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Search Results (2,336)

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11 pages, 5383 KiB  
Article
Analysis of ABA and Fructan Contents during Onion (Allium cepa L.) Storage in the Search for Internal Sprouting Indicators
by Antonino Crucitti, Wouter Kohlen, Annemarie Dechesne, Amber van Seters, Christian W. B. Bachem, Richard G. H. Immink and Olga E. Scholten
Horticulturae 2024, 10(9), 975; https://doi.org/10.3390/horticulturae10090975 (registering DOI) - 14 Sep 2024
Viewed by 142
Abstract
Early sprouting is a main cause of onion spoilage during storage. However, limited knowledge is available on which factors trigger sprouting. Here, this was studied in the Hyfive and Exhibition cultivars, which largely differ in sprouting time. Sprouting progress was compared to the [...] Read more.
Early sprouting is a main cause of onion spoilage during storage. However, limited knowledge is available on which factors trigger sprouting. Here, this was studied in the Hyfive and Exhibition cultivars, which largely differ in sprouting time. Sprouting progress was compared to the fructan and abscisic acid (ABA) profiles in the bulb scales and basal plates. Fructan concentrations decreased in the scales from harvest time onwards in the late-sprouting cultivar Hyfive, while remaining constant in the cultivar Exhibition until internal sprouting. In the basal plates, fructan concentrations increased in both cultivars from approximately one month after harvest, but reached maximum concentrations at moments that could not be related to the difference in internal sprouting. ABA levels generally decreased in the scales of both cultivars, while increasing in their basal plates. Nevertheless, for fructans, the measured variation in ABA concentrations was not consistently associated with differences in internal sprouting. A subsequent perturbation of internal sprouting by Maleic Hydrazide treatment in the cultivar Hyfive confirmed a lack of correlation. Altogether, this indicates that fructan and ABA levels in the scales and basal plate tissue change independent of internal sprouting and cannot be regarded as predictive markers for sprouting and storability. Full article
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Figure 1

Figure 1
<p>Longitudinal sections of bulbs from two onion cultivars at two different time points during storage. Pictures show cross-sections of Hyfive (<b>A</b>) and Exhibition (<b>B</b>) bulbs at 11 and 10 weeks after harvest (WAH), and of Hyfive (<b>C</b>) and Exhibition (<b>D</b>) bulbs at 17 and 16 WAH, respectively. The red lines in pictures (<b>B</b>–<b>D</b>) highlight the internal sprouts. No internal sprout was present in picture (<b>A</b>).</p>
Full article ">Figure 2
<p>Internal sprouting and fructan profiles during dry storage of cv. Hyfive and Exhibition onion bulbs. Storage time is indicated as weeks after harvest (WAH). (<b>A</b>,<b>C</b>,<b>E</b>) Measurements for Hyfive. (<b>B</b>,<b>D</b>,<b>F</b>) Measurements for Exhibition. In panels (<b>A</b>,<b>B</b>), the internal sprouting is plotted as the ratio ‘sprout length/bulb height’ (yellow bars); in panel (<b>C</b>,<b>D</b>), the DP4-DP7 fructan concentrations, and in panel (<b>E</b>,<b>F</b>), the DP3 fructan concentrations, are plotted in milligram (mg) fructans per gram (g) of dried scale or basal plate material (dry weight). The internal sprouting ratio and the fructan values are the mean values of three biological replicates. Error bars represent the standard error of the mean values.</p>
Full article ">Figure 3
<p>Simple sugars and internal sprouting profiles in dry-stored bulbs of cv. Hyfive and Exhibition. Storage time is indicated as weeks after harvest (WAH). (<b>A</b>,<b>C</b>) Internal sprouting, glucose, fructose, and sucrose profiles in scales. (<b>B</b>,<b>D</b>) Internal sprouting, glucose, fructose, and sucrose profiles in the basal plates. Sugar values are the mean values of three biological replicates, depicted in mg per g of dry weight. Error bars represent the standard errors of the mean values. Different lines represent levels of glucose (blue circles), fructose (orange triangles), and sucrose (grey squares). The yellow bars refer to internal sprouting, which is expressed as the ratio between ‘sprout length and bulb height’.</p>
Full article ">Figure 4
<p>Bulb ABA and internal sprouting profiles. The comparisons of ABA concentrations in (<b>A</b>) cv. Hyfive and (<b>B</b>) cv. Exhibition bulb scales (orange squares) and basal plates (blue circles) at six time points during dry storage. Timepoint values are the mean ABA concentrations of three biological replicates, depicted in pmol per mg of dry weight. Error bars represent the standard errors of the mean values. The yellow bar refers to internal sprouting, expressed as the ratio between ‘sprout length and bulb height’.</p>
Full article ">Figure 5
<p>Effects of Maleic Hydrazide (MH) treatment on internal sprouting, DP4-DP7 fructan profiles, and ABA profiles in scales and basal plates of onion cv. Hyfive bulbs. (<b>A</b>) DP4-DP7 fructan profile and (<b>B</b>) ABA profile in the basal plates of MH-treated (grey circles) and untreated (UN) control bulbs (orange circles), and the bulb scales of MH-treated (blue squares) and UN control bulbs (yellow squares) at four time points during dry storage. DP4-DP7 fructan values are the mean values of three biological replicates, depicted in mg per g of dry weight. ABA concentration values are the mean values of three biological replicates, depicted in pmol per mg of dry weight. Error bars represent the standard errors of the mean values. The yellow bar refers to internal sprouting, expressed as the ratio between ‘sprout length and bulb height’. Internal sprouting was only present in the UN samples, whereas no internal sprouting was observed in the MH-treated bulbs. Independent T-test showed that average fructan (DP4-DP7) and ABA concentrations are not statistically different (<span class="html-italic">p</span> &gt; 0.05) between the UN and MH-treated onion bulbs.</p>
Full article ">Figure A1
<p>Measurement of the bulb height and sprout length. Pictures show cross-sections of onion bulbs for bulb height (<b>left</b>) and longest sprout length (<b>right</b>) measurements.</p>
Full article ">Figure A2
<p>Schematic representation of the bulb tissues used in this study. The regions outlined in red represent the tissues referred to as the onion basal plate (<b>left</b>) and the scales (<b>right</b>).</p>
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17 pages, 9924 KiB  
Article
Osmanthus fragrans Ethylene Response Factor OfERF1-3 Delays Petal Senescence and Is Involved in the Regulation of ABA Signaling
by Gongwei Chen, Fengyuan Chen, Dandan Zhang, Yixiao Zhou, Heng Gu, Yuanzheng Yue, Lianggui Wang and Xiulian Yang
Forests 2024, 15(9), 1619; https://doi.org/10.3390/f15091619 (registering DOI) - 14 Sep 2024
Viewed by 241
Abstract
Osmanthus fragrans is widely used in gardening, but the short flowering period of O. fragrans affects its ornamental and economic value. ERF, as a plant ethylene response factor, is an important link in the regulation of plant senescence. In this study, we conducted [...] Read more.
