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Search Results (1,358)

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Keywords = rice (Oryza sativa)

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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 (registering DOI) - 5 Jan 2025
Viewed by 60
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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Figure 1

Figure 1
<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>
Full article ">Figure 2
<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>
Full article ">Figure 3
<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>
Full article ">Figure 3 Cont.
<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>
Full article ">Figure 3 Cont.
<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>
Full article ">Figure 4
<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>
Full article ">Figure 4 Cont.
<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>
Full article ">
16 pages, 993 KiB  
Article
Piriformospora indica Enhances Rice Blast Resistance and Plant Growth
by Manegdebwaoga Arthur Fabrice Kabore, Guanpeng Huang, Changqing Feng, Shuhong Wu, Jiayi Guo, Guofeng Wu, Yiqiong Sun, Samuel Tareke Woldegiorgis, Yufang Ai, Lina Zhang, Wei Liu and Huaqin He
Agronomy 2025, 15(1), 118; https://doi.org/10.3390/agronomy15010118 (registering DOI) - 4 Jan 2025
Viewed by 286
Abstract
Rice blast disease, caused by Magnaporthe oryzae (M. oryzae), is a significant threat to global rice production. Conventional methods for disease management face limitations, emphasizing the importance of sustainable alternatives. In this study, two rice cultivars with different blast resistance abilities, [...] Read more.
Rice blast disease, caused by Magnaporthe oryzae (M. oryzae), is a significant threat to global rice production. Conventional methods for disease management face limitations, emphasizing the importance of sustainable alternatives. In this study, two rice cultivars with different blast resistance abilities, the susceptible variety CO39 and the resistant variety Pi4b, were used as materials to study the effects of Piriformospora indica (Pi) on the resistance to M. oryzae infection and rice growth. The in vitro tests revealed no direct antagonistic interaction between Pi and M. oryzae. However, the in vivo experiments showed that Pi promoted plant growth by increasing root and shoot length, chlorophyll content, and nitrogen uptake, particularly in CO39 during pathogen infection. Pi inoculation also significantly reduced disease severity, which was indicated by smaller lesion areas and shorter lesion lengths in both cultivars but a more pronounced effect in CO39. This occurred due to the decreasing levels of MDA and the modulating activity of antioxidant enzymes in Pi-inoculated rice plants. At the early stage of M. oryzae infection, the expression of the ethylene signaling gene OsEIN2 and the gibberellin biosynthesis gene OsGA20ox1 in Pi-inoculated CO39 decreased but significantly increased in both rice cultivars at the later stage. The reverse was found for the pathogenesis-related (PR) genes OsPR10 and OsPBZ1 and the blast-resistant genes OsBRG1, OsBRG2, and OsBRW1, suggesting early growth suppression for rice resilience to blast followed by a later shift back to growth. Meanwhile, Pi inoculation increased OsCesA9 expression in rice to strengthen cell walls and establish the primary defense barrier against M. oryzae and upregulated the expression of OsNPR1 without a significant difference in CO39 but downregulated it in Pi4b to activate PR genes to enhance plant resistance. In summary, these results underscore the potential of Pi as a sustainable biological control agent for rice blast disease, which is particularly beneficial for blast-susceptible rice cultivars. Full article
(This article belongs to the Section Pest and Disease Management)
23 pages, 3877 KiB  
Article
Split Application of Potassium Reduces Rice Chalkiness by Regulating Starch Accumulation Process Under High Temperatures
by Xinyue Zhang, Youfa Li, Junjie Dong, Yuanze Sun and Haowei Fu
Agronomy 2025, 15(1), 116; https://doi.org/10.3390/agronomy15010116 (registering DOI) - 4 Jan 2025
Viewed by 279
Abstract
Chalkiness in rice is adversely affected by high temperatures during the flowering and grain-filling stages. Potassium (K) is essential for improving grain quality and heat resilience. The effects of split application K fertilizer on rice chalkiness under high temperatures during the flowering and [...] Read more.
Chalkiness in rice is adversely affected by high temperatures during the flowering and grain-filling stages. Potassium (K) is essential for improving grain quality and heat resilience. The effects of split application K fertilizer on rice chalkiness under high temperatures during the flowering and grain-filling stages were investigated in this study. Four treatments, including ambient temperatures with basal K fertilizer (AT-K1), high temperatures with basal K fertilizer (HT-K1), high temperatures with 70% K pre-transplanting and 30% K at the heading stage (HT-K2), and high temperatures with 30% K pre-transplanting and 70% K at the heading stage (HT-K3), were conducted. The results revealed that the chalky grain rate and chalkiness degree were reduced by 9.2–13.72% and 12.16–19.91%, respectively, by the split application of K fertilizer through effectively modulating the sucrose-to-starch conversion process in the rice grains, relative to the single basal application of K fertilizer under high temperatures. Specifically, the split application of K fertilizer reduced the enzymatic activities of SuSy, ADPGase, and SBE by 3.17–34.20% at 5–10 DAA, and GBSS and SSS by 6.48–13.50% at 5 DAA, but enhanced them by 5.50–54.90% from 15 DAA and 2.07–97.10% from 10 DAA. Similarly, the gene expression levels of enzymes involved in this process were decreased by 3.52–24.12% at 5 DAA but increased by 8.61–30.00% at 20 DAA by the split application of K fertilizer. This modulation led to a retardation in the excessive accumulation of starch during the early grain-filling stage but a higher starch accumulation rate during the middle and later stages, combined with a longer duration of starch accumulation, ultimately resulting in higher starch accumulation and reduced rice chalkiness. These results suggest that the application of K fertilizer during the heading stage is effective in compensating the deterioration of rice chalkiness by high temperatures. Full article
(This article belongs to the Section Plant-Crop Biology and Biochemistry)
16 pages, 7321 KiB  
Article
The Relative Contribution of Root Morphology and Arbuscular Mycorrhizal Fungal Colonization on Phosphorus Uptake in Rice/Soybean Intercropping Under Dry Cultivation
by Huimin Ma, Hongcheng Zhang, Qian Gao, Shilin Li, Yuanyuan Yu, Jiaying Ma, Congcong Zheng, Meng Cui, Zhihai Wu and Hualiang Zhang
Plants 2025, 14(1), 106; https://doi.org/10.3390/plants14010106 - 2 Jan 2025
Viewed by 234
Abstract
Intercropping has the potential to improve phosphorus (P) uptake and crop growth, but the potential benefits and relative contributions of root morphology and arbuscular mycorrhizal fungi (AMF) colonization are largely unknown for the intercropping of rice and soybean under dry cultivation. Both field [...] Read more.
Intercropping has the potential to improve phosphorus (P) uptake and crop growth, but the potential benefits and relative contributions of root morphology and arbuscular mycorrhizal fungi (AMF) colonization are largely unknown for the intercropping of rice and soybean under dry cultivation. Both field and pot experiments were conducted with dry-cultivated rice (Oryza sativa L.) and soybean (Glycine max L. Merr.) grown alone or intercropped under two P levels. Two root separation modes between rice and soybean were employed to explore the contribution of AMF association and root plasticity on P uptake in intercrops. The results showed that rice/soybean intercropping resulted in a notable increase in the total biomass and yield compared to monoculture in the field. In the potted experiment, compared to the plastic root separation treatment (PS), the no root separation treatment (NS) increased the total biomass and P uptake by 9.4% and 19.9%, irrespective of the P levels. This was primarily attributable to a considerable enhancement in biomass and phosphorus uptake in soybean by 40.4% and 49.7%, which offset a slight decline in the rice of NS compared to PS by 26.8% and 18.0%, respectively. The results of random forest analysis indicate that the P uptake by the dominant species, soybean, was mainly contributed by root morphology, while rice was more dependent on AMF colonization in the intercropping system. Therefore, dry-cultivated rice/soybean intercropping enhances P uptake and productivity by leveraging complementary belowground strategies, with soybean benefiting primarily from root morphological adjustments and rice relying more on arbuscular mycorrhizal fungi colonization. Full article
(This article belongs to the Section Plant Response to Abiotic Stress and Climate Change)
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Figure 1

Figure 1
<p>Rice (<b>A</b>), soybean (<b>B</b>) and total (<b>C</b>) biomass under monoculture and intercropping pattern at two P levels in the field. Sole represents monoculture, and Inter represents rice/soybean intercropping system; P0 and P1 represent without and with P fertilizer addition, respectively; P represents P level; CP represents cropping pattern. Different capital letters represent significant differences between two P levels within the same cropping pattern at <span class="html-italic">p</span> &lt; 0.05; different lowercase letters denote significant differences between the different cropping patterns within the same P level at <span class="html-italic">p</span> &lt; 0.05. Values = means ± SE (<span class="html-italic">n</span> = 5). The values are the F values; **, *** and “ns” indicate significance at <span class="html-italic">p</span> &lt; 0.01, <span class="html-italic">p</span> &lt; 0.001 and no significant difference, respectively.</p>
Full article ">Figure 2
<p>Rice (<b>A</b>), soybean (<b>B</b>) and total (<b>C</b>) yield response efficiency under two P levels in the field. P0 and P1 represent without and with P fertilizer addition, respectively. The same capital letters represent no significant difference between the two P levels at <span class="html-italic">p</span> &lt; 0.05. Values = means ± SE (<span class="html-italic">n</span> = 5). The values are the F values; “ns” indicates no significant difference between two P levels.</p>
Full article ">Figure 3
