSD3</span> mutants. Note: The x-axis 0 d and 10 d indicate seeds without aging (0 d) and after aging (10 d), respectively. OX− represents negative control of the overexpression line (the negative plant that did not contain the transgenic element), OX+ represents the positive overexpression line, WT, wild-type plants, MT#1 and MT#2 represents the mutant line generated by CRISPR/Cas9. Note: Different letters denote significant difference at <span class="html-italic">p</span> &lt; 0.05 by LSD.</p>
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<p>Phylogenetic tree and analysis of <span class="html-italic">cis</span>-acting elements and their distribution in <span class="html-italic">OsCSD2</span> and <span class="html-italic">OsCSD3</span>. (<b>A</b>) Phylogenetic tree of <span class="html-italic">CSD2</span> and <span class="html-italic">CSD3</span> in <span class="html-italic">Oryza sativa</span>, <span class="html-italic">Arabidopsis thaliana</span>, <span class="html-italic">Zea mays</span> and <span class="html-italic">Setaria italica</span>. (<b>B</b>,<b>C</b>) Number and distribution of <span class="html-italic">cis</span>-acting elements in the promoters of <span class="html-italic">OsCSD2</span> and <span class="html-italic">OsCSD3</span>, respectively. Note: The color in B and C represent the same motif.</p>
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<p><span class="html-italic">OsCSD2</span> and <span class="html-italic">OsCSD3</span> expression profiles in different tissues and subcellular localization. (<b>A</b>,<b>B</b>) <span class="html-italic">OsCSD2</span> and <span class="html-italic">OsCSD3</span> expression profiles in different tissues. (<b>C</b>,<b>D</b>) Subcellular localization of OsCSD2 and OsCSD3. NLS, FM64-4 and PXRB are markers for nuclear, membrane and peroxisome, respectively. The red bar indicates 10 µm.</p>
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<p>Relative electrical conductivity of <span class="html-italic">OsCSD2</span> and <span class="html-italic">OsCSD3</span> transgenic lines after artificial aging. (<b>A</b>,<b>B</b>) The relative electrical conductivity of the <span class="html-italic">OsCSD2-</span> and <span class="html-italic">OsCSD3</span>- overexpression lines. (<b>C</b>,<b>D</b>) Relative electrical conductivity of the <span class="html-italic">OsCSD2-</span> and <span class="html-italic">OsCSD3</span>- knockout mutants. OX− represents negative control of the overexpression line (the negative plant that did not contain the transgenic element), OX+ represents the positive overexpression line, WT, wild-type plants, MT#1 and MT#2 represents the mutant line generated by CRISPR/Cas9. Note: Different letters denote significant difference at <span class="html-italic">p</span> &lt; 0.05 by LSD.</p>
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<p>Eenzyme activities of SOD, CAT and POD in <span class="html-italic">OsCSD2</span> and <span class="html-italic">OsCSD3</span> transgenic lines after artificial aging. (<b>A</b>–<b>C</b>) SOD, CAT and POD activity of the <span class="html-italic">OsCSD2</span> overexpression line. (<b>D</b>–<b>F</b>) SOD, CAT and POD activity of the <span class="html-italic">OsCSD3</span> overexpression line. (<b>G</b>–<b>I</b>) SOD, CAT and POD activity of <span class="html-italic">OsCSD2</span> mutant lines, respectively. (<b>J</b>–<b>L</b>) SOD, CAT and POD activity of <span class="html-italic">OsCSD3</span> mutant lines. OX− represents negative control of the overexpression line (the negative plant that did not contain the transgenic element), OX+ represents the positive overexpression line, WT, wild-type plants, MT#1 and MT#2 represents the mutant line generated by CRISPR/Cas9. Note: Different letters denote significant difference at <span class="html-italic">p</span> &lt; 0.05 by LSD.</p>
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<p>ABA and IAA contents of <span class="html-italic">OsCSD2</span> and <span class="html-italic">OsCSD3</span> transgenic seeds after artificial aging. (<b>A</b>,<b>B</b>) ABA content of the <span class="html-italic">OsCSD2-</span> and <span class="html-italic">OsCSD3</span>-overexpression lines. (<b>C</b>,<b>D</b>) ABA content of the <span class="html-italic">OsCSD2-</span> and <span class="html-italic">OsCSD3</span>-knockout mutants. (<b>E</b>,<b>F</b>) IAA content of the <span class="html-italic">OsCSD2-</span> and <span class="html-italic">OsCSD3</span>-overexpression lines. (<b>G</b>,<b>H</b>) IAA content of the <span class="html-italic">OsCSD2-</span> and <span class="html-italic">OsCSD3</span>-knockout mutants. OX− represents negative control of the overexpression line (the negative plant that did not contain the transgenic element), OX+ represents the positive overexpression line, WT, wild-type plants, MT#1 and MT#2 represent the mutant line generated by CRISPR/Cas9. Note: Different letters denote significant difference at <span class="html-italic">p</span> &lt; 0.05 by LSD.</p>
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12 pages, 2025 KiB  
Article
Effects of OsAOX1a Deficiency on Mitochondrial Metabolism at Critical Node of Seed Viability in Rice
by Jing Ji, Shuangshuang Lin, Xia Xin, Yang Li, Juanjuan He, Xinyue Xu, Yunxia Zhao, Gefei Su, Xinxiong Lu and Guangkun Yin
Plants 2023, 12(12), 2284; https://doi.org/10.3390/plants12122284 - 12 Jun 2023
Cited by 3 | Viewed by 1451
Abstract
Mitochondrial alternative oxidase 1a (AOX1a) plays an extremely important role in the critical node of seed viability during storage. However, the regulatory mechanism is still poorly understood. The aim of this study was to identify the regulatory mechanisms by comparing OsAOX1a-RNAi and [...] Read more.