Osmanthus fragrans is widely used in gardening, but the short flowering period of O. fragrans affects its ornamental and economic value. ERF, as a plant ethylene response factor, is an important link in the regulation of plant senescence. In this study, we conducted a comprehensive analysis of the functional role of OfERF1-3 within the petals of O. fragrans. Specifically, the OfERF1-3 gene was cloned and subjected to rigorous sequence analysis. Subsequently, to evaluate its expression patterns and effects, gene overexpression experiments were carried out on both Nicotiana tabacum and O. fragrans. The results showed that OfERF1-3-overexpressing tobacco plants exhibited longer petal opening times compared with those of wild plants. Measurements of physiological parameters also showed that the flowers of overexpressed tobacco plants contained lower levels of malondialdehyde (MDA) and hydrogen peroxide (H2O2) than those of the wild type. There was a lower expression of senescence marker genes in overexpressed tobacco and O. fragrans. A yeast two-hybrid assay showed that OfERF1-3 interacted with OfSKIP14 in a manner related to the regulation of ABA. In summary, OfERF1-3 can play a delaying role in the petal senescence process in O. fragrans, and it interacts with OfSKIP14 to indirectly affect petal senescence by regulating the ABA pathway. Full article
(This article belongs to the Section Genetics and Molecular Biology)
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Figure 1
<p>The identification of the <span class="html-italic">OfERF1-3</span> gene. (<b>A</b>) Phylogenetic tree of ERF1-3. (<b>B</b>) Alignment of the deduced amino acids <span class="html-italic">OfERF1-3</span>, <span class="html-italic">AtERF1-3</span>, and <span class="html-italic">OeERF1-3</span>. <span class="html-italic">OfERF1-3</span>: <span class="html-italic">Osmanthus fragrans ERF1-3</span>; <span class="html-italic">AtERF1-3</span>: <span class="html-italic">Arabidopsis thaliana ERF1-3</span>; <span class="html-italic">OeERF1-3</span>: <span class="html-italic">Olea europaea</span> var. sylvestris <span class="html-italic">ERF1-3</span>. The amino acids with light blue backgrounds indicate part homology.</p>
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<p>The expression level of <span class="html-italic">OfERF1-3</span> in petals of <span class="html-italic">O. fragrans</span> at different flowering stages. (<b>A</b>) <span class="html-italic">OfERF1-3</span> expression in the S1–S5 time periods in <span class="html-italic">O. fragrans</span>. FPKM is a unit of gene expression commonly used to measure the relative level of gene expression in the transcriptome. Groups labeled with the same letter indicate <span class="html-italic">p</span> &gt; 0.05, while different letters indicate <span class="html-italic">p</span> &lt; 0.05. Transcriptome data was obtained from the published article: “Analysis of the Aging-Related AP2/ERF Transcription Factor Gene Family in <span class="html-italic">Osmanthus fragrans</span>”. (<b>B</b>) The five flowering periods of <span class="html-italic">O. fragrans</span>: S1: linggeng stage, S2: xiangyan stage, S3: initial flowering stage, S4: full flowering stage, and S5: late flowering stage.</p>
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<p>Expression of the <span class="html-italic">OfERF1-3</span> in transgenic <span class="html-italic">Nicotiana tabacum</span> petals. (<b>A</b>) The expression of <span class="html-italic">OfERF1-3</span> in the S1–S5 time periods in <span class="html-italic">N. tabacum</span>. Groups labeled with the same letter indicate <span class="html-italic">p</span> &gt; 0.05, while different letters indicate <span class="html-italic">p</span> &lt; 0.05. (<b>B</b>) The five flowering periods of tobacco: S1: tight bud stage, S2: mature bud stage, S3: initial flowering stage, S4: full flowering stage, and S5: late flowering stage.</p>
Full article ">Figure 4
<p>Phenotypes of transgenic plants of tobacco with <span class="html-italic">OfERF1-3</span>. WT: wild-type plants; OE: overexpression plants. (<b>A</b>) Comparison of flowering time between wild-type and overexpression plants as a whole. (<b>B</b>) Single flowers from wild-type and overexpression plants from the early flowering stage period to abscission.</p>
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<p>Changes in the expression of senescence marker genes and physiological indexes in petals of <span class="html-italic">OfERF1-3</span> overexpressing tobacco. (<b>A</b>) Expression of <span class="html-italic">NtSAG12</span> in WT and OE petals. (<b>B</b>) Expression of <span class="html-italic">NtACO1</span> in WT and OE petals. (<b>C</b>) MDA content in WT and OE petals. Groups labeled with the same letter indicate <span class="html-italic">p</span> &gt; 0.05, while different letters indicate <span class="html-italic">p</span> &lt; 0.05. (<b>D</b>) H<sub>2</sub>O<sub>2</sub> content in WT and OE petals. WT: wild-type plants; OE: overexpression plants. ** indicate <span class="html-italic">p</span> &lt; 0.01.</p>
Full article ">Figure 6
<p>Phenotype analysis of transgenic petals of <span class="html-italic">O. fragrans</span> transformed with <span class="html-italic">OfERF1-3</span>; EV: pSuper1300 empty vector. (<b>A</b>) Phenotypes of transgenic petals of <span class="html-italic">O. fragrans</span> transformed with <span class="html-italic">OfERF1-3</span> over a 48 h period. (<b>B</b>) Comparative analysis of <span class="html-italic">OfERF1-3</span> expression of empty vector and transgenic petals of <span class="html-italic">O. fragrans</span>. (<b>C</b>) Expression of <span class="html-italic">OfSAG21</span> in pSuper1300 empty vector and pSuper1300-<span class="html-italic">OfERF1-3</span> transgenic petals. (<b>D</b>) Expression of <span class="html-italic">OfACS1</span> in pSuper1300 empty vector and pSuper1300-<span class="html-italic">OfERF1-3</span> transgenic petals. * indicate <span class="html-italic">p</span> &lt; 0.05 and *** indicate <span class="html-italic">p</span> &lt; 0.001.</p>