<p>Rice (<b>A</b>), soybean (<b>B</b>) and total biomass (<b>C</b>) under the different root separation modes at two P levels in pots. PS represents a complete plastic root separation between rice and soybean grown in pots; NS represents no root separation between rice and soybean grown in pots. P0 and P1 represent without and with P fertilizer addition, respectively; P represents P level; SM represents root separation mode. Different capital letters represent significant differences between two P levels within the same root separation mode at <span class="html-italic">p</span> &lt; 0.05; different lowercase letters denote significant differences between the different root separation modes within the same P level at <span class="html-italic">p</span> &lt; 0.05. Values = means ± SE (<span class="html-italic">n</span> = 5). The values are the F values; **, *** and “ns” indicate significance at <span class="html-italic">p</span> &lt; 0.01, <span class="html-italic">p</span> &lt; 0.001 and no significant difference, respectively.</p>
Full article ">Figure 4
<p>Rice (<b>A</b>), soybean (<b>B</b>) and total P uptake (<b>C</b>) under the different root separation modes at two P levels in pots. PS represents a complete plastic root separation between rice and soybean grown in pots; NS represents no root separation between rice and soybean grown in pots. P0 and P1 represent without and with P fertilizer addition, respectively; P represents P level; SM represents root separation mode. Different capital letters represent significant differences between two P levels within the same root separation mode at <span class="html-italic">p</span> &lt; 0.05; different lowercase letters denote significant differences between the different root separation modes within the same P level at <span class="html-italic">p</span> &lt; 0.05. Values = means ± SE (<span class="html-italic">n</span> = 5). The values are the F values; *, **, *** and “ns” indicate significance at <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 and no significant difference, respectively.</p>
Full article ">Figure 5
<p>Root length, root surface area, root volume and root dry weight of rice (<b>A</b>–<b>D</b>) and soybean (<b>E</b>–<b>H</b>) under the different root separation modes at two P levels in pots. PS represents a complete plastic root separation between rice and soybean grown in pots; NS represents no root separation between rice and soybean grown in pots. P0 and P1 represent without and with P fertilizer addition, respectively; P represents P level; SM represents root separation mode. Different capital letters represent significant differences between two P levels within the same root separation mode at <span class="html-italic">p</span> &lt; 0.05; different lowercase letters denote significant differences between the different root separation modes within the same P level at <span class="html-italic">p</span> &lt; 0.05. Values = means ± SE (<span class="html-italic">n</span> = 5). The values are the F values; *, **, *** and “ns” indicate significance at <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 and no significant difference, respectively.</p>
Full article ">Figure 6
<p>Mycorrhizal infection density, arbuscular mycorrhiza (AM) richness and vesicle richness of rice (<b>A</b>–<b>C</b>) and soybean (<b>D</b>–<b>F</b>) under the different root separation modes at two P levels in pots. PS represents a complete plastic root separation between rice and soybean grown in pots; NS represents no root separation between rice and soybean grown in pots. P0 and P1 represent without and with P fertilizer addition, respectively; P represents P level; SM represents root separation mode. Different capital letters represent significant differences between two P levels within the same root separation mode at <span class="html-italic">p</span> &lt; 0.05; different lowercase letters denote significant differences between the different root separation modes within the same P level at <span class="html-italic">p</span> &lt; 0.05. Values = means ± SE (<span class="html-italic">n</span> = 5). The values are the F values; **, *** and “ns” indicate significance at <span class="html-italic">p</span> &lt; 0.01, <span class="html-italic">p</span> &lt; 0.001 and no significant difference, respectively.</p>
Full article ">Figure 7
<p>Random forest analysis to identify the main predictors of P uptake in rice (<b>A</b>) and soybean (<b>B</b>). * and ** indicate significance between the predictors and P uptake at <span class="html-italic">p</span> &lt; 0.05 and <span class="html-italic">p</span> &lt; 0.01. Abbreviations of the conceptual schema are defined as follows: root length (RL); root surface area (RA); root volume (RV); root dry weight (RDW); mycorrhizal infection density (MID); arbuscular mycorrhiza richness (AMR); vesicle richness (VER); mean square error (MSE).</p>
Full article ">Figure 8
<p>Schematic diagram showing the root separation modes in pots and plant performance of rice and soybean at harvest time. PS represents a complete plastic root separation between rice and soybean grown in pots; NS represents no root separation between rice and soybean grown in pots. P0 and P1 represent without and with P fertilizer addition, respectively.</p>
Full article ">
18 pages, 4436 KiB  
Article
Combining Controlled-Release and Normal Urea Enhances Rice Grain Quality and Starch Properties by Improving Carbohydrate Supply and Grain Filling
by Chang Liu, Tianyang Zhou, Zhangyi Xue, Chenhua Wei, Kuanyu Zhu, Miao Ye, Weiyang Zhang, Hao Zhang, Lijun Liu, Zhiqin Wang, Junfei Gu and Jianchang Yang
Plants 2025, 14(1), 107; https://doi.org/10.3390/plants14010107 - 2 Jan 2025
Viewed by 217
Abstract
Controlled-release nitrogen fertilizers are gaining popularity in rice (Oryza stavia L.) cultivation for their ability to increase yields while reducing environmental impact. Grain filling is essential for both the yield and quality of rice. However, the impact of controlled-release nitrogen fertilizer on [...] Read more.
Controlled-release nitrogen fertilizers are gaining popularity in rice (Oryza stavia L.) cultivation for their ability to increase yields while reducing environmental impact. Grain filling is essential for both the yield and quality of rice. However, the impact of controlled-release nitrogen fertilizer on grain-filling characteristics, as well as the relationship between these characteristics and rice quality, remains unclear. This study aimed to identify key grain-filling characteristics influencing rice milling quality, appearance, cooking and eating qualities, and physicochemical properties of starch. In this study, a two-year field experiment was conducted that included four nitrogen management practices: zero nitrogen input (CK), a local high-yield practice with split urea applications (100% urea, CU), a single basal application of 100% controlled-release nitrogen fertilizer (CRNF), and a basal application blend of 70% controlled-release nitrogen fertilizer with 30% urea (CRNF-CU). The results showed that a sufficient amount of carbohydrates for the rice grain-filling process, as indicated by a higher sugar–spikelet ratio, is essential for improving grain quality. An increased sugar–spikelet ratio enhances the grain-filling process, resulting in an elevated average grain-filling rate (Gmean) and the peak grain-filling rate (Gmax), while also reducing the overall time required for grain filling (D). Compared to CU, CRNF and CRNF-CU treatments did not significantly change milling qualities, but reduced the chalky kernel rate and chalkiness, thereby enhancing the appearance quality. These treatments increased the amylose and amylopectin contents while reducing protein content, though the proportion of protein constituents remained unchanged. These treatments led to larger starch granules with notably smoother surfaces. Additionally, CRNF and CRNF-CU reduced relative crystallinity and structural order, while increasing the amorphous structure in the outer region of starch granules, which lowered rice starch crystal stability. The treatments also increased viscosity and improved the thermodynamic properties of starch, resulting in enhanced eating and cooking quality of the rice. In conclusion, the CRNF-CU is the most effective strategy in this study to enhance both grain yield and quality. This practice ensures an adequate carbohydrate supply for grain filling, which is essential for efficient grain filling and improved overall quality. Full article
(This article belongs to the Special Issue Rice Physiology and Production)
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Figure 1

Figure 1
<p>The effects of different nitrogen fertilizer treatments on grain yield (<b>A</b>) and its components (panicles per m<sup>2</sup>, (<b>B</b>); spikelets per panicle, (<b>C</b>); total sipkelets per m<sup>2</sup>, (<b>D</b>); filled grains, (<b>E</b>); 1000-grain weight, (<b>F</b>)). Different letters indicate significant differences between different treatments of the same year (<span class="html-italic">p</span> &lt; 0.05). CK, control check treatment; CU, conventional urea treatment; CRNF, controlled-release nitrogen fertilizer treatment; CRNF-CU, combined 30% conventional urea with 70% controlled-release nitrogen fertilizer treatment.</p>
Full article ">Figure 2
<p>The effects of different nitrogen fertilizer treatments on the dynamics of grain weight (<b>A</b>) and the dynamics of grain-filling rate (<b>B</b>). Both measured values and fitted curves are plotted for grain weight changes, while only calculated curves are shown for grain-filling dynamics. CK, control check treatment; CU, conventional urea treatment; CRNF, controlled-release nitrogen fertilizer treatment; CRNF-CU, combined 30% conventional urea with 70% controlled-release nitrogen fertilizer treatment.</p>
Full article ">Figure 3
<p>The effects of different nitrogen fertilizer treatments on the morphology of rice starch granules in 2022. (<b>A</b>–<b>D</b>) The morphology of rice starch granules of grain filling under CK, CU, CRNF, and CRNF-CU treatments, respectively; D10, D20, D30, the morphology of rice starch granules observed at 10, 20, and 30 days post anthesis, respectively. Magnifications = 2000×. CK, control check treatment; CU, conventional urea treatment; CRNF, controlled-release nitrogen fertilizer treatment; CRNF-CU, combined 30% conventional urea with 70% controlled-release nitrogen fertilizer treatment.</p>
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<p>The effects of different nitrogen fertilizer treatments on the X-ray diffraction patterns of rice starch in 2021 (<b>A</b>) and 2022 (<b>B</b>). CK, control check treatment; CU, conventional urea treatment; CRNF, controlled-release nitrogen fertilizer treatment; CRNF-CU, combined 30% conventional urea with 70% controlled-release nitrogen fertilizer treatment.</p>
Full article ">Figure 5
<p>The effects of different nitrogen fertilizer treatments on starch Fourier Transform Infrared (FTIR) spectra of rice starch. CK, control check treatment; CU, conventional urea treatment; CRNF, controlled-release nitrogen fertilizer treatment; CRNF-CU, combined 30% conventional urea with 70% controlled-release nitrogen fertilizer treatment.</p>