Mitochondrial alternative oxidase 1a (AOX1a) plays an extremely important role in the critical node of seed viability during storage. However, the regulatory mechanism is still poorly understood. The aim of this study was to identify the regulatory mechanisms by comparing OsAOX1a-RNAi and wild-type (WT) rice seed during artificial aging treatment. Weight gain and time for the seed germination percentage decreased to 50% (P50) in OsAOX1a-RNAi rice seed, indicating possible impairment in seed development and storability. Compared to WT seeds at 100%, 90%, 80%, and 70% germination, the NADH- and succinate-dependent O2 consumption, the activity of mitochondrial malate dehydrogenase, and ATP contents all decreased in the OsAOX1a-RNAi seeds, indicating that mitochondrial status in the OsAOX1a-RNAi seeds after imbibition was weaker than in the WT seeds. In addition, the reduction in the abundance of Complex I subunits showed that the capacity of the mitochondrial electron transfer chain was significantly inhibited in the OsAOX1a-RNAi seeds at the critical node of seed viability. The results indicate that ATP production was impaired in the OsAOX1a-RNAi seeds during aging. Therefore, we conclude that mitochondrial metabolism and alternative pathways were severely inhibited in the OsAOX1a-RNAi seeds at critical node of viability, which could accelerate the collapse of seed viability. The precise regulatory mechanism of the alternative pathway at the critical node of viability needs to be further analyzed. This finding might provide the basis for developing monitoring and warning indicators when seed viability declines to the critical node during storage. Full article
(This article belongs to the Special Issue Seed Aging Mechanism)
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<p>Comparison of wild-type (WT) and <span class="html-italic">OsAOX1a</span>-RNAi rice seeds (<b>a</b>) with respect to width (<b>b</b>) length (<b>c</b>) and 100-grain weight (<b>d</b>). Data represent the mean ± standard deviation of three independent experiments. Asterisks indicate a significant difference between <span class="html-italic">OsAOX1a</span>-RNAi transgenic plants and WT controls via Student’s <span class="html-italic">t</span>-test: * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Seed viability curves showing the time for the seed germination percentage decrease to 50% (<span class="html-italic">P50</span> (<b>a</b>)) and the vigor index (<b>b</b>) of wild-type and <span class="html-italic">OsAOX1a</span>-RNAi rice following artificial aging treatment. Data represent the mean ± standard deviation of three independent experiments. “……” represents the time when germination percentage decreases to 50%. Asterisks indicate a significant difference between <span class="html-italic">OsAOX1a</span> -RNAi transgenic plants and WT controls via Student’s <span class="html-italic">t</span>-test: * <span class="html-italic">p</span> &lt; 0.05; **, <span class="html-italic">p</span> &lt; 0.01; ***, <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Western blot showing verification abundance of AOX1 and Cyt <span class="html-italic">c</span> in mitochondria from wild type and <span class="html-italic">OsAOX1a</span>-RNAi seeds at germination percentage of 100%,90%, 80%, and 70% after 48 h imbibition.</p>
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<p>Malate dehydrogenase (MDH) activity (<b>a</b>) and ATP content (<b>b</b>) in crude mitochondria from wild type and <span class="html-italic">OsAOX1a</span>-RNAi seeds at germination percentage of 100%, 90%, 80%, and 70% after 48 h imbibition. Data represent the mean ± standard deviation of three independent experiments. Asterisks indicate a significant difference between <span class="html-italic">OsAOX1a</span>-RNAi transgenic plants and WT controls via Student’s <span class="html-italic">t</span>-test: * <span class="html-italic">p</span> &lt; 0.05; ***, <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Western blotting showing verification abundance of mitochondrial Complex I subunits in crude mitochondria from wild type and <span class="html-italic">OsAOX1a</span>-RNAi seeds at germination <span class="html-italic">percentage</span> of 100%, 90%, 80%, and 70% after 48 h imbibition.</p>
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22 pages, 1006 KiB  
Opinion
Regionally Adapted Model of an Ideal Malus×domestica Borkh Apple Variety for Industrial-Scale Cultivation in European Russia
by Ivan M. Kulikov, Julia V. Burmenko, Natalya YU. Svistunova, Sergey N. Evdokimenko, Fedor F. Sazonov, Tatyana A. Tumaeva and Sergey N. Konovalov
Agriculture 2022, 12(12), 2124; https://doi.org/10.3390/agriculture12122124 - 10 Dec 2022
Cited by 5 | Viewed by 2728
Abstract
Apple is one of the most common fruit crops in the Russian fruit-growing industry, with huge varietal diversity and a vast cultivation area. The key regions for industrial-scale apple cultivation are the Central, Central Chernozem, and North Caucasian Districts. The main disadvantage of [...] Read more.