Full article ">Figure 7
<p>Yeast self-activation assay and two-hybrid sieve library assay results. (<b>A</b>) The results of the yeast self-activation assay showed that <span class="html-italic">OfERF1-3</span> exhibits no self-activating activity. The pGBKT7-Lam + pGADT7-T control vector served as a negative control. The pGBKT7-53 + pGADT7-T control vector served as a positive control. (<b>B</b>) A total of 21 positive yeast monoclones were obtained via yeast two-hybrid library screening.</p>
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<p>A yeast two-hybrid assay identified the <span class="html-italic">OfERF1-3</span> that interacted with <span class="html-italic">OfSKIP14</span>. The pGBKT7-Lam + pGADT7-T control vector served as a negative control. The pGBKT7-53 + pGADT7-T control vector served as a positive control.</p>
Full article ">
15 pages, 1726 KiB  
Article
Activation of ABA Signaling Pathway and Up-Regulation of Salt-Responsive Genes Confer Salt Stress Tolerance of Wheat (Triticum aestivum L.) Seedlings
by Zhiyou Zou, Aziz Khan, Adnan Khan, Zhongyi Tao, Sheng Zhang, Qiteng Long, Jinfu Lin and Shunshe Luo
Agronomy 2024, 14(9), 2095; https://doi.org/10.3390/agronomy14092095 (registering DOI) - 13 Sep 2024
Viewed by 277
Abstract
Salt is a potent abiotic stress that arrests plant growth by impairing their physio-biochemical and molecular processes. However, it is unknown how the ABA signaling system and vacuolar-type Na+/H+ antiporter proteins induce stress tolerance in wheat (Triticum aestivum L.) [...] Read more.
Salt is a potent abiotic stress that arrests plant growth by impairing their physio-biochemical and molecular processes. However, it is unknown how the ABA signaling system and vacuolar-type Na+/H+ antiporter proteins induce stress tolerance in wheat (Triticum aestivum L.) seedlings. The present study aimed to identify salt-responsive proteins and signaling pathways involved in the resistance of wheat to salt stress. We explored the proteome profile, 20 amino acids, 14 carbohydrates, 8 major phytohormones, ion content, and salt tolerance genes in wheat (Triticum aestivum L., cv.) under 200 mM NaCl with control plants for six days. The results showed that amino acids such as alanine, serine, proline, glutamine, and aspartic acid were highly expressed under salt stress compared with control plants, suggesting that amino acids are the main players in salinity tolerance. The ABA signaling system was activated in response to salinity stress through the modulation of protein phosphatase 2C (PP2C) and ABA-responsive element binding factor (ABF), resulting in an ABA-mediated downstream response. Additionally, the vacuolar-type Na+/H+ antiporter was identified as a key protein in salt stress tolerance via compartmentalizing Na+ in the vacuole. Furthermore, a significant increase in the abundance of the 14-3-3 protein was noticed in salt-fed plants, suggesting that this protein plays an important role in Na+ compartmentalization. Moreover, up-regulation of ascorbate peroxidase (APX), glutathione-S-transferase (GST), and thioredoxin-scavenged reactive oxygen species resulted in improved plant growth under salt stress. These data will help to identify salt-responsive proteins that can be used in future breeding programs to develop salt-tolerant varieties. Full article
(This article belongs to the Section Plant-Crop Biology and Biochemistry)
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Figure 1
<p>Effects of salinity stress (200 mM NaCl) on Na<sup>+</sup> and K<sup>+</sup> ion content in hexaploid wheat (20 days old). Between the stress and control treatments, a significant difference is indicated by * (<span class="html-italic">p</span> &lt; 0.05). A <span class="html-italic">t</span>-test was used to compare the differences between the treatments.</p>
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<p>The effects of salinity stress on carbohydrate contents of hexaploid wheat roots. Salinity stress (200 mM NaCl) was applied to wheat seedlings (20 days old). Between the stress and control treatments, the significance differences are indicated by * (<span class="html-italic">p</span> &lt; 0.05). A <span class="html-italic">t</span>-test was used to compare the differences between the treatments.</p>
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<p>The effects of salinity stress on free amino acids of hexaploid wheat roots. Salinity stress (200 mM NaCl) was applied to wheat seedlings (20 days old). Between the stress and control treatments, the significance differences are indicated by * (<span class="html-italic">p</span> &lt; 0.05). A <span class="html-italic">t</span>-test was used to compare the differences between the treatments.</p>
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<p>Potential regulatory mechanism of salinity stress tolerance in wheat root.</p>
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12 pages, 1072 KiB  
Article
Connecting Gene Variation to Treatment Outcomes in Metastatic Castration-Resistant Prostate Adenocarcinoma: Insights into Second-Generation Androgen Receptor Axis-Targeted Therapies
by Ana Vaz-Ferreira, Valéria Tavares, Inês Guerra de Melo, Patrícia Rafaela Rodrigues, Ana Afonso, Maria Joaquina Maurício and Rui Medeiros
Int. J. Mol. Sci. 2024, 25(18), 9874; https://doi.org/10.3390/ijms25189874 - 12 Sep 2024
Viewed by 252
Abstract
Prostate cancer (PC) is one of the most commonly diagnosed tumours among men. Second-generation androgen receptor axis-targeted (ARAT) agents, namely abiraterone acetate (AbA) and enzalutamide (ENZ), are currently used in the management of metastatic castration-resistant PC (mCRPC). However, the treatment is challenging due [...] Read more.