Full article ">Figure 6
<p>The essential amino acid content of rice grain (<b>A</b>–<b>D</b>) and non-essential amino acid content of rice grain (<b>a</b>–<b>d</b>) under CK (<b>A</b>,<b>a</b>), CU (<b>B</b>,<b>b</b>), CRNF (<b>C</b>,<b>c</b>), and CRNF-CU (<b>D</b>,<b>d</b>) treatments. Phe, phenylalanine; Lys, lysine; Val, valine; Met, methionine; Thr, threonine; Ile, isoleucine; Leu, leucine; His, histidine; Arg, arginase; Asp, aspartic; Ser, serine; Glu, glutamic; Gly, glycine; Ala, alanine; Pro, proline; Tyr, tyrosine; Cys, cysteine; CK, control check treatment; CU, conventional urea treatment; CRNF, controlled-release nitrogen fertilizer treatment; CRNF-CU, combined 30% conventional urea with 70% controlled-release nitrogen fertilizer treatment.</p>
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<p>Heat map of correlations among traits of starch properties, grain filling and rice qualities. For analysis of variance, *, **, and *** significant differences at <span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">p</span> &lt; 0.01, and <span class="html-italic">p</span> &lt; 0.001, respectively.</p>
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<p>Principal component analysis (PCA) of traits of starch properties, grain filling and rice qualities. Am, amylose; Ap, amylopectin; <span class="html-italic">T</span><sub>O</sub>, onset temperature; <span class="html-italic">T</span><sub>P</sub>, peak temperature; <span class="html-italic">T</span><sub>C</sub>, conclusion temperature; Δ<span class="html-italic">H</span><sub>gel</sub>, gelatinization enthalpy; G<sub>max</sub>, maximum grain-filling rate; G<sub>mean</sub>, mean grain-filling rate; D, active grain-filling period.</p>
Full article ">Figure 9
<p>Daily maximum and minimum temperature (<b>A</b>,<b>C</b>), sunshine hours (<b>B</b>,<b>D</b>), and precipitation (A and C) during the rice growth stage in 2021 (<b>A</b>,<b>B</b>) and 2022 (<b>C</b>,<b>D</b>).</p>
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18 pages, 3594 KiB  
Systematic Review
Rice Bran Consumption Improves Lipid Profiles: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
by Soo-yeon Park, Yehyeon Kim, Min Ju Park and Ji Yeon Kim
Nutrients 2025, 17(1), 114; https://doi.org/10.3390/nu17010114 - 30 Dec 2024
Viewed by 330
Abstract
Background: Dyslipidemia, a leading risk factor for cardiovascular diseases (CVDs), significantly contributes to global morbidity and mortality. Rice bran, rich in bioactive compounds such as γ-oryzanol and tocotrienols, has demonstrated promising lipid-modulating effects. Objective: This meta-analysis aimed to evaluate the effects of rice [...] Read more.
Background: Dyslipidemia, a leading risk factor for cardiovascular diseases (CVDs), significantly contributes to global morbidity and mortality. Rice bran, rich in bioactive compounds such as γ-oryzanol and tocotrienols, has demonstrated promising lipid-modulating effects. Objective: This meta-analysis aimed to evaluate the effects of rice bran on lipid profiles, including triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C), and identify factors influencing its efficacy across different populations and intervention conditions. Methods: A systematic search of PubMed, Web of Science, and Scopus was conducted to identify randomized controlled trials (RCTs) published up to November 2024. Effect sizes were calculated as mean differences with 95% confidence intervals (CIs). Subgroup analyses were performed based on intervention form, dosage, duration, region, and participant characteristics. Heterogeneity was estimated by the I2 statistic, and sensitivity analyses were conducted to assess the robustness of the findings. Results: Eleven RCTs involving 572 participants met the inclusion criteria. Pooled results showed that rice bran consumption significantly reduced TG (−15.13 mg/dL; 95% CI: −29.56, −0.71), TC (−11.80 mg/dL; 95% CI: −19.35, −4.25), and LDL-C (−15.11 mg/dL; 95% CI: −24.56, −5.66) with moderate heterogeneity (I2 = 38.1–63.0%). No significant changes were observed for HDL-C. Subgroup analyses showed that rice bran oil had greater effects on TC and LDL-C than whole rice bran. High-dose interventions (≥30 g/mL) and longer durations (>4 weeks) yielded stronger effects. Asian populations demonstrated greater reductions compared to Western populations. Conclusion: Rice bran, especially in the form of rice bran oil, significantly improves lipid profiles, supporting its role as a functional food for CVD prevention. Future research should focus on long-term studies with diverse populations to confirm its efficacy and explore underlying mechanisms. Full article
(This article belongs to the Section Carbohydrates)
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<p>PRISMA flow diagram for literature search and study selection.</p>
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<p>Risk of bias assessment for each included study [<a href="#B27-nutrients-17-00114" class="html-bibr">27</a>,<a href="#B28-nutrients-17-00114" class="html-bibr">28</a>,<a href="#B29-nutrients-17-00114" class="html-bibr">29</a>,<a href="#B30-nutrients-17-00114" class="html-bibr">30</a>,<a href="#B31-nutrients-17-00114" class="html-bibr">31</a>,<a href="#B32-nutrients-17-00114" class="html-bibr">32</a>,<a href="#B33-nutrients-17-00114" class="html-bibr">33</a>,<a href="#B34-nutrients-17-00114" class="html-bibr">34</a>,<a href="#B35-nutrients-17-00114" class="html-bibr">35</a>,<a href="#B36-nutrients-17-00114" class="html-bibr">36</a>,<a href="#B37-nutrients-17-00114" class="html-bibr">37</a>,<a href="#B38-nutrients-17-00114" class="html-bibr">38</a>,<a href="#B39-nutrients-17-00114" class="html-bibr">39</a>,<a href="#B40-nutrients-17-00114" class="html-bibr">40</a>,<a href="#B41-nutrients-17-00114" class="html-bibr">41</a>,<a href="#B42-nutrients-17-00114" class="html-bibr">42</a>,<a href="#B43-nutrients-17-00114" class="html-bibr">43</a>,<a href="#B44-nutrients-17-00114" class="html-bibr">44</a>,<a href="#B45-nutrients-17-00114" class="html-bibr">45</a>,<a href="#B46-nutrients-17-00114" class="html-bibr">46</a>,<a href="#B47-nutrients-17-00114" class="html-bibr">47</a>,<a href="#B48-nutrients-17-00114" class="html-bibr">48</a>,<a href="#B49-nutrients-17-00114" class="html-bibr">49</a>].</p>
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<p>Forest plots of the effect of rice bran on (<b>a</b>) triglycerides (TGs), (<b>b</b>) total cholesterol (TC), (<b>c</b>) low-density lipoprotein cholesterol (LDL-C), and (<b>d</b>) high-density lipoprotein cholesterol (HDL-C). Each square represents the point estimate of an individual study, with its size reflecting the study’s weight in the meta-analysis (based on sample size and precision). Squares are shown with 95% confidence intervals (CIs), and the diamond represents the pooled mean difference with its 95% CI. <sup>a, b, c</sup> represent rice bran containing different levels of gamma-oryzanol: a = 4000 ppm, b = 8000 ppm, c = 11000 ppm. [<a href="#B27-nutrients-17-00114" class="html-bibr">27</a>,<a href="#B28-nutrients-17-00114" class="html-bibr">28</a>,<a href="#B29-nutrients-17-00114" class="html-bibr">29</a>,<a href="#B30-nutrients-17-00114" class="html-bibr">30</a>,<a href="#B31-nutrients-17-00114" class="html-bibr">31</a>,<a href="#B32-nutrients-17-00114" class="html-bibr">32</a>,<a href="#B33-nutrients-17-00114" class="html-bibr">33</a>,<a href="#B34-nutrients-17-00114" class="html-bibr">34</a>,<a href="#B35-nutrients-17-00114" class="html-bibr">35</a>,<a href="#B36-nutrients-17-00114" class="html-bibr">36</a>,<a href="#B37-nutrients-17-00114" class="html-bibr">37</a>].</p>
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<p>Forest plots of the effect of rice bran on (<b>a</b>) triglycerides (TGs), (<b>b</b>) total cholesterol (TC), (<b>c</b>) low-density lipoprotein cholesterol (LDL-C), and (<b>d</b>) high-density lipoprotein cholesterol (HDL-C). Each square represents the point estimate of an individual study, with its size reflecting the study’s weight in the meta-analysis (based on sample size and precision). Squares are shown with 95% confidence intervals (CIs), and the diamond represents the pooled mean difference with its 95% CI. <sup>a, b, c</sup> represent rice bran containing different levels of gamma-oryzanol: a = 4000 ppm, b = 8000 ppm, c = 11000 ppm. [<a href="#B27-nutrients-17-00114" class="html-bibr">27</a>,<a href="#B28-nutrients-17-00114" class="html-bibr">28</a>,<a href="#B29-nutrients-17-00114" class="html-bibr">29</a>,<a href="#B30-nutrients-17-00114" class="html-bibr">30</a>,<a href="#B31-nutrients-17-00114" class="html-bibr">31</a>,<a href="#B32-nutrients-17-00114" class="html-bibr">32</a>,<a href="#B33-nutrients-17-00114" class="html-bibr">33</a>,<a href="#B34-nutrients-17-00114" class="html-bibr">34</a>,<a href="#B35-nutrients-17-00114" class="html-bibr">35</a>,<a href="#B36-nutrients-17-00114" class="html-bibr">36</a>,<a href="#B37-nutrients-17-00114" class="html-bibr">37</a>].</p>
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<p>Funnel plots of studies included in this meta-analysis showing the effects of rice bran intake on (<b>a</b>) triglycerides (TGs), (<b>b</b>) total cholesterol (TC), (<b>c</b>) low-density lipoprotein cholesterol (LDL-C), and (<b>d</b>) high-density lipoprotein cholesterol (HDL-C). Funnel plot of standard error (y-axis) versus standardized mean difference in means (x-axis). Each circle represents an individual study included in the meta-analysis.</p>
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<p>Funnel plots of studies included in this meta-analysis showing the effects of rice bran intake on (<b>a</b>) triglycerides (TGs), (<b>b</b>) total cholesterol (TC), (<b>c</b>) low-density lipoprotein cholesterol (LDL-C), and (<b>d</b>) high-density lipoprotein cholesterol (HDL-C). Funnel plot of standard error (y-axis) versus standardized mean difference in means (x-axis). Each circle represents an individual study included in the meta-analysis.</p>
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19 pages, 6476 KiB  
Article
Molecular Profiling for Blast Resistance Genes Pita2 and Pi2/Pi9 in Some Rice (Oryza sativa L.) Accessions and Selected Crosses
by Walaa M. Essa, Nesreen N. Bassuony, Abed El-aziz Tahoon, Abeer M. Mosalam and József Zsembeli
Agriculture 2025, 15(1), 61; https://doi.org/10.3390/agriculture15010061 - 29 Dec 2024
Viewed by 310
Abstract
Identifying major blast resistance genes in Oryza sativa L. genotypes is key to enhancing and maintaining the resistance. Observing rice varieties with durable resistance to blast has become a potential target in rice breeding programs. In this study, an evaluation was conducted during [...] Read more.