Apple is one of the most common fruit crops in the Russian fruit-growing industry, with huge varietal diversity and a vast cultivation area. The key regions for industrial-scale apple cultivation are the Central, Central Chernozem, and North Caucasian Districts. The main disadvantage of the relevant apple cultivars, especially the ones intended for intensified horticultural practices, is their low resistance against abiotic stresses and the fruit’s low quality and poor marketable condition. In Russia, apple is a crop of strategic importance that is consistently included in the household food basket, so fruit producers hold new varieties to higher standards and expect them to outperform their predecessors in terms of yield per plant, resistance against abiotic and biotic stresses, and quality, as well as show strong competitiveness and a more rapid return on investment, while satisfying stricter requirements. The objective of the present study was to summarize the data on the phenotypic manifestations of economically valuable traits of the apple cultivars approved for use in the Russian Federation depending on the region of cultivation; to determine the parametric characteristics of the most valuable traits in the form of a model of an “ideal” regionally adapted industrial cultivar, and to identify the sources of the traits in the regions suitable for their production. A regionally adapted model of commercial apple cultivars, characterized by 28 features and properties divided into three groups and defining the cultivar’s resistance against abiotic and biotic stresses, yield per plant, product quality, and suitability for mechanized harvesting, is presented in this paper. In the European part of Russia, the optimal parameters of a commercial apple tree cultivar are as follows: plant height on a medium-sized rootstock under 3 m; potential yield per plant of at least 25–50 kg; high fruit uniformity above 80%; winter and late-winter harvest maturity period; high storability of over 210 days and good transportability; average fruit mass from 120 g to 220 g; juicy and shattering crisp pulp; small seed cavity; fragrant fruits with taste rating of at least 4.5 points; appearance rating of 5 points and attractive, mostly red, glossy color with natural wax bloom; regular, symmetric, but diverse shapes; content of sugar above 10%, ascorbic acid above 15 mg/100 g, organic acids up to 1% (for dessert varieties); content of soluble dry solids of at least 20%. The cultivars that come closest to the regionally adapted model of an ideal variety based on the set of features discussed are as follows: Feya, Soyuz, Orfej, Margo, Sirius, Noktyurn, Vasilisa Karmen, Florina, Dayton, Early Mac, Gala and Gala Schniga in the North Caucasian District; Svezhest’, Orlovskoe Poles’e, Aprel’skoe, Ven’yaminovskoe, Bolotovskoe, Vympel, Uspenskoe, Fregat, Bylina, Flagman, and Akademik Kazakov in the North Caucasian District; and varieties Imrus, Mayak Zagor’ya, and Bolotovskoe in the Central District. These cultivars are characterized by high resistance against weather anomalies, scab immunity, high yields, marketable quality, and storability. In addition, in southern regions, a prolonged bloom period acts as a protective adaptive response to low-temperature stress. Full article
(This article belongs to the Special Issue Breeding, Genetics, and Genomics of Fruit Crops)
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<p>Borders of the industrial-scale apple cultivation zone of the Russian Federation. Wa—varieties for winter consumption; Oa—varieties for autumn consumption; A—varieties for summer consumption.</p>
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<p>Optimal values of key economic and biological features of an ideal commercial apple variety recommended for breeding and cultivation activities in the Russian Federation.</p>
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21 pages, 5229 KiB  
Article
Identification of miRNAs Mediating Seed Storability of Maize during Germination Stage by High-Throughput Sequencing, Transcriptome and Degradome Sequencing
by Yongfeng Song, Zhichao Lv, Yue Wang, Chunxiang Li, Yue Jia, Yong Zhu, Mengna Cao, Yu Zhou, Xing Zeng, Zhenhua Wang, Lin Zhang and Hong Di
Int. J. Mol. Sci. 2022, 23(20), 12339; https://doi.org/10.3390/ijms232012339 - 15 Oct 2022
Cited by 3 | Viewed by 2206
Abstract
Seed storability is an important trait for improving grain quality and germplasm conservation, but little is known about the regulatory mechanisms and gene networks involved. MicroRNAs (miRNAs) are small non-coding RNAs regulating the translation and accumulation of their target mRNAs by means of [...] Read more.