Prostate cancer (PC) is one of the most commonly diagnosed tumours among men. Second-generation androgen receptor axis-targeted (ARAT) agents, namely abiraterone acetate (AbA) and enzalutamide (ENZ), are currently used in the management of metastatic castration-resistant PC (mCRPC). However, the treatment is challenging due to the lack of prognostic biomarkers. Meanwhile, single-nucleotide polymorphisms (SNPs) have emerged as potential prognostic indicators of mCRPC. Thus, this study evaluated the impact of relevant SNPs on the treatment outcomes of 123 mCRPC patients enrolled in a hospital-based cohort study. The CYP17A1 rs2486758 C allele was associated with a 50% reduction in the risk of developing castration resistance (hazard ratio (HR) = 0.55; p = 0.003). Among patients without metastasis at tumour diagnosis and under AbA, a marginal association between YBX1 rs10493112 and progression-free survival was detected (log-rank test, p = 0.056). In the same subgroup, significant associations of HSD3B1 rs1047303 (CC/CA vs. AA; HR = 3.41; p = 0.025), YBX1 rs12030724 (AT vs. AA; HR = 3.54; p = 0.039) and YBX1 rs10493112 (log-rank test, p = 0.041; CC vs. AA/AC; HR = 3.22; p = 0.053) with overall survival were also observed, which were confirmed by multivariate Cox analyses. Although validation with larger cohorts is required, these findings suggest that SNPs could enhance the prognosis assessment of mCRPC patients, leading to a more personalised treatment. Full article
(This article belongs to the Special Issue Recent Molecular Research in Virology and Oncology)
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Figure 1
<p>Time to castration resistance (TCR) by Kaplan–Meier and Log-rank test for mCRPC patients (N = 116), according to <span class="html-italic">CYP17A1</span> rs2486758 genotypes. Patients with the C allele had a prolonged TCR compared to those carrying the TT genotype (<span class="html-italic">p</span> = 0.003).</p>
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<p>Overall survival (OS) by Kaplan–Meier and Log-rank test for mCRPC patients with localised tumour at diagnosis and under AbA-based treatment (N = 24), according to <span class="html-italic">HSD3B1</span> rs1047303 (<b>a</b>), <span class="html-italic">YBX1</span> rs12030724 (<b>b</b>) and <span class="html-italic">YBX1</span> rs10493112 (<b>c</b>) genotypes. Patients with the <span class="html-italic">HSD3B1</span> rs1047303 C allele genotypes had a lower OS compared to those carrying the AA genotype (<span class="html-italic">p</span> = 0.014). Patients with the <span class="html-italic">YBX1</span> rs12030724 AT genotype had a lower OS than AA genotype carriers (<span class="html-italic">p</span> = 0.027). Patients with the <span class="html-italic">YBX1</span> rs10493112 CC genotype had a lower OS than their counterparts (<span class="html-italic">p</span> = 0.041).</p>
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24 pages, 3080 KiB  
Article
Integrated Metabolome, Transcriptome and Long Non-Coding RNA Analysis Reveals Potential Molecular Mechanisms of Sweet Cherry Fruit Ripening
by Gangshuai Liu, Daqi Fu, Xuwei Duan, Jiahua Zhou, Hong Chang, Ranran Xu, Baogang Wang and Yunxiang Wang
Int. J. Mol. Sci. 2024, 25(18), 9860; https://doi.org/10.3390/ijms25189860 - 12 Sep 2024
Viewed by 211
Abstract
Long non-coding RNAs (lncRNAs), a class of important regulatory factors for many biological processes in plants, have received much attention in recent years. To explore the molecular roles of lncRNAs in sweet cherry fruit ripening, we conducted widely targeted metabolome, transcriptome and lncRNA [...] Read more.
Long non-coding RNAs (lncRNAs), a class of important regulatory factors for many biological processes in plants, have received much attention in recent years. To explore the molecular roles of lncRNAs in sweet cherry fruit ripening, we conducted widely targeted metabolome, transcriptome and lncRNA analyses of sweet cherry fruit at three ripening stages (yellow stage, pink stage, and dark red stage). The results show that the ripening of sweet cherry fruit involves substantial metabolic changes, and the rapid accumulation of anthocyanins (cyanidin 3-rutinoside, cyanidin 3-O-galactoside, and cyanidin 3-O-glucoside) is the main cause of fruit coloration. These ripening-related alterations in the metabolic profile are driven by specific enzyme genes related to the synthesis and decomposition of abscisic acid (ABA), cell wall disintegration, and anthocyanin biosynthesis, as well as transcription factor genes, such as MYBs, bHLHs, and WD40s. LncRNAs can target these ripening-related genes to form regulatory modules, incorporated into the sweet cherry fruit ripening regulatory network. Our study reveals that the lncRNA-mRNA module is an important component of the sweet cherry fruit ripening regulatory network. During sweet cherry fruit ripening, the differential expression of lncRNAs will meditate the spatio-temporal specific expression of ripening-related target genes (encoding enzymes and transcription factors related to ABA metabolism, cell wall metabolism and anthocyanin metabolism), thus driving fruit ripening. Full article
(This article belongs to the Special Issue Recent Advances in Plant Molecular Science in China 2024)
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Figure 1
<p>Analysis of differential metabolites in sweet cherry fruit at yellow stage, pink stage, and dark red stage. (<b>A</b>) The appearance of sweet cherry fruit at different ripening stages. (<b>B</b>) PCA of metabolites in sweet cherry fruit at different ripening stages. (<b>C</b>) Volcano plot of differential metabolites in sweet cherry fruit at yellow stage and pink stage. (<b>D</b>) Volcano plot of differential metabolites in sweet cherry fruit at pink stage and dark red stage. (<b>E</b>) Volcano plot of differential metabolites in sweet cherry fruit at yellow stage and dark red stage.</p>
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<p>Analysis of differential flavonoids in sweet cherry fruit at yellow stage, pink stage, and dark red stage. (<b>A</b>) Venn diagram of differential flavonoids in sweet cherry fruit at different ripening stages. (<b>B</b>) Abundance heatmap of differential flavonoids in sweet cherry fruit at different ripening stages.</p>
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<p>Analysis of DEGs in sweet cherry fruit at yellow stage, pink stage, and dark red stage. (<b>A</b>) Volcano plot of DEGs in sweet cherry fruit at yellow stage and pink stage. (<b>B</b>) Volcano plot of DEGs in sweet cherry fruit at pink stage and dark red stage. (<b>C</b>) KEGG analysis of DEGs in sweet cherry fruit at yellow stage and pink stage. (<b>D</b>) KEGG analysis of DEGs in sweet cherry fruit at pink stage and dark red stage. (<b>E</b>) GO analysis of DEGs in sweet cherry fruit at yellow stage and pink stage. (<b>F</b>) GO analysis of DEGs in sweet cherry fruit at pink stage and dark red stage. Rich factor: the ratio of the number of DEGs annotated in one pathway to the number of all genes annotated in that pathway. GeneRatio: the ratio of the number of DEGs annotated in one term to all DEG number. Qvalue is the correction of pvalue after multiple hypothesis testing. A smaller qvalue indicates a more reliable enrichment significance of DEGs in this pathway.</p>