Identifying major blast resistance genes in Oryza sativa L. genotypes is key to enhancing and maintaining the resistance. Observing rice varieties with durable resistance to blast has become a potential target in rice breeding programs. In this study, an evaluation was conducted during 2020 and 2021 on ten Egyptian and introduced varieties. First, a field experiment was conducted in a randomized complete block design with three replications, and it was found that the Egyptian cultivar Sakha 101 had the highest crop grain yields (53.27 g). The Spanish varieties Hispagrán and Puebla were the earliest (110 and 108 days, respectively) as well as the highest in 1000-grain yield, giving them priority as donors for these traits; however, they had the lowest mean values in the number of panicles. Second, these cultivars were subjected to eighteen isolates from five strains of Pyricularia oryzae (IH, IC, ID, IE, and II). The Egyptian varieties Giza 177, Giza 179, Sakha 106, Giza 182, GZ1368-5-5-4, and GZ6296 were 100% resistant, while Hispagrán’s resistance was 16.6%, followed by Sakha 101 with 27.8%. To gain insight into the ten varieties, we used STS, SCAR, and CAPS markers to detect and mine alleles for major blast broad-spectrum resistance genes Pi2, Pi9, and Pita2. In the context of considering gene pyramiding as an effective strategy for achieving broad durable spectrum resistance to blast, molecular profiling was also conducted on eighteen F2 single plants obtained from the hybridization of Giza 177 (resistant) × Puebla (susceptible) varieties. Also, eighteen F2 single plants were obtained from Giza 177 × Hispagrán (highly susceptible) varieties. Conducting a molecular scan with STS dominant marker YL153/YL154 was performed on ten cultivars to detect the presence of the Pita2 gene, which conferred a unique band in Puebla. By doing a scan of the 18 second-generation plants derived from Giza 177 × Puebla, 11 individual plants of the 18 plants obtained a band, which was transferred from Puebla. F2 plants obtained from Giza 177 × Puebla amplified with CAPS marker RG64-431/RG64-432 had higher numbers of Pi2 alleles, while F2 plants of Giza 177 × Hispagrán cross-amplified with SCAR marker linked to Pi9 exceeded their parents more. Our results have revealed that molecular markers played an essential role in determining the direction of evolution for blast resistance traits. Full article
(This article belongs to the Section Crop Genetics, Genomics and Breeding)
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<p>Races virulence percentage: (1) IH, (2) ID, (3) IC, (4) IE, (5) II.</p>
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<p>Blast resistance percentage of the ten rice genotypes. (1) Giza 177, (2) Giza 179, (3) Giza 182, (4) Sakha 101, (5) Sakha 104, (6) Sakha 106, (7) GZ1368-5-5-4, (8) GZ6296-12-1-2-1-1, (9) Puebla, (10) Hispagrán.</p>
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<p>Leaf blast lesions on some varieties.</p>
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<p>Screening of the ten rice genotypes under study using the dominant STS marker YL155/YL87 for Pita2; (1) Giza 177, (2) Giza 179, (3) Giza 182, (4) Sakha 101, (5) Sakha 104, (6) Sakha 106, (7) GZ1368-5-5-4, (8) GZ6296-12-1-2-1-1, (9) Puebla, (10) Hispagrán, (M) ferments as 1000 bp ladder.</p>
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<p>Screening of the eighteen F2 individual plants of the cross Puebla × Giza 177 using the dominant STS marker YL155/YL87.</p>
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<p>Screening of the ten rice genotypes under study using SCAR marker RG64-431/RG64-432 closely linked to Pi2 gene; (1) Giza 177, (2) Giza 179, (3) Giza 182, (4) Sakha 101, (5) Sakha 104, (6) Sakha 106, (7) GZ1368-5-5-4, (8) GZ6296-12-1-2-1-1, (9) Puebla, (10) Hispagrán, (M) ferments as 50 bp ladder.</p>
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<p>Screening of the eighteen F2 individual plants of the cross Puebla × Giza 177 using CAPS marker RG64-431/RG64-432.</p>
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<p>Screening of the eighteen F2 individual plants of the cross Hispagrán × Giza 177 using CAPS marker RG64-431/RG64-432.</p>
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<p>Screening of the ten rice genotypes under study using SCAR marker PBA12; (1) Giza 177, (2) Giza 179, (3) Giza 182, (4) Sakha 101, (5) Sakha 104, (6) Sakha106, (7) GZ1368-5-5-4, (8) GZ6296-12-1-2-1-1, (9) Puebla, (10) Hispagrán, (M) ferments as 50 bp ladder.</p>
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<p>Screening of the eighteen F2 individual plants of the cross Puebla × Giza 177 using SCAR marker PBA12.</p>
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<p>Screening of the eighteen F2 individual plants of the cross Hispagrán × Giza 177 using SCAR marker PBA12.</p>
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6 pages, 956 KiB  
Communication
OsBBX2 Delays Flowering by Repressing Hd3a Expression Under Long-Day Conditions in Rice
by Yusi Yang, Jiaming Wei, Xiaojie Tian, Changhua Liu, Xiufeng Li and Qingyun Bu
Plants 2025, 14(1), 48; https://doi.org/10.3390/plants14010048 - 27 Dec 2024
Viewed by 243
Abstract
Members of the B-Box (BBX) family of proteins play crucial roles in the growth and development of rice. Here, we identified a rice BBX protein, Oryza sativa BBX2 (OsBBX2), which exhibits the highest expression in the root. The transcription of OsBBX2 follows a [...] Read more.