Seed storability is an important trait for improving grain quality and germplasm conservation, but little is known about the regulatory mechanisms and gene networks involved. MicroRNAs (miRNAs) are small non-coding RNAs regulating the translation and accumulation of their target mRNAs by means of sequence complementarity and have recently emerged as critical regulators of seed germination. Here, we used the germinating embryos of two maize inbred lines with significant differences in seed storability to identify the miRNAs and target genes involved. We identified a total of 218 previously known and 448 novel miRNAs by miRNA sequencing and degradome analysis, of which 27 known and 11 newly predicted miRNAs are differentially expressed in two maize inbred lines, as measured by Gene Ontology (GO) enrichment analysis. We then combined transcriptome sequencing and real-time quantitative polymerase chain reaction (RT-PCR) to screen and confirm six pairs of differentially expressed miRNAs associated with seed storability, along with their negative regulatory target genes. The enrichment analysis suggested that the miRNAs/target gene mediation of seed storability occurs via the ethylene activation signaling pathway, hormone synthesis and signal transduction, as well as plant organ morphogenesis. Our results should help elucidate the mechanisms through which miRNAs are involved in seed storability in maize. Full article
(This article belongs to the Special Issue Molecular Mechanisms of Seed Dormancy and Germination)
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<p>Changes of germination percentage and water content of two inbred lines Dong 156 and Dong 237. (<b>A</b>) The change in Dong156 and Dong237 seed germination during accelerated aging. The seeds were aged under 45 °C and 95% RH. The error bars indicate ± SE (<span class="html-italic">n</span> = 3), (<b>B</b>) the moisture content change curves of Dong156 and Dong237 of the control group, (<b>C</b>) the moisture content change curves of in the fourth day artificial aging treatment group Dong156 and Dong237.</p>
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<p>Summary of miRNA sizes and differentially expressed miRNAs and the number of identified known miRNAs. (<b>A</b>) The size distribution of unique sRNAs. (<b>B</b>) Differential expression heat maps of known miRNAs and putative miRNA s in maize. R+n means that there are n more bases on the right end of the miRNA included in the miRBase, R−n means that there are n fewer bases on the right end of the miRNA included in the miRBase, L+n means that there are n more bases on the left end of the miRNA included in the miRBase, L+n means that there are n more bases on the left end of the miRNA included in the miRBase, 2ss12AT20TA means that the 12th base T is replaced by A (ss, substitution), and the 20th base T is replaced by A, a total of 2. New miRNAs are labeled with PC (Predicted Candidate).</p>
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<p>The miRNA, target, and degradation product abundances by RNA seq and validation by alternative methods. (<b>A</b>) high throughput sequencing to verify miRNA, the orange bars indicate the up-regulated miRNA and the blue bars indicate the down-regulated miRNA. (<b>B</b>) qRT-PCR to verify miRNA, the orange bars indicate the up-regulated miRNA and the blue bars indicate the down-regulated miRNA. (<b>C</b>) high throughput sequencing to verify target genes, the orange bars indicate the up-regulated target genes and the blue bars indicate the down-regulated target genes. (<b>D</b>) qRT-PCR to verify target genes, the orange bars indicate the up-regulated target genes and the blue bars indicate the down-regulated target genes. (<b>E</b>–<b>L</b>) the relative abundance of zma-miR169o-5p, zma-miR390a-5p, zma-miR396c_L-1, zma-miR397b-p5, zma-miR444, and novel-miR4 and their target genes. The red dot is the same position of the predicted target gene and the degradation site obtained from the degradome sequencing, and the black line is the actual degradation site measured in the degradome sequencing. (<b>M</b>) zma-miR169o-5p_R-1 acts on the cleavage site of the target gene <span class="html-italic">GRMZM2G165488</span> were confirmed by 5′RLM-RACE. (<b>N</b>) zma-miR444a_1ss12TC acts on the cleavage site of the target gene <span class="html-italic">GRMZM2G399072</span> were confirmed by 5′RLM-RACE. The yellow bars represent the exon part of the gene, the green bars represent the cutting site part of the target gene, the black broken line represents the intron part of the gene, and the blue bars represent other structures of the gene.</p>