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<p>Characterization of lncRNAs in sweet cherry fruit at yellow stage, pink stage, and dark red stage. (<b>A</b>) Classification of lncRNAs identified in sweet cherry fruit at different ripening stages. (<b>B</b>) Length of lncRNAs identified in sweet cherry fruit at different ripening stages.</p>
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<p>Analysis of DE-lncRNAs and their target genes in sweet cherry fruit at yellow stage, pink stage, and dark red stage. (<b>A</b>) Volcano plot of DE-lncRNAs in sweet cherry fruit at yellow stage and pink stage. (<b>B</b>) GO analysis of the target genes of DE-lncRNAs in sweet cherry fruit at yellow stage and pink stage. (<b>C</b>) Volcano plot of DE-lncRNAs in sweet cherry fruit at pink stage and dark red stage. (<b>D</b>) GO analysis of the target genes of DE-lncRNAs in sweet cherry fruit at pink stage and dark red stage. GeneRatio: the ratio of the number of target genes annotated in one term to all target gene numbers.</p>
Full article ">Figure 6
<p>RT-qPCR verification of the results of transcriptome and lncRNA sequencing. (<b>A</b>) The relative expression and fold change of DEGs and DE-lncRNAs in sweet cherry fruit at yellow stage and pink stage. (<b>B</b>) The relative expression and fold change of DEGs and DE-lncRNAs in sweet cherry fruit at pink stage and dark red stage. Error bars represent the SE (n = 3). Asterisk denotes significant difference between two groups (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01).</p>
Full article ">Figure 7
<p>Regulatory network of anthocyanin biosynthesis and other ripening aspects mediated by lncRNAs in sweet cherry fruit. The dotted line represents a multi-step reaction. The red arrows represent transcriptional activation. The yellow ovals represent transcription factors. Purple boxes represent upstream DE-lncRNAs of target genes.</p>
Full article ">
23 pages, 4097 KiB  
Review
Current Insights into Weak Seed Dormancy and Pre-Harvest Sprouting in Crop Species
by Angel J. Matilla
Plants 2024, 13(18), 2559; https://doi.org/10.3390/plants13182559 - 12 Sep 2024
Viewed by 250
Abstract
During the domestication of crops, seed dormancy has been reduced or eliminated to encourage faster and more consistent germination. This alteration makes cultivated crops particularly vulnerable to pre-harvest sprouting, which occurs when mature crops are subjected to adverse environmental conditions, such as excessive [...] Read more.
During the domestication of crops, seed dormancy has been reduced or eliminated to encourage faster and more consistent germination. This alteration makes cultivated crops particularly vulnerable to pre-harvest sprouting, which occurs when mature crops are subjected to adverse environmental conditions, such as excessive rainfall or high humidity. Consequently, some seeds may bypass the normal dormancy period and begin to germinate while still attached to the mother plant before harvest. Grains affected by pre-harvest sprouting are characterized by increased levels of α-amylase activity, resulting in poor processing quality and immediate grain downgrading. In the agriculture industry, pre-harvest sprouting causes annual economic losses exceeding USD 1 billion worldwide. This premature germination is influenced by a complex interplay of genetic, biochemical, and molecular factors closely linked to environmental conditions like rainfall. However, the exact mechanism behind this process is still unclear. Unlike pre-harvest sprouting, vivipary refers to the germination process and the activation of α-amylase during the soft dough stage, when the grains are still immature. Mature seeds with reduced levels of ABA or impaired ABA signaling (weak dormancy) are more susceptible to pre-harvest sprouting. While high seed dormancy can enhance resistance to pre-harvest sprouting, it can lead to undesirable outcomes for most crops, such as non-uniform seedling establishment after sowing. Thus, resistance to pre-harvest sprouting is crucial to ensuring productivity and sustainability and is an agronomically important trait affecting yield and grain quality. On the other hand, seed color is linked to sprouting resistance; however, the genetic relationship between both characteristics remains unresolved. The identification of mitogen-activated protein kinase kinase-3 (MKK3) as the gene responsible for pre-harvest sprouting-1 (Phs-1) represents a significant advancement in our understanding of how sprouting in wheat is controlled at the molecular and genetic levels. In seed maturation, Viviparous-1 (Vp-1) plays a crucial role in managing pre-harvest sprouting by regulating seed maturation and inhibiting germination through the suppression of α-amylase and proteases. Vp-1 is a key player in ABA signaling and is essential for the activation of the seed maturation program. Mutants of Vp-1 exhibit an unpigmented aleurone cell layer and exhibit precocious germination due to decreased sensitivity to ABA. Recent research has also revealed that TaSRO-1 interacts with TaVp-1, contributing to the regulation of seed dormancy and resistance to pre-harvest sprouting in wheat. The goal of this review is to emphasize the latest research on pre-harvest sprouting in crops and to suggest possible directions for future studies. Full article
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<p>Wheat seeds exhibiting pre-harvest sprouting (courtesy of J. Barrero-Sánchez; <a href="https://people.csiro.au/B/J/Jose-Barrero" target="_blank">https://people.csiro.au/B/J/Jose-Barrero</a> (accessed on 25 May 2024 )).</p>
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<p>Wheat spike with pre-harvest sprouting (courtesy of Z. Pang and Y. Liang; <a href="mailto:ycliang@zju.edu.cn">ycliang@zju.edu.cn</a>).</p>
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18 pages, 7449 KiB  
Article
Norisoprenoid Accumulation under Genotype and Vintage Effects in Vitis vinifera L. Wine Varieties
by Xiangyi Li, Naveed Ahmad, Yuan Gao, Yachen Wang, Xiao Meng, Changqing Duan, Jiang Lu and Qiuhong Pan
Horticulturae 2024, 10(9), 970; https://doi.org/10.3390/horticulturae10090970 - 12 Sep 2024
Viewed by 276
Abstract
Norisoprenoids are important chemical compounds to grape and wine aroma, and their content in the grape berries can be greatly affected by varietal, terroir, and environmental factors. In this study, we investigate how major factors, such as genotype and climate conditions, influence the [...] Read more.