Members of the B-Box (BBX) family of proteins play crucial roles in the growth and development of rice. Here, we identified a rice BBX protein, Oryza sativa BBX2 (OsBBX2), which exhibits the highest expression in the root. The transcription of OsBBX2 follows a diurnal rhythm under photoperiodic conditions, peaking at dawn. Functional analysis revealed that OsBBX2 possesses transcriptional repression activity. The BBX2 was overexpressed in the rice japonica cultivar Longjing 11 (LJ11), in which Ghd7 and PRR37 were non-functional or exhibited weak functionality. The overexpression of OsBBX2 (OsBBX2 OE) resulted in a delayed heading date under a long-day (LD) condition, whereas the bbx2 mutant exhibited flowering patterns similar to the wild type (WT). Additionally, transcripts of Ehd1, Hd3a, and RFT1 were downregulated in the OsBBX2 OE lines under the LD condition. OsBBX2 interacted with Hd1 (BBX18), and the bbx2 hd1 double mutant displayed a late flowering phenotype comparable to that of hd1. Furthermore, OsBBX2 enhanced the transcriptional repression of Hd3a through its interaction with Hd1, as demonstrated in the protoplast-based assay. Taken together, these findings suggest that the OsBBX2 delays flowering by interacting with Hd1 and co-repressing Hd3a transcription. Full article
(This article belongs to the Special Issue Crop Functional Genomics and Biological Breeding)
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<p><span class="html-italic">OsBBX2</span> delays flowering by repressing <span class="html-italic">Hd3a</span> expression. (<b>A</b>) Representative image of LJ11 and <span class="html-italic">BBX OE</span> plants grown under LD conditions at the heading stage. (<b>B</b>) Flowering time of LJ11 and <span class="html-italic">BBX OE</span> under LD conditions. Data are means ± standard error (SE; <span class="html-italic">n</span> = 20). <span class="html-italic">p</span> values were calculated by Student’s <span class="html-italic">t</span> test compared to LJ11; **: <span class="html-italic">p</span> &lt; 0.01. (<b>C</b>) Schematic diagrams of the reporter plasmids used in the rice protoplast transient assay. REN, Renilla luciferase; LUC, firefly luciferase. (<b>D</b>) The LUC activity in rice protoplasts with indicated reporter plasmids. Data are means ± SE (<span class="html-italic">n</span> = 3). Statistically significant differences are indicated by different lowercase letters (<span class="html-italic">p</span> &lt; 0.05, one-way ANOVA with Tukey’s significant difference test). (<b>E</b>) The yeast two-hybrid assay showed that BBX2 interacts with Hd1. Yeast grew at 30 °C for 3 days. Empty vectors were used as the negative controls. AD, activation domain. BD, binding domain. (<b>F</b>) The LCI assay of the <span class="html-italic">BBX2</span> interaction with <span class="html-italic">Hd1</span> in <span class="html-italic">N. benthamiana</span> leaves. The co-transformation of cLUC-<span class="html-italic">Hd1</span> and nLUC-<span class="html-italic">BBX2</span> led to the re-constitution of the LUC signal, whereas no signal was detected when cLUC-<span class="html-italic">Hd1</span> and nLUC, cLUC and nLUC-<span class="html-italic">BBX2</span>, and cLUC and nLUC were co-expressed. In each experiment, at least five independent <span class="html-italic">N. benthamiana</span> leaves were infiltrated and analyzed. (<b>G</b>–<b>I</b>) A representative image of LJ11, <span class="html-italic">bbx2</span> (<b>G</b>), <span class="html-italic">hd1</span> (<b>H</b>), and <span class="html-italic">bbx2 hd1</span> (<b>I</b>) mutants grown under the LD condition at the heading stage. (<b>J</b>) The flowering time of LJ11, <span class="html-italic">bbx2</span>, <span class="html-italic">hd1</span>, and <span class="html-italic">bbx2 hd1</span> mutants under LD conditions. Data are means ± standard error (SE; <span class="html-italic">n</span> = 20). Statistically significant differences are indicated by different lowercase letters (<span class="html-italic">p</span> &lt; 0.05, one-way ANOVA with Tukey’s significant difference test). (<b>K</b>) Schematic diagrams of the reporter plasmids used in the rice protoplast transient assay. <span class="html-italic">35S<sub>Pro</sub>:GFP</span> was used as the control and <span class="html-italic">35S<sub>Pro</sub>:BBX2</span>, <span class="html-italic">35S<sub>Pro</sub>:Hd1</span>, and <span class="html-italic">Hd3a<sub>Pro</sub>:LUC</span> were used as the effectors and reporters. (<b>L</b>) Relative LUC activity expressed with reporters and effectors. The expression level of Renilla (REN) was used as an internal control. The LUC/REN ratio represents the relative activity of the <span class="html-italic">Hd3a</span> promoter. Data are shown as means ± SE (<span class="html-italic">n</span> = 3). Statistically significant differences are indicated by different lowercase letters (<span class="html-italic">p</span> &lt; 0.05, one-way ANOVA with Tukey’s significant difference test).</p>
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12 pages, 1974 KiB  
Article
The Effects of Polystyrene Microplastics and Copper Ion Co-Contamination on the Growth of Rice Seedlings
by Huiyu Jin, Guohe Lin, Mingzi Ma, Lin Wang and Lixiang Liu
Nanomaterials 2025, 15(1), 17; https://doi.org/10.3390/nano15010017 - 26 Dec 2024
Viewed by 315
Abstract
Microplastics (MPs) are emerging pollutants of global concern, while heavy metals such as copper ions (Cu2+) are longstanding environmental contaminants with well-documented toxicity. This study investigates the independent and combined effects of polystyrene microplastics (PS-MPs) and Cu on the physiological and [...] Read more.
Microplastics (MPs) are emerging pollutants of global concern, while heavy metals such as copper ions (Cu2+) are longstanding environmental contaminants with well-documented toxicity. This study investigates the independent and combined effects of polystyrene microplastics (PS-MPs) and Cu on the physiological and biochemical responses of rice seedlings (Oryza sativa L.), a key staple crop. Hydroponic experiments were conducted under four treatment conditions: control (CK), PS-MPs (50 mg·L−1), Cu (20 mg·L−1 Cu2+), and a combined PS-MPs + Cu treatment. The results showed that PS-MPs had a slight stimulatory effect on root elongation, while Cu exposure mildly inhibited root growth. However, the combined treatment (PS-MPs + Cu) demonstrated no significant synergistic or additive toxicity on growth parameters such as root, stem, and leaf lengths or biomass (fresh and dry weights). Both PS-MPs and Cu significantly reduced peroxidase (POD) activity in root, stem, and leaf, indicating oxidative stress and disrupted antioxidant defenses. Notably, in the combined treatment, PS-MPs mitigated Cu toxicity by adsorbing Cu2+ ions, reducing their bioavailability, and limiting Cu accumulation in rice tissues. These findings reveal a complex interaction between MPs and heavy metals in agricultural systems. While PS-MPs can alleviate Cu toxicity by reducing its bioavailability, they also compromise antioxidant activity, potentially affecting plant resilience to stress. This study provides a foundation for understanding the combined effects of MPs and heavy metals, emphasizing the need for further research into their long-term environmental and agronomic impacts. Full article
(This article belongs to the Section Environmental Nanoscience and Nanotechnology)
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<p>Effect of single and combined treatment of PS-MP and Cu<sup>2+</sup> on root length of rice seedlings. The letter of a indicates insignificant differences between treatments.</p>
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<p>Effect of single and combined treatment of PS-MP and Cu<sup>2+</sup> on shoot length of rice seedlings. The letter of a indicates insignificant differences between treatments.</p>
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<p>Effect of single and combined treatment of PS-MP and Cu<sup>2+</sup> on fresh weight of rice seedlings. The letter of a indicates insignificant differences between treatments.</p>
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<p>Effect of single and combined treatment of PS-MP and Cu<sup>2+</sup> on dry weight of rice seedlings. The letter of a indicates insignificant differences between treatments.</p>
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<p>Effect of single and combined treatment of PS-MP and Cu<sup>2+</sup> on root POD activity of rice seedlings. Different letters a, b, c and d represent significant difference between different treatments (at <span class="html-italic">p</span> ≤ 0.05) whereas, same letters indicate insignificant differences between treatments.</p>
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<p>Effect of single and combined treatment of PS-MP and Cu<sup>2+</sup> on shoot POD activity of rice seedlings. Different letters a, b and c represent significant difference between different treatments (at <span class="html-italic">p</span> ≤ 0.05) whereas, same letters indicate insignificant differences between treatments.</p>
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<p>Effect of single and combined treatment of PS-MP and Cu<sup>2+</sup> on Cu content of rice seedlings. Different letters a, b and c represent significant difference between different treatments (at <span class="html-italic">p</span> ≤ 0.05) whereas, same letters indicate insignificant differences between treatments.</p>
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17 pages, 2980 KiB  
Article
Mapping and Validation of Quantitative Trait Loci on Yield-Related Traits Using Bi-Parental Recombinant Inbred Lines and Reciprocal Single-Segment Substitution Lines in Rice (Oryza sativa L.)
by Ghulam Ali Manzoor, Changbin Yin, Luyan Zhang and Jiankang Wang
Plants 2025, 14(1), 43; https://doi.org/10.3390/plants14010043 - 26 Dec 2024
Viewed by 286
Abstract
Yield-related traits have higher heritability and lower genotype-by-environment interaction, making them more suitable for genetic studies in comparison with the yield per se. Different populations have been developed and employed in QTL mapping; however, the use of reciprocal SSSLs is limited. In this [...] Read more.
Yield-related traits have higher heritability and lower genotype-by-environment interaction, making them more suitable for genetic studies in comparison with the yield per se. Different populations have been developed and employed in QTL mapping; however, the use of reciprocal SSSLs is limited. In this study, three kinds of bi-parental populations were used to investigate the stable and novel QTLs on six yield-related traits, i.e., plant height (PH), heading date (HD), thousand-grain weight (TGW), effective tiller number (ETN), number of spikelets per panicle (NSP), and seed set percentage (SS). Two parental lines, i.e., japonica Asominori and indica IR24, their recombinant inbred lines (RILs), and reciprocal single-segment substitution lines (SSSLs), i.e., AIS and IAS, were genotyped by SSR markers and phenotyped in four environments with two replications. Broad-sense heritability of the six traits ranged from 0.67 to 0.94, indicating their suitability for QTL mapping. In the RIL population, 18 stable QTLs were identified for the six traits, 4 for PH, 6 for HD, 5 for TGW, and 1 each for ETN, NSP, and SS. Eight of them were validated by the AIS and IAS populations. The results indicated that the allele from IR24 increased PH, and the alternative allele from Asominori reduced PH at qPH3-1. AIS18, AIS19, and AIS20 were identified to be the donor parents which can be used to increase PH in japonica rice; on the other hand, IAS14 and IAS15 can be used to reduce PH in indica rice. The allele from IR24 delayed HD, and the alternative allele reduced HD at qHD3-1. AIS14 and AIS15 were identified to be the donor parents which can be used to delay HD in japonica rice; IAS13 and IAS14 can be used to reduce HD in indica rice. Reciprocal SSSLs not only are the ideal genetic materials for QTL validation, but also provide the opportunity for fine mapping and gene cloning of the validated QTLs. Full article
(This article belongs to the Special Issue Genetic Analysis of Quantitative Traits in Plants)
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<p>Frequency distributions of six yield-related traits in the rice (<span class="html-italic">Oryza sativa</span> L.) bi-parental RIL population. The two parents are represented by symbols IR24 and ASO at the top of each histogram. The six traits are denoted by PH: plant height; HD: heading date; TGW: thousand-grain weight; ETN: effective tiller number; NSP: number of spikelets per panicle; SS: seed set percentage. The four environments and BLUE values are denoted by GL: Guilin; GY: Guiyang: NC: Nanchang; NJ: Nanjing; and BL: BLUE.</p>
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<p>Validation of two QTLs on chromosome 3, one for PH and one for HD, i.e., <span class="html-italic">qPH3-1</span> (<b>A</b>) and <span class="html-italic">qHD3-1</span> (<b>B</b>), by reciprocal SSSLs. Indicated on the left side are phenotypic values; on the right side are marker genotypes. Chromosomal segments of Asominori are denoted by 0, and IR24 denoted by 2.</p>
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<p>The development diagram of the RIL and reciprocal SSSL populations from the same two parents.</p>
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18 pages, 1983 KiB  
Article
Diversity of Mycotoxins in Stored Paddy Rice: Contamination Patterns in the Mekong Delta, Vietnam
by Lien Thi Kim Phan, Thuy Thi Ngoc Nguyen, Thien Thi Thanh Tran and Sarah De Saeger
Toxins 2025, 17(1), 6; https://doi.org/10.3390/toxins17010006 - 26 Dec 2024
Viewed by 422
Abstract
Rice (Oryza sativa L.) is the most important food in Vietnam. However, rice is often lost in post-harvest due to fungal growth and mycotoxins contamination. This study aimed to evaluate mycotoxin contamination in stored paddy rice collected in 2018, 2019, and 2022 [...] Read more.