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<p>Differential expression of the known and putative miRNAs and target genes in maize. FC, fold change. Orange bars represent expression of miRNAs; blue bars represent expression of target genes.</p>
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<p>Analysis of the targets of identified miRNAs in maize seed storability. (<b>A</b>) Hierarchical clustering of DEGs expression. (<b>B</b>) Profile of GO analysis of the targets of identified responsive miRNAs in maize seed storability. (<b>C</b>) Profile of KEGG analysis of the targets of identified responsive miRNAs in maize seed storability.</p>
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<p>Analysis of differentially expressed genes in transcriptome. (<b>A</b>) Gene ontology LoopCircos of DEGs. (<b>B</b>) The volcano map of differentially expressed genes. (<b>C</b>) Hierarchical clustering of DEGs expression. (<b>D</b>) KEGG enrichment analysis of DEGs.</p>
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<p>Possible microRNAs-dependent regulatory pathways that participate in seed storability during maize germination.</p>
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24 pages, 5617 KiB  
Article
Insights into the Regulation of Rice Seed Storability by Seed Tissue-Specific Transcriptomic and Metabolic Profiling
by Fangzhou Liu, Nannan Li, Yuye Yu, Wei Chen, Sibin Yu and Hanzi He
Plants 2022, 11(12), 1570; https://doi.org/10.3390/plants11121570 - 14 Jun 2022
Cited by 9 | Viewed by 2439
Abstract
Non-dormant seeds are continuously aging and deteriorating during storage, leading to declining seed vigor, which is a challenge for the rice seed industry. Improving the storability of seeds is of great significance to ensure the quality of rice and national food security. Through [...] Read more.
Non-dormant seeds are continuously aging and deteriorating during storage, leading to declining seed vigor, which is a challenge for the rice seed industry. Improving the storability of seeds is of great significance to ensure the quality of rice and national food security. Through a set of chromosome segment substitution lines population constructed using japonica rice NIP as donor parent and indica rice ZS97 as recurrent parent, we performed seed storability QTL analysis and selected four non-storable NILs to further investigate the storability regulatory mechanisms underlying it. The seeds were divided into four tissues, which were the embryo, endosperm, aleurone layer, and hull, and tissue-specific transcriptome and metabolome analyses were performed on them. By exploring the common differentially expressed genes and differentially accumulated metabolites, as well as the KEGG pathway of the four non-storable NILs, we revealed that the phenylpropanoid biosynthesis pathway and diterpenoid biosynthesis pathway played a central role in regulating seed storability. Integrated analysis pinpointed 12 candidate genes that may take part in seed storability. The comprehensive analysis disclosed the divergent and synergistic effect of different seed tissues in the regulation of rice storability. Full article
(This article belongs to the Special Issue Genomic Breeding of Green Crops)
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<p>Seed germination phenotype of the two parents NIP and ZS97 before and after artificial aging. ** indicates a significant difference with ZS97 at <span class="html-italic">p</span> &lt; 0.01. <span class="html-italic">p</span>-values were calculated by Student’s <span class="html-italic">t</span>-test.</p>
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<p>Frequency distribution of the germination parameters before and after artificial aging of the CSSLs population. (<b>A</b>) Distribution map of Gmax before artificial aging, (<b>B</b>) Distribution map of T50 before artificial aging, (<b>C</b>) Distribution map of T10 before artificial aging, (<b>D</b>) Distribution map of AUC before artificial aging, (<b>E</b>) Distribution map of Gmax after artificial aging, (<b>F</b>) Distribution map of T50 after artificial aging, (<b>G</b>) Distribution map of T10 after artificial aging, (<b>H</b>) Distribution map of AUC after artificial aging.</p>