Norisoprenoids are important chemical compounds to grape and wine aroma, and their content in the grape berries can be greatly affected by varietal, terroir, and environmental factors. In this study, we investigate how major factors, such as genotype and climate conditions, influence the physicochemical properties of grape juice, volatile C13-norisoprenoid compounds, and gene expression profiles of three Vitis vinifera grape varieties: Muscat blanc à Petit grain, Muscat à petits grains rouges, and Gewürztraminer during the production period in 2010 and 2011. The total soluble solids (TSS) of both Muscat varieties were significantly higher in 2011 compared to 2010, reflecting interannual climatic variations, while Gewürztraminer showed no significant differences. At full maturity, total acid of all three cultivars was consistent between the years, indicating genetic determination. Thirteen norisoprenoids were identified, with Muscat varieties showing consistently higher levels than Gewürztraminer, irrespective of the production year. Varietal differences were significant for 13 out of 14 volatile compounds, and vintage effects were notable for 11 compounds, including key aroma contributors β-damascenone and β-ionone. OPLS-DA analysis highlighted distinct volatile profiles for each variety and vintage, influenced by climatic factors such as precipitation and sunlight hours. Gene expression analysis revealed strong correlations between VvCCD1, VvCCD4a, and VvCCD4b genes and C13-norisoprenoid accumulation, with these genes also implicated in the ABA biosynthesis pathway. Single nucleotide polymorphisms (SNPs) in VvCCD1, VvCCD4a, and VvCCD4b were linked to variations in norisoprenoid content among the cultivars. Altogether, these findings revealed the interaction of genetic and environmental factors in shaping the physicochemical properties for the grape, volatile profiles, and gene expression patterns of grape berries, with significant implications for viticulture and the winemaking process. Full article
(This article belongs to the Special Issue Novel Insights into Sustainable Viticulture)
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<p>Physicochemical indexes of three <span class="html-italic">Vitis vinifera</span> varieties during grape development and climatic data of Gaotai in 2011 and 2010. (<b>A</b>) Physicochemical indexes of three <span class="html-italic">Vitis vinifera</span> varieties during grape development. (<b>B</b>) Climatic data of Gaotai in 2011 and 2010, including average daily temperature, diurnal temperature difference, rainfall, and sunshine duration. Orange lines indicate 7 sampling days (7.13, 7.26, 8.10, 8.23, 9.10, 9.22, and 9.27), respectively. * Represents significant differences of parameters between 2010 and 2011 groups for the same stage (<span class="html-italic">t</span> test, <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Evolution of the concentrations of total volatile norisoprenoids in three grape cultivars during berry development. Mus: Muscat blanc à Petit grains; MR: Muscat à petits grains rouges; Gew: Gewürztraminer. Different letters on the line chart indicate statistically different values (<span class="html-italic">p</span> &lt; 0.05) according to Duncan’s test; no letters indicate no statistical difference.</p>
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<p>Volatile C13-norisoprenoid compounds between vintage 2010 and 2011. Orthogonal partial least squares discriminant analysis (OPLS-DA) score plot (<b>A</b>) and the loadings plot (<b>B</b>) obtained from grape berries between vintage 2010 and 2011. (<b>C</b>) Key volatile C13-norisoprenoid compounds between vintage 2010 and 2011. * Represents significant differences of parameters between 2010 and 2011 groups for the same stage (<span class="html-italic">t</span> test, <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Volatile C13-norisoprenoid compounds among different varieties. Orthogonal partial least squares discriminant analysis (OPLS-DA) (<b>A</b>) score plot and variable correlation map obtained from grape berries among different varieties. (<b>B</b>) Key volatile C13-norisoprenoid compounds among different varieties. Different letters on the line chart indicate statistically different values (<span class="html-italic">p</span> &lt; 0.05) according to Duncan’s test; no letters indicate no statistical difference.</p>
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<p>The correlation between gene expression levels and monoterpenoid compound content in different varieties. The grape varieties were Muscat blanc à Petit grain, Muscat à petits grains rouges, and Gewürztraminer. Key volatile C13-norisoprenoid compounds were positively related gene expression levels (bright red) or negatively related gene expression levels (bright blue), and white represented no relation between compounds and gene expression levels.</p>
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<p>The correlation between gene expression levels and monoterpenoid compound content in different varieties. The grape varieties were Muscat blanc à Petit grain, Muscat à petits grains rouges, and Gewürztraminer. Key volatile C13-norisoprenoid compounds were positively related gene expression levels (bright red) or negatively related gene expression levels (bright blue), and white represented no relation between compounds and gene expression levels.</p>
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21 pages, 6139 KiB  
Article
Identification of VvAGL Genes Reveals Their Network’s Involvement in the Modulation of Seed Abortion via Responding Multi-Hormone Signals in Grapevines
by Fei Liu, Rana Badar Aziz, Yumiao Wang, Xuxian Xuan, Mucheng Yu, Ziyang Qi, Xinpeng Chen, Qiqi Wu, Ziyang Qu, Tianyu Dong, Shaonan Li, Jinggui Fang and Chen Wang
Int. J. Mol. Sci. 2024, 25(18), 9849; https://doi.org/10.3390/ijms25189849 - 12 Sep 2024
Viewed by 205
Abstract
The formation of seedless traits is regulated by multiple factors. AGLs, which belong to the MADS-box family, were reported to be important regulators in this process; however, the underlying mechanism remains elusive. Here, we identified the VvAGL sub-family genes during the seed abortion [...] Read more.