Rice (Oryza sativa L.) is the most important food in Vietnam. However, rice is often lost in post-harvest due to fungal growth and mycotoxins contamination. This study aimed to evaluate mycotoxin contamination in stored paddy rice collected in 2018, 2019, and 2022 in six provinces in Mekong Delta, Vietnam, using LC-MS/MS. The results revealed that 47% of the samples were contaminated with 12 types of mycotoxins. The prevalence of these mycotoxins was 30% (ZEN), 10% (FUS/MON), 6% (BEA/AFB2), 2–4% (AFG1, AFB1, AFG2), 2% (FB1), and 1% (OTA/AME/ENB). Among the provinces, stored paddy rice from Kien Giang had the highest contamination, followed by Ben Tre, Long An, An Giang, Dong Thap, and Can Tho. Remarkably, paddy rice collected in 2022 was usually contaminated with emerging mycotoxins with a higher incidence of co-occurrence ranging from 2–6% of the samples. Additionally, five stored paddy rice samples were contaminated with levels of AFB1, OTA, and ZEN exceeding Vietnamese regulatory limits for unprocessed rice. Our findings provide valuable insights into mycotoxin contamination across different years and growing regions in the Mekong Delta, Vietnam. This study could give essential information to stakeholders, including policy-makers or food safety authorities, etc., to inform strategies to mitigate these toxins in the near future and underscores the importance of monitoring rice production. Full article
(This article belongs to the Section Mycotoxins)
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<p>Prevalence (%) and mycotoxins concentration (µg/kg) in stored paddy rice in 2018 (AFB1: Aflatoxin B1; AFB2: Aflatoxin B2; AFG2: Aflatoxin G2; FB1: Fumonisin B1).</p>
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<p>Prevalence (%) and mycotoxins concentration (µg/kg) in stored paddy rice in 2019 (AFB1: Aflatoxin B1; AFB2: Aflatoxin B2; OTA: Ochratoxin A; AME: Alternariol Monomethyl Ether).</p>
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<p>The prevalence (%) and mycotoxins concentration (µg/kg) in stored paddy rice in 2022 (ZEN: Zearalenone; FUS: Fusaric acid; FB1: Fumonisin B1; BEA: Beauvericin; MON: Moniliformin; ENB: Enniatin B).</p>
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<p>The prevalence (%) of mycotoxin contaminations in stored paddy rice collected in different regions in Mekong Delta, Vietnam. (AFG1: Aflatoxin G1; AFG2: Aflatoxin G2; AFB1: Aflatoxin B1; AFB2: Aflatoxin B2; OTA: Ochratoxin A; FB1: Fumonisin B1; AME: Alternariol Monomethyl Ether).</p>
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<p>Sampling locations–Dong Thap, An Giang, Can Tho, Kien Giang, Long An, and Ben Tre provinces–in Mekong Delta, Vietnam.</p>
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14 pages, 396 KiB  
Article
Assessment of Optimal Seeding Rate for Fine and Coarse Rice Varieties Using the Direct Seeded Rice (DSR) Method
by Atif Naeem, Madad Ali, Ahmad Jawad, Asif Ameen, Mehwish, Talha Liaqat, Samreen Nazeer, Muhammad Zubair Akram and Shahbaz Hussain
Seeds 2025, 4(1), 1; https://doi.org/10.3390/seeds4010001 - 26 Dec 2024
Viewed by 358
Abstract
Rice (Oryza sativa L.) is one of the most crucial cereal crops worldwide, serving as a staple food for a significant portion of the global population. Rice is the second most important staple food crop in Pakistan after wheat, and it is [...] Read more.
Rice (Oryza sativa L.) is one of the most crucial cereal crops worldwide, serving as a staple food for a significant portion of the global population. Rice is the second most important staple food crop in Pakistan after wheat, and it is also a major export commodity. Concerning this, the current study aimed to evaluate the effects of different seed rates on the yield and yield-contributing parameters of rice varieties. The experiment was conducted over two consecutive kharif summer seasons, from 2020–21 and 2021–22, at the Pakistan Agricultural Research Council (PARC) Rice Program experimental area in Kala Shah Kaku, Lahore, Pakistan, by following a factorial randomized complete block design with three replications using coarse rice (KSK-133) and fine rice (Super Basmati) varieties. Different seed rates, including 27 kg/ha, 22 kg/ha, 17 kg/ha, and 12 kg/ha, were tested during the experiment. Different growth and yield-related attributes, such as plant height (cm), the number of productive tillers per plant, panicle length (cm), the number of grains per panicle, and grain yield (m−2), were recorded. The results showed that for KSK-133 and Super Basmati, the maximum grain yield was achieved at a sowing rate of 27 kg/ha in direct seed rice (DSR). The lowest yield was observed at a seeding rate of 12 kg/ha for KSK-133 and Super Basmati in DSR. Both basmati (Super Basmati) and coarse-grain (KSK-133) varieties exhibited similar responses to seed rate treatments, with the optimal performance observed at the highest seed rate of 27 kg/ha for both seasons. Grains per panicle and thousand grain weight emerged as critical determinants of yield, highlighting the need to balance vegetative growth with reproductive development. Breeding programs should focus on developing varieties that balance vegetative traits like tiller production and panicle length with reproductive traits to enhance overall yield. Based on these findings, it is concluded that using an optimal seeding rate of 27 kg/ha for direct-seeded fine and coarse rice varieties is beneficial in terms of tillers and higher yield. Full article
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<p>Pearson’s Correlation: (<b>a</b>) Pearson’s Correlation among yield contributing traits during season 2020–21; (<b>b</b>) Pearson’s Correlation among yield contributing traits during season 2021–22.</p>
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20 pages, 5163 KiB  
Article
Evaluating Photosynthetic Light Response Models for Leaf Photosynthetic Traits in Paddy Rice (Oryza sativa L.) Under Field Conditions
by Xinfeng Yao, Huifeng Sun, Sheng Zhou and Linyi Li
Plants 2025, 14(1), 23; https://doi.org/10.3390/plants14010023 - 25 Dec 2024
Viewed by 246
Abstract
Accurate photosynthetic parameters obtained from photosynthetic light-response curves (LRCs) are crucial for enhancing our comprehension of plant photosynthesis. However, the task of fitting LRCs is still demanding due to diverse variations in LRCs under different environmental conditions, as previous models were evaluated based [...] Read more.