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<p>Histogram of Circos, four indicators for evaluating the storage quality of rice seeds. The outermost circle of the Circos diagram is the chromosome marker distribution map. The chromosomes (chr01-chr12) are arranged clockwise. Each sector bar is the size of the chromosome (Mb). The second circle is the Gmax (maximum germination percentage of seven days germination) indicator QTL distribution chart, yellow is <span class="html-italic">p</span> &gt; 0.01, the black is <span class="html-italic">p</span> ≤ 0.01; the third circle is the AUC (area under the germination curve until 168 h) indicator QTL distribution chart, red is <span class="html-italic">p</span> &gt; 0.01, and black is <span class="html-italic">p</span> ≤ 0.01; The fourth circle is the T50 (time to reach 50% germination of the total number of germinated seeds) index QTL distribution chart, green is <span class="html-italic">p</span> &gt; 0.01, black is <span class="html-italic">p</span> ≤ 0.01; the fifth circle is the T10 (time to reach 10% germination of the total number of germinated seeds) index QTL distribution chart, blue is <span class="html-italic">p</span> &gt; 0.01, and black is <span class="html-italic">p</span> ≤ 0.01. The QTL analysis is calculated by the R language ridge packet RR algorithm.</p>
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<p>Array chip information of the four selected non-storable NILs. NIP introgressed segment is shown in red, and the gray background is the background material ZS97.</p>
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<p>Total antioxidant content in rice seeds. (<b>A</b>) Total antioxidant content of unaged rice seeds; (<b>B</b>) Total antioxidant content of rice seeds after artificial aging. ** indicates significant difference at <span class="html-italic">p</span> &lt; 0.01. <span class="html-italic">p</span> values were calculated by Student’s <span class="html-italic">t</span>-test.</p>
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<p>The number of DEGs (differentially expressed genes) in four NILs compared with ZS97. Em, Ed and Al represent embryo, endosperm, and aleurone layer.</p>
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<p>Venn diagram of differential expressed genes between 4 selected NILs (NZ19, NZ24, NZ29, NZ127) and ZS97. Em, Ed, Al, and Hu represent embryo, endosperm, aleurone layer, and hull.</p>
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<p>Classification of identified 261 metabolites.</p>
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<p>General analysis of metabolites in four non-storable NILs and ZS97. PCA (<b>A</b>) and heatmap (<b>B</b>) of all the metabolites. Em, Ed, Al, and Hu represent embryo, endosperm, aleurone layer, and hull, respectively.</p>
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<p>Venn diagram of different metabolites between four selected NILs (NZ19, NZ24, NZ29, NZ127) and ZS97. Em, Ed, Al, and Hu represent embryo, endosperm, aleurone layer, and hull.</p>
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<p>Differential expressed genes and differential accumulated metabolites mapped on phenylpropanoid biosynthesis pathway of embryo (<b>A</b>), endosperm (<b>B</b>), and aleurone layer (<b>C</b>).</p>
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<p>Differential expressed genes and differential accumulated metabolites mapped on diterpenoid biosynthesis pathway of embryo (<b>A</b>), endosperm (<b>B</b>), and aleurone layer (<b>C</b>).</p>
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<p>Differential expressed genes and differential accumulated metabolites mapped on diterpenoid biosynthesis pathway of embryo (<b>A</b>), endosperm (<b>B</b>), and aleurone layer (<b>C</b>).</p>
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<p>Schematic diagram and sequence variation of 12 candidate genes. (<b>A</b>) LOC_Os01g71860; (<b>B</b>) LOC_Os04g33640; (<b>C</b>) LOC_Os04g39864; (<b>D</b>) LOC_Os04g52210; (<b>E</b>) LOC_Os04g52504; (<b>F</b>) LOC_Os04g53630; (<b>G</b>) LOC_Os04g48290; (<b>H</b>) LOC_Os04g43410; (<b>I</b>) LOC_Os04g44500; (<b>J</b>) LOC_Os04g44150; (<b>K</b>) LOC_Os04g43800; (<b>L</b>) LOC_Os04g44580.</p>
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12 pages, 6002 KiB  
Article
Identification, Analysis, and Confirmation of Seed Storability-Related Loci in Dongxiang Wild Rice (Oryza rufipogon Griff.)
by Minmin Zhao, Biaolin Hu, Yuanwei Fan, Gumu Ding, Wanling Yang, Yong Chen, Yanhong Chen, Jiankun Xie and Fantao Zhang
Genes 2021, 12(11), 1831; https://doi.org/10.3390/genes12111831 - 19 Nov 2021
Cited by 7 | Viewed by 2686
Abstract
Dongxiang wild rice (Oryza rufipogon Griff.) (DXWR) has strong seed storability and identifying its elite gene resources may facilitate genetic improvements in rice seed storability. In this study, we developed two backcross inbred lines (BILs) populations, with DXWR as a common donor [...] Read more.