The formation of seedless traits is regulated by multiple factors. AGLs, which belong to the MADS-box family, were reported to be important regulators in this process; however, the underlying mechanism remains elusive. Here, we identified the VvAGL sub-family genes during the seed abortion process in seedless grapevine cv. ‘JingkeJing’ and found 40 differentially expressed VvAGL members and 1069 interacting proteins in this process. Interestingly, almost all members and their interacting proteins involved in the tryptophan metabolic pathway (K14486) and participated in the phytohormone signalling (KO04075) pathway, including the growth hormone (IAA), salicylic acid (SA), abscisic acid (ABA), cytokinin (CTK), and ethylene signalling pathways. The promoters of AGL sub-family genes contain cis-elements in response to hormones such as IAA, ABA, CTK, SA, and ETH, implying that they might respond to multi-hormone signals and involve in hormone signal transductions. Further expression analysis revealed VvAGL6-2, VvAGL11, VvAGL62-11, and VvAGL15 had the highest expression at the critical period of seed abortion, and there were positive correlations between ETH-VvAGL15-VvAGL6-2, ABA-VvAGL80, and SA-VvAGL62 in promoting seed abortion but negative feedback between IAA-VvAGL15-VvAGL6-2 and CTK-VvAGL11. Furthermore, many genes in the IAA, ABA, SA, CTK, and ETH pathways had a special expressional pattern in the seed, whereby we developed a regulatory network mediated by VvAGLs by responding to multihormonal crosstalk during grape seed abortion. Our findings provide new insights into the regulatory network of VvAGLs in multi-hormone signalling to regulate grape seed abortion, which could be helpful in the molecular breeding of high-quality seedless grapes. Full article
(This article belongs to the Collection Advances in Molecular Plant Sciences)
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<p>Morphological changes in berries’ development during grapevine seed abortion process: (<b>A</b>) Morphological changes of ‘Zhengyan seedless’ during berries and seeds development in seeds abortion process. (<b>B</b>) The length and width of grapefruits in the seed abortion process.</p>
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<p>Physicochemical properties of AGL family genes: (<b>A</b>) Residual base. (<b>B</b>) Molecular mass/KD. (<b>C</b>) Isoelectric point. (<b>D</b>) Fat factor. (<b>E</b>) Hydrophilicity. (<b>F</b>) Maximum distribution of chromosomes. The X-axis indicates the chromosome on which the AGL sub-family genes are located, and the Y-axis indicates the maximum and minimum values of the corresponding physicochemical properties of the genes distributed on this chromosome.</p>
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<p>Sequence analysis and chromosomal location of <span class="html-italic">VvAGLs</span>: (<b>A</b>) The phylogenetic tree generated using the MEGA11.0 program with the Maximum Likelihood method. Yellow, green and blue colours represent different branches; red colour indicates the CDS region of the gene structure and purple colour represents the exons of the gene. (<b>B</b>) The exon–intron composition of <span class="html-italic">AGL</span> genes. The coding sequences (CDS) and up- or down-stream regions of <span class="html-italic">AGL</span> genes are represented by red and purple boxes, respectively. Lines represent the introns. (<b>C</b>) The chromosome localisation of <span class="html-italic">VvAGLs</span>. The red dashed box indicates the distribution of key AGL family members on the chromosome. Different colours represent different chromosomes.</p>
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<p>Phylogenetic analysis of the AGL family across six species: The phylogenetic tree was generated after an alignment of deduced <span class="html-italic">Vitis vinifera</span>, <span class="html-italic">Zea mays</span>, <span class="html-italic">Arabidopsis thaliana</span>, <span class="html-italic">Citrus sinensis</span>, <span class="html-italic">Malus domestic</span>, and <span class="html-italic">Solanum lycopersicum AGL</span> domains at the N-terminus. We constructed the phylogenetic tree using the Maximum Likelihood method against MEGA11.0 to study the evolutionary relationship between species. Based on the results of the clustering analysis of the homologous gene family, a single copy of the homologous gene was selected for multi-sequence alignment [using MUSCLE software (MEGA11.0) for sequence alignment] and a phylogenetic tree was constructed based on the single-copy gene method. Stars represented VvAGLs family members, triangular stars represented the main members of VvAGLs, different colors represented different groupings.</p>
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<p>GO and KEEG enrichment analysis of <span class="html-italic">VvAGLs</span> family: (<b>A</b>) Number of interacting proteins in different periods. (<b>B</b>) The KEGG pathway annotation of the top 42 significant enriched pathways. (<b>C</b>) The GO analysis of 14 biological processes. Numbers on columns: The number of interacted genes involved in the GO pathway. ko00604//Glycosphingolipid biosynthesis—ganglio series.</p>
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<p>Screening and analysis of cis-elements in the promoters of <span class="html-italic">VvAGLs</span>: (<b>A</b>) The number of hormone-responsive cis-elements in promoter regions of <span class="html-italic">VvAGLs</span>; (<b>B</b>) Types of hormone-responsive cis-elements in promoter regions of <span class="html-italic">VvAGLs.</span> ABRE: ABA-responsive cis-elements; AuxRR-core and TGA-element: IAA-responsive cis-elements; CGTCA-motif and TGACG-motif: MeJA-responsive cis-elements; TCA-element and SARE: SA-responsive cis-elements; GARE-motif, P-box and TATC-box: GA-responsive cis-elements. ABA: abscisic acid; CTK: cytokinin; AUX/IAA: auxin–response repressor protein/indole acetic acid; SA: salicylic acid; MeJA: methyl jasmonate. Lighter to darker colous represented an increase in the number of cis-elements.</p>