Accurate photosynthetic parameters obtained from photosynthetic light-response curves (LRCs) are crucial for enhancing our comprehension of plant photosynthesis. However, the task of fitting LRCs is still demanding due to diverse variations in LRCs under different environmental conditions, as previous models were evaluated based on a limited number of leaf traits and a small number of LRCs. This study aimed to compare the performance of nine LRC models in fitting a set of 108 LRCs measured from paddy rice (Oryza sativa L.) grown in field across 3 years under different leaf positions, leaf ages, nitrogen levels, irrigation levels, and varieties. The shape of 108 LRCs varies significantly under a range of leaf traits, which can be typed into three leaf light-acclimation types—high-light leaves (HL-1 and HL-2), and low-light leaves (LL). The accuracy of these models was evaluated by (1) LRCs from three acclimation types: HL-1 and HL-2, and LL; and (2) LRCs across three irradiance stages: light-limited, light-saturated, and photoinhibition. Results indicate that the Ye model emerged as the top performance among the nine models, particularly in the photoinhibition stage of LL leaves, with median values of R2, SSE, and AIC of 0.99, 2.39, and −14.03, respectively. Furthermore, the Ye model produced the most accurate predictions of key photosynthetic parameters, including dark respiration (RD), light-compensation point (Icomp), maximum net photosynthetic rate (PNmax), and light-saturation point (Isat). Results also suggest that PNImax and Imax were the most appropriate parameters to describe photosynthetic activity at the light-saturation point. These findings have significant implications for improving the accuracy of fitting LRCs, and thus robust predictions of photosynthetic parameters in rice under different environmental conditions. Full article
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<p>Box plots of observed values for <span class="html-italic">P<sub>Nmax</sub></span> (<b>a</b>), <span class="html-italic">I<sub>sat</sub></span> (<b>b</b>), <span class="html-italic">I<sub>comp</sub></span> (<b>c</b>), and <span class="html-italic">R<sub>D</sub></span> (<b>d</b>) derived from the LRCs in the HL-1, HL-2, and LL types. The red circles indicate the observed values, while the blue box plot shows the middle 50% of the data (interquartile range, IQR), the lower and upper boundaries correspond to the first (Q1) and third quartiles (Q3), respectively, and the red line denotes the median. The lower and upper whiskers represent the minimum and maximum values within 1.5 times the IQR from Q1 and Q3, and any points beyond the whiskers are considered outliers.</p>
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<p>Comparisons of three representative LRCs from HL-1 (<b>a</b>), HL-2 (<b>b</b>), and LL (<b>c</b>) leaves fitted by the nine models.</p>
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<p>The <span class="html-italic">R<sup>2</sup></span> of the nine models for fitting LRCs involved in the HL-1 (<b>a</b>), HL-2 (<b>b</b>), and LL (<b>c</b>) and overall set (<b>d</b>). Red + indicate outliers, defined as values beyond the minimum and maximum values within 1.5 times the IQR from the first and third quartiles.</p>
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<p>The <span class="html-italic">SSE</span> of the nine models for fitting LRCs involved in the HL-1 (<b>a</b>), HL-2 (<b>b</b>), and LL (<b>c</b>) and overall set (<b>d</b>). Red + indicate outliers, defined as values beyond the minimum and maximum values within 1.5 times the IQR from the first and third quartiles.</p>
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<p>The <span class="html-italic">AIC</span> of the nine models for fitting LRCs involved in the HL-1 (<b>a</b>), HL-2 (<b>b</b>), and LL (<b>c</b>) and overall set (<b>d</b>). Red + indicate outliers, defined as values beyond the minimum and maximum values within 1.5 times the IQR from the first and third quartiles.</p>
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<p><span class="html-italic">SSE</span> values of the LRCs in the light-limited stage (0 and 200 μmol (photon) m<sup>–2</sup> s<sup>–1</sup>) across HL-1 (<b>a</b>), HL-2 (<b>b</b>), and LL (<b>c</b>) and overall set (<b>d</b>). Red + indicate outliers, defined as values beyond the minimum and maximum values within 1.5 times the IQR from the first and third quartiles.</p>
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<p><span class="html-italic">SSE</span> values of the LRCs in the light-saturated (200 to 1000 μmol (photon) m<sup>–2</sup> s<sup>–1</sup>) stage across HL-1 (<b>a</b>), HL-2 (<b>b</b>), and LL (<b>c</b>) and overall set (<b>d</b>). Red + indicate outliers, defined as values beyond the minimum and maximum values within 1.5 times the IQR from the first and third quartiles.</p>
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<p><span class="html-italic">SSE</span> values of the LRCs in the photoinhibition stage (1000 to 2000 μmol (photon) m<sup>–2</sup> s<sup>–1</sup>) across HL-1 (<b>a</b>), HL-2 (<b>b</b>), and LL (<b>c</b>) and overall set (<b>d</b>). Red + indicate outliers, defined as values beyond the minimum and maximum values within 1.5 times the IQR from the first and third quartiles.</p>
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<p>The box plots of <span class="html-italic">P<sub>Nmax</sub></span> values estimated from the nine LRC models compared across HL-1 (<b>a</b>), HL-2 (<b>b</b>), and LL (<b>c</b>) and the overall set (<b>d</b>). (OB. denotes the observation of <span class="html-italic">P<sub>Nmax</sub></span>). Red + indicate outliers, defined as values beyond the minimum and maximum values within 1.5 times the IQR from the first and third quartiles.</p>
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<p>The box plots of <span class="html-italic">P<sub>NImax</sub></span> values estimated from the nine LRC models compared across HL-1 (<b>a</b>), HL-2 (<b>b</b>), and LL (<b>c</b>) and the overall set (<b>d</b>) (OB. denotes the observation of <span class="html-italic">P<sub>Nmax</sub></span>).</p>
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<p>The box plots of <span class="html-italic">I<sub>max</sub></span> values estimated from the nine LRC models compared across HL-1 (<b>a</b>), HL-2 (<b>b</b>), and LL (<b>c</b>) and the overall set (<b>d</b>) (OB. denotes the observation of <span class="html-italic">I<sub>sat</sub></span>). Red + indicate outliers, defined as values beyond the minimum and maximum values within 1.5 times the IQR from the first and third quartiles.</p>
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<p>The box plots of <span class="html-italic">I<sub>comp</sub></span> values estimated from the nine LRC models compared across HL-1 (<b>a</b>), HL-2 (<b>b</b>), and LL (<b>c</b>) and the overall set (<b>d</b>) (OB. denotes the observation of <span class="html-italic">I<sub>comp</sub></span>). Red + indicate outliers, defined as values beyond the minimum and maximum values within 1.5 times the IQR from the first and third quartiles.</p>
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<p>The box plots of <span class="html-italic">R<sub>D</sub></span> values estimated from the nine LRC models compared across HL-1 (<b>a</b>), HL-2 (<b>b</b>), and LL (<b>c</b>) and the overall set (<b>d</b>) (OB. denotes the observation of <span class="html-italic">R<sub>D</sub></span>). Red + indicate outliers, defined as values beyond the minimum and maximum values within 1.5 times the IQR from the first and third quartiles.</p>
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15 pages, 3429 KiB  
Article
Receptor-like Kinase GOM1 Regulates Glume-Opening in Rice
by Xinhui Zhao, Mengyi Wei, Qianying Tang, Lei Tang, Jun Fu, Kai Wang, Yanbiao Zhou and Yuanzhu Yang
Plants 2025, 14(1), 5; https://doi.org/10.3390/plants14010005 - 24 Dec 2024
Viewed by 301
Abstract
Glume-opening of thermosensitive genic male sterile (TGMS) rice (Oryza sativa L.) lines after anthesis is a serious problem that significantly reduces the yield and quality of hybrid seeds. However, the molecular mechanisms regulating the opening and closing of rice glumes remain largely [...] Read more.
Glume-opening of thermosensitive genic male sterile (TGMS) rice (Oryza sativa L.) lines after anthesis is a serious problem that significantly reduces the yield and quality of hybrid seeds. However, the molecular mechanisms regulating the opening and closing of rice glumes remain largely unclear. In this study, we report the isolation and functional characterization of a glum-opening mutant after anthesis, named gom1. gom1 exhibits dysfunctional lodicules that lead to open glumes following anthesis. Map-based cloning and subsequent complementation tests confirmed that GOM1 encodes a receptor-like kinase (RLK). GOM1 was expressed in nearly all floral tissues, with the highest expression in the lodicule. Loss-of-function of GOM1 resulted in a decrease in the expression of genes related to JA biosynthesis, JA signaling, and sugar transport. Compared with LK638S, the JA content in the gom1 mutant was significantly reduced, while the soluble sugar, sucrose, glucose, and fructose contents were significantly increased in lodicules after anthesis. Together, we speculated that GOM1 regulates carbohydrate transport in lodicules during anthesis through JA and JA signaling, maintaining a higher osmolality in lodicules after anthesis, which leads to glum-opening. Full article
(This article belongs to the Section Crop Physiology and Crop Production)
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<p>Phenotypic comparison of the LK638S and <span class="html-italic">gom1</span> mutant. (<b>A</b>) Plant architecture of LK638S and <span class="html-italic">gom1</span> mutant plants. Scale bars, 20 cm. (<b>B</b>) Comparison of LK638S and <span class="html-italic">gom1</span> mutant panicles post-anthesis. Scale bars: 5 cm for panicles and 5 mm for glumes. (<b>C</b>,<b>D</b>) The <span class="html-italic">gom1</span> mutant set up malformed seeds within open glumes compared with LK638S. Scale bar, 5 mm. (<b>E</b>) Comparison of flowering habits between the LK638S and <span class="html-italic">gom1</span> mutant (<span class="html-italic">n</span> = 6). (<b>F</b>) Comparison of glume-opening in LK638S and <span class="html-italic">gom1</span> mutant following anthesis. Data are presented as mean ± SD (<span class="html-italic">n</span> = 3, ** <span class="html-italic">p</span> ≤ 0.01, Student’s <span class="html-italic">t</span>-test).</p>