Dongxiang wild rice (Oryza rufipogon Griff.) (DXWR) has strong seed storability and identifying its elite gene resources may facilitate genetic improvements in rice seed storability. In this study, we developed two backcross inbred lines (BILs) populations, with DXWR as a common donor parent and two rice varieties (F6 and R974) as recipient parents. Bulked segregant analysis via whole genome sequencing (BSA-seq) was used to identify seed storability-related loci in the DXWR and F6 population. Two main genomic regions containing 18,550,000–20,870,000 bp on chromosome 4 and 7,860,000–9,780,000 bp on chromosome 9 were identified as candidate loci of DXWR seed storability; these overlapped partially with seed storability-related quantitative trait loci (QTLs) discovered in previous studies, suggesting that these loci may provide important regions for isolating the responsible genes. In total, 448 annotated genes were predicted within the identified regions, of which 274 and 82 had nonsynonymous and frameshift mutations, respectively. We detected extensive metabolic activities and cellular processes during seed storability and confirmed the effects of the seed storability-related candidate loci using four BILs from DXWR and R974. These results may facilitate the cloning of DXWR seed storability-related genes, thereby elucidating rice seed storability and its improvement potential. Full article
(This article belongs to the Section Plant Genetics and Genomics)
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<p>Germination rates of Dongxiang wild rice (DXWR), F6 and their backcross inbred lines (BILs) after aging treatment. (<b>a</b>) the germination rate of DXWR was significantly higher than that of F6 after aging treatment. (<b>b</b>) average germination rates of DXWR and F6 after aging treatment. Significant differences are indicated by asterisks (Student’s <span class="html-italic">t</span>-test; <span class="html-italic">p</span>-value &lt; 0.01). (<b>c</b>) distribution of germination rates in BILs population from crosses DXWR and F6 after aging treatment.</p>
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<p>The identifcation of candidate genome regions for seed storability by Euclidean Distance (ED) and delta—index methods. (<b>a</b>) SNP—ED association algorithm. (<b>b</b>) InDel—ED association algorithm. (<b>c</b>) Δ(SNP—index) method. (<b>d</b>) Δ(InDel—index) method. The abscissa is the chromosome number, the colored dot represents the calculated ED value or Δ(SNP—index)/Δ(InDel—index) value of the SNP/InDel site, and the black line is the fitted ED value or Δ(SNP—index)/Δ(InDel—index) value. The red dashed horizontal lines represent the signifcance association threshold.</p>
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<p>Graphical representation of four BILs (BIL27, BIL49, BIL64, and BIL203) from the DXWR and R974 population. White squares represent homozygous for R974 allele, black squares represent homozygous for DXWR allele.</p>
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<p>Germination rates of the parents and the four BILs after aging treatment. (<b>a</b>) Comparison of germination rates between DXWR and R974 after aging treatment. (<b>b</b>) phenotype chart of DR49 and DR203. (<b>c</b>) phenotype chart of DR27 and DR64. (<b>d</b>) average germination rates of parents and four BILs after aging treatment. Significant differences are indicated by asterisks (Student’s <span class="html-italic">t</span>-test; ** <span class="html-italic">p</span>-value &lt; 0.01).</p>
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13 pages, 2293 KiB  
Article
Genome-Wide Association Study Reveals the QTLs for Seed Storability in World Rice Core Collections
by Fangxi Wu, Xi Luo, Lingqiang Wang, Yidong Wei, Jianguo Li, Huaan Xie, Jianfu Zhang and Guosheng Xie
Plants 2021, 10(4), 812; https://doi.org/10.3390/plants10040812 - 20 Apr 2021
Cited by 14 | Viewed by 3073
Abstract
Seed storability is a main agronomically important trait to assure storage safety of grain and seeds in rice. Although many quantitative trait loci (QTLs) and associated genes for rice seed storability have been identified, the detailed genetic mechanisms of seed storability remain unclear [...] Read more.