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<p>Comparison of major agronomic traits between LK638S and the gom1 mutant in Changsha. (<b>A</b>) Phenotypes of LK638S and <span class="html-italic">gom1</span> mutant at the heading stage. Scale bar, 20 cm. (<b>B</b>–<b>E</b>) The major traits, including plant height (<b>B</b>), panicle length (<b>C</b>), tiller number (<b>D</b>), and grain number per panicle (<b>E</b>), are shown in histograms. In 2023, agronomic traits were examined in a paddy field located in Guanshan village (28°19′32″ N, 112°40′38″ E), Changsha. Data are presented as the mean ± SD (<span class="html-italic">n</span> = 20). ** indicates a significant difference (<span class="html-italic">p</span> &lt; 0.01 from Student’s <span class="html-italic">t</span>-test).</p>
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<p>The dynamic process of the glumes and lodicules in LK638S and <span class="html-italic">gom1</span> plants. (<b>A</b>) The dynamic process of the opening of the glumes and the size of the lodicules in LK638S and <span class="html-italic">gom1</span> plants at different stages of anthesis. BA2, 2 h before anthesis; A, anthesis; AA2, 2 h after anthesis. Scale bar: 2 mm. (<b>B</b>) The dynamic process occurring in the epidermal cells of the lodicules in LK638S and <span class="html-italic">gom1</span> plants at different stages of anthesis. Scale bar: 50 μm. (<b>C</b>) The average area of the epidemic cells of the lodicules in LK638S and <span class="html-italic">gom1</span> plants at different stages of anthesis. (<b>D</b>) Osmolality measurements of lodicules in LK638S and <span class="html-italic">gom1</span> plants at different stages of anthesis. Data are presented as the mean ± SD (<span class="html-italic">n</span> = 10). Lowercase letters indicate significant differences at <span class="html-italic">p</span> &lt; 0.05. Statistical significance was determined using the Student’s <span class="html-italic">t</span>-test.</p>
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<p>Map-based cloning of <span class="html-italic">gom1</span>. (<b>A</b>) The <span class="html-italic">GOM1</span> locus was mapped to chromosome 3 within a region of 287 kb. A 1 bp deletion in the seven exons led to a premature stop codon. The candidate open reading frame is highlighted in red. (<b>B</b>–<b>D</b>) Complementation tests rescued the <span class="html-italic">gom1</span> phenotypes. Whole-plant morphology (<b>B</b>), Panicle and spikelet morphology (<b>C</b>), and percentage of glume open following anthesis (<b>D</b>). Data are presented as mean ± SD (<span class="html-italic">n</span> = 3, ** <span class="html-italic">p</span> ≤ 0.01, Student’s <span class="html-italic">t</span>-test). (<b>E</b>–<b>G</b>) Knockout lines of GOM1 showed an open glume phenotype. Whole-plant morphology (<b>E</b>), panicle and spikelet morphology (<b>F</b>), and percentage of glume open following anthesis (<b>G</b>). Data are presented as mean ± SD (<span class="html-italic">n</span> = 3, ** <span class="html-italic">p</span> ≤ 0.01, Student’s <span class="html-italic">t</span>-test).</p>
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<p><span class="html-italic">GOM1</span> expression pattern in rice. (<b>A</b>–<b>J</b>) <span class="html-italic">GOM1</span> expression revealed by GUS staining in <span class="html-italic">GOM1</span> promoter-GUS transgenic plants. Root (<b>A</b>), stem (<b>B</b>), leaf (<b>C</b>), leaf sheath (<b>D</b>), and young spikelet at stage 4 to stage 8 (St4 to St8) (<b>E</b>–<b>I</b>) and a mature spikelet (<b>J</b>). Bar = 2 mm; (<b>K</b>) The expression level of <span class="html-italic">GOM1</span> in various tissues of Kitaake. (<b>L</b>) The expression level of <span class="html-italic">GOM1</span> in mature spikelet tissues of Kitaake. Data in (<b>K</b>–<b>L</b>) are presented as mean ± SD (<span class="html-italic">n</span> = 3).</p>
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<p>Transcriptomic analyses of lodicules from LK638S and <span class="html-italic">gom1</span> mutant. (<b>A</b>) Volcano plots comparing the transcriptomes of LK638S with the <span class="html-italic">gom1</span> mutant. <span class="html-italic">X</span>-axis and <span class="html-italic">Y</span>-axis represent log2 fold change (FC) and −log10 (<span class="html-italic">p</span>-value), respectively. The green dots represent downregulated DEGs, while the red dots indicate upregulated DEGs. The blue dots indicate no significant difference in transcriptomes. (<b>B</b>) GO enrichment analysis of DEGs with a cut-off value of <span class="html-italic">p</span> &lt; 0.05. Notably, genes involved in JA biosynthesis and signaling pathways, as well as carbohydrate transport (highlighted in red), were significantly enriched. (<b>C</b>) qRT-PCR analysis of the expression levels of JA synthesis and signal pathway genes at the AA2 stage. (<b>D</b>) JA content in lodicules of LK638S and <span class="html-italic">gom1</span>. (<b>E</b>) qRT-PCR analysis of expression levels of <span class="html-italic">OsSWEET</span> genes at the AA2 stage. (<b>F</b>–<b>I</b>) Soluble sugar (<b>F</b>), sucrose (<b>G</b>), glucose (<b>H</b>), and fructose (<b>I</b>) levels in lodicules of LK638S and <span class="html-italic">gom1</span> at different stages of anthesis. Data in (<b>C</b>–<b>I</b>) are presented as mean ± SD (<span class="html-italic">n</span> = 3, ** <span class="html-italic">p</span> ≤ 0.01, Student’s <span class="html-italic">t</span>-test).</p>
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19 pages, 3707 KiB  
Article
The Role of Different Rhizobacteria in Mitigating Aluminum Stress in Rice (Oriza sativa L.)
by Mercedes Susana Carranza-Patiño, Juan Antonio Torres-Rodriguez, Juan José Reyes-Pérez, Robinson J. Herrera-Feijoo, Ángel Virgilio Cedeño-Moreira, Alejandro Jair Coello Mieles, Cristhian John Macías Holguín and Cristhian Chicaiza-Ortiz
Int. J. Plant Biol. 2024, 15(4), 1418-1436; https://doi.org/10.3390/ijpb15040098 - 23 Dec 2024
Viewed by 282
Abstract
Aluminum toxicity in acidic soils threatens rice (Oryza sativa L.) cultivation, hindering agricultural productivity. This study explores the potential of plant growth-promoting rhizobacteria (PGPR) as a novel and sustainable approach to mitigate aluminum stress in rice. Two rice varieties, INIAP-4M and SUPREMA [...] Read more.
Aluminum toxicity in acidic soils threatens rice (Oryza sativa L.) cultivation, hindering agricultural productivity. This study explores the potential of plant growth-promoting rhizobacteria (PGPR) as a novel and sustainable approach to mitigate aluminum stress in rice. Two rice varieties, INIAP-4M and SUPREMA I-1480, were selected for controlled laboratory experiments. Seedlings were exposed to varying aluminum concentrations (0, 2, 4, 8, and 16 mM) in the presence of four PGPR strains: Serratia marcescens (MO4), Enterobacter asburiae (MO5), Pseudomonas veronii (R4), and Pseudomonas protegens (CHAO). The INIAP-4M variety exhibited greater tolerance to aluminum than SUPREMA I-1480, maintaining 100% germination up to 4 mM and higher vigor index values. The study revealed that rhizobacteria exhibited different responses to aluminum concentrations. P. protegens and S. marcescens showed the highest viability at 0 mM (2.65 × 1010 and 1.71 × 1010 CFU mL−1, respectively). However, P. veronii and S. marcescens exhibited the highest viability at aluminum concentrations of 2 and 4 mM, indicating their superior tolerance and adaptability under moderate aluminum stress. At 16 mM, all strains experienced a decrease, with P. protegens and E. asburiae being the most sensitive. The application of a microbial consortium significantly enhanced plant growth, increasing plant height to 73.75 cm, root fresh weight to 2.50 g, and leaf fresh weight to 6 g compared to the control (42.75 cm, 0.88 g, and 3.63 g, respectively). These findings suggest that PGPR offer a promising and sustainable strategy to bolster rice resilience against aluminum stress and potentially improve crop productivity in heavy metal-contaminated soils. Full article
(This article belongs to the Section Plant–Microorganisms Interactions)
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<p>Analysis of morphological traits in rice genotypes grown on water–agar with different concentrations of aluminum. (<b>A</b>) Degree of tolerance to aluminum of the SUPREMA I-1480 genotype. (<b>B</b>) Degree of tolerance to aluminum of the INIAP-4M genotype. The red arrows indicate root necrosis at concentrations of 8 and 16 mM.</p>
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<p>Viability of aluminum-tolerant rhizobacteria (Al<sub>2</sub>(SO<sub>4</sub>)<sub>3</sub>). The bars indicate the individual SD (standard deviation) for treatment (±). Means with equal letters on the lines do not differ significantly, according to Tukey’s test (<span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Cellular content of aluminum-tolerant rhizobacteria. The red arrows indicate the degree of tolerance at a 16 mM concentration. The Al concentration in X, while the abundance of specific species in Y from the figure.</p>
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<p>Tolerance of rhizobacteria to different concentrations of aluminum. The bars indicate the individual SD (standard deviation) for treatment (±). Means with equal letters in the column do not differ significantly, according to Tukey’s test (<span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Hydrogen potential content of aluminum-tolerant bacteria. Bars indicate individual SD (standard deviation) for treatment (±). Means with equal letters in the column do not differ significantly, according to Tukey’s test (<span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Plant and root length inoculated with aluminum-tolerant rhizobacteria. Bars indicate individual SD (standard deviation) for treatment (±). Means with equal letters in the column do not differ significantly, according to Tukey’s test (<span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Effect of rhizobacteria on fresh and dry weight of roots in aluminum-contaminated soil. Bars indicate individual SD (standard deviation) for treatment (±). Means with equal letters in the column do not differ significantly according to Tukey’s test (<span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Effect of rhizobacteria on fresh and dry weight of leaves in soil contaminated with aluminum. Bars indicate individual SD (standard deviation) for treatment (±). Means with equal letters in the column do not differ significantly, according to Tukey’s test (<span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Viability of the presence of tolerant rhizobacteria in aluminum-contaminated soil. Bars indicate individual SD (standard deviation) for treatment (±). Means with equal letters in the column do not differ significantly according to Tukey’s test (<span class="html-italic">p</span> ≤ 0.05). CUF: colony-forming units.</p>
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