Seed storability is a main agronomically important trait to assure storage safety of grain and seeds in rice. Although many quantitative trait loci (QTLs) and associated genes for rice seed storability have been identified, the detailed genetic mechanisms of seed storability remain unclear in rice. In this study, a genome-wide association study (GWAS) was performed in 456 diverse rice core collections from the 3K rice genome. We discovered the new nine QTLs designated as qSS1-1, qSS1-2, qSS2-1, qSS3-1, qSS5-1, qSS5-2, qSS7-1, qSS8-1, and qSS11-1. According to the analysis of the new nine QTLs, our results could well explain the reason why seed storability of indica subspecies was superior to japonica subspecies in rice. Among them, qSS1-2 and qSS8-1 were potentially co-localized with a known associated qSS1/OsGH3-2 and OsPIMT1, respectively. Our results also suggest that pyramiding breeding of superior alleles of these associated genes will lead to new varieties with improved seed storability in the future. Full article
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<p>The distribution of SNPs, analysis of population structure, and LD decay of 456 rice accessions. (<b>A</b>) The distribution of SNPs in the rice genome analysis of population structure. (<b>B</b>) principal component, the red indicates the <span class="html-italic">indica</span> subspecies, while the purple indicates the <span class="html-italic">japonica</span> subspecies. (<b>C</b>) Phylogenetic tree: the blue indicates the <span class="html-italic">indica</span> subspecies, while the yellow indicates the <span class="html-italic">japonica</span> subspecies. (<b>D</b>) LD (linkage disequilibrium) decay of 456 rice accessions in the whole population, <span class="html-italic">indica</span> subspecies, and <span class="html-italic">japonica</span> subspecies, subspecies.</p>
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<p>The difference phenotype of 456 rice accessions. (<b>A</b>) Distribution and variations of seed germination percentage at temperature 42 °C and relative humidity 88% for 20 days in the whole population, <span class="html-italic">indica</span> subspecies, and <span class="html-italic">japonica</span> subspecies. (<b>B</b>) Scatter dot plot of seed germination percentage in <span class="html-italic">indica</span> subspecies and <span class="html-italic">japonica</span> subspecies. **** denote significant differences in mean phenotypic between superior and inferior alleles at <span class="html-italic">p</span> &gt; 0.0001.</p>
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<p>Quantile-quantile (Q-Q) and Manhattan plots of GWAS for rice seed storability in the whole population (<b>A</b>), <span class="html-italic">indica</span> subspecies (<b>B</b>), and <span class="html-italic">japonica</span> subspecies (<b>C</b>), respectively.</p>
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<p>The box plots showing the phenotypic distribution at the nine superior and inferior alleles of lead SNPs in the world core collection. The plus shows the mean; the middle line shows the median, and the box shows the range of the 25th to 75th percentiles of the total data and the whiskers show the interquartile range and the outliers. ns, *, **, *** and **** denote significant differences in mean phenotypic between superior and inferior alleles at <span class="html-italic">p</span> &gt; 0.05, <span class="html-italic">p</span> &lt; 0.05, 0.01, 0.001, and 0.0001, respectively.</p>
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<p>The average germination percentage of pyramiding QTL superior alleles in the whole population.</p>
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<p>Scatter dot plot of seed germination percentage at temperature 42 °C and relative humidity 88% for 20 days in different subgroups.</p>
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<p>Comparison of the frequency of these QTLs superior alleles in <span class="html-italic">indica</span> and <span class="html-italic">japonica</span> subspecies.</p>
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22 pages, 2515 KiB  
Review
Challenges and Prospects for the Conservation of Crop Genetic Resources in Field Genebanks, in In Vitro Collections and/or in Liquid Nitrogen
by Bart Panis, Manuela Nagel and Ines Van den houwe
Plants 2020, 9(12), 1634; https://doi.org/10.3390/plants9121634 - 24 Nov 2020
Cited by 104 | Viewed by 12406
Abstract
The conservation of crop genetic resources, including their wild relatives, is of utmost importance for the future of mankind. Most crops produce orthodox seeds and can, therefore, be stored in seed genebanks. However, this is not an option for crops and species that [...] Read more.
The conservation of crop genetic resources, including their wild relatives, is of utmost importance for the future of mankind. Most crops produce orthodox seeds and can, therefore, be stored in seed genebanks. However, this is not an option for crops and species that produce recalcitrant (non-storable) seeds such as cacao, coffee and avocado, for crops that do not produce seeds at all; therefore, they are inevitably vegetatively propagated such as bananas, or crops that are predominantly clonally propagated as their seeds are not true to type, such as potato, cassava and many fruit trees. Field, in vitro and cryopreserved collections provide an alternative in such cases. In this paper, an overview is given on how to manage and setup a field, in vitro and cryopreserved collections, as well as advantages and associated problems taking into account the practical, financial and safety issues in the long-term. In addition, the need for identification of unique accessions and elimination of duplicates is discussed. The different conservation methods are illustrated with practical examples and experiences from national and international genebanks. Finally, the importance of establishing safe and long-term conservation methods and associated backup possibilities is highlighted in the frame of the global COVID-19 pandemic. Full article
(This article belongs to the Special Issue Plant Biodiversity and Genetic Resources)
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<p>Pros and cons of storing crop genetic resources in field genebanks, in vitro and through cryopreservation. Arrows indicate the potential source material and target approach to maintain plant genetic resources for food and agriculture (PGRFA) and can be specific for each plant species.</p>
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<p><span class="html-italic">Allium</span> field collection at IPK, Germany.</p>
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<p>Banana in vitro collection at the International Transit Centre (ITC), Belgium.</p>
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<p>Cryopreservation facilities at the ITC.</p>
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