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<p>Expression analysis of <span class="html-italic">VvAGL</span> and reciprocal proteins: (<b>A</b>) Expression of the <span class="html-italic">VvAGL</span> family at different developmental periods. (<b>B</b>) Expression of <span class="html-italic">VvAGL</span> reciprocal proteins at different developmental periods. (<b>C</b>) Proteins are specifically expressed in different hormone metabolic pathways. JY: Stage of seed development; JS: Stage of seed beginning to abort; JB: Late stage of seed abortion; Asterisks indicate a significant difference between JY, JS, and JB by Student (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Expression analysis of <span class="html-italic">VvAGL</span> and reciprocal proteins: (<b>A</b>) Expression of the <span class="html-italic">VvAGL</span> family at different developmental periods. (<b>B</b>) Expression of <span class="html-italic">VvAGL</span> reciprocal proteins at different developmental periods. (<b>C</b>) Proteins are specifically expressed in different hormone metabolic pathways. JY: Stage of seed development; JS: Stage of seed beginning to abort; JB: Late stage of seed abortion; Asterisks indicate a significant difference between JY, JS, and JB by Student (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Expression of <span class="html-italic">VvAGL</span> family members in grape seed. Asterisks indicate a significant difference between different development periods using Student’s <span class="html-italic">t</span>-test (* <span class="html-italic">p</span> &lt; 0.01; ** <span class="html-italic">p</span> &lt; 0.05); WK ‘Wink’; HL ‘Blush seedless’; JT ‘Jintian Huangjia seedless’.</p>
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<p>VvAGL-interacting genes and polyhormonal regulatory networks: (<b>A</b>) AGL family genes and interacting proteins are involved in the metabolic processes. (<b>B</b>) Involvement of VvAGL-interacting genes in the polyhormonal interaction network. (<b>C</b>) The levels of VvAGLs expression and the involvement of hormonal biosynthesis and signalling genes in plant growth and development. Asterisks indicate a significant difference between different development periods using Student’s <span class="html-italic">t</span>-test (* <span class="html-italic">p</span> &lt; 0.01; ** <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Schematic representation of plant hormone metabolism and signalling pathway gene network during grape seed abortion. The genes involved in the CTK signalling pathway are represented by a grey box; the genes involved in the SA signalling pathway are represented by a brown box; the genes involved in the IAA signalling pathway are represented by an orange box; the genes involved in ETH signalling pathway are represented by the yellow box; the genes involved in ABA signalling pathway are represented by the green box. STK, AP2, FUL, SUN, and bHLH were referenced in previous studies but not identified in this study. The bidirectional arrows represented the interaction between the two genes.</p>
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24 pages, 18105 KiB  
Article
Diverse Strategies to Develop Poly(ethylene glycol)–Polyester Thermogels for Modulating the Release of Antibodies
by Daria Lipowska-Kur, Łukasz Otulakowski, Urszula Szeluga, Katarzyna Jelonek and Alicja Utrata-Wesołek
Materials 2024, 17(18), 4472; https://doi.org/10.3390/ma17184472 - 12 Sep 2024
Viewed by 260
Abstract
In this work, we present basic research on developing thermogel carriers containing high amounts of model antibody immunoglobulin G (IgG) with potential use as injectable molecules. The quantities of IgG loaded into the gel were varied to evaluate the possibility of tuning the [...] Read more.
In this work, we present basic research on developing thermogel carriers containing high amounts of model antibody immunoglobulin G (IgG) with potential use as injectable molecules. The quantities of IgG loaded into the gel were varied to evaluate the possibility of tuning the dose release. The gel materials were based on blends of thermoresponsive and degradable ABA-type block copolymers composed of poly(lactide-co-glycolide)-b-poly(ethylene glycol)-b-poly(lactide-co-glycolide) (PLGA–PEG–PLGA) or poly(lactide-co-caprolactone)-b-poly(ethylene glycol)-b-(lactide-co-caprolactone) (PLCL–PEG–PLCL). Primarily, the gels with various amounts of IgG were obtained via thermogelation, where the only factor inducing gel formation was the change in temperature. Next, to control the gels’ mechanical properties, degradation rate, and the extent of antibody release, we have tested two approaches. The first one involved the synergistic physical and chemical crosslinking of the copolymers. To achieve this, the hydroxyl groups located at the ends of the PLGA–PEG–PLGA chain were modified into acrylate groups. In this case, the thermogelation was accompanied by chemical crosslinking through the Michael addition reaction. Such an approach increased the dynamic mechanical properties of the gels and simultaneously prolonged their decomposition time. An alternative solution was to suspend crosslinked PEG–polyester nanoparticles loaded with IgG in a PLGA–PEG–PLGA gelling copolymer. We observed that loading IgG into thermogels lowered the gelation temperature (TGEL) value and increased the storage modulus of the gels, as compared with gels without IgG. The prepared gel materials were able to release the IgG from 8 up to 80 days, depending on the gel formulation and on the amount of loaded IgG. The results revealed that additional, chemical crosslinking of the thermogels and also suspension of particles in the polymer matrix substantially extended the duration of IgG release. With proper matching of the gel composition, environmental conditions, and the type and amount of active substances, antibody-containing thermogels can serve as effective IgG delivery materials. Full article
(This article belongs to the Special Issue Applied Stimuli-Responsive Polymer Based Materials)
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<p>DMA measurement results for exemplifying <b>P2</b> and <b>P4</b> copolymers at a concentration of 25 wt%: <b>P2</b> in (<b>A</b>) water and (<b>B</b>) 0.9% NaCl; <b>P4</b> in (<b>C</b>) water and (<b>D</b>) 0.9% NaCl (oscillation frequency of 1 Hz).</p>
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<p>DMA measurement results for exemplifying blends: (<b>A</b>) <b>P2</b>/<b>P4</b> in water, (<b>B</b>) <b>P2</b>/<b>P4</b> in 0.9% NaCl, (<b>C</b>) <b>P2</b>/<b>P4</b> with IgG in 0.9% NaCl, (<b>D</b>) <b>P2</b>/<b>P4</b>/<b>P6</b> in 0.9% NaCl, and (<b>E</b>) <b>P2</b>/<b>P4</b>/<b>P6</b> blend with IgG in 0.9% NaCl (oscillation frequency of 1 Hz).</p>
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<p>DMA measurement results for (<b>A</b>) <b>P2M</b> in 0.9% NaCl, (<b>B</b>) <b>P4M</b> in 0.9% NaCl (<b>C</b>) <b>P2M</b>/<b>P4M</b> blend in 0.9% NaCl, (<b>D</b>) <b>P2M</b>/<b>P4M</b> blend with IgG in 0.9% NaCl, and (<b>E</b>) <b>P2M</b>/<b>P4M</b>/<b>P6</b> blend in 0.9% NaCl. Oscillation frequency of 1 Hz.</p>
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<p>(<b>A</b>) Decrease in molar mass during degradation of two-component gels made of non-modified and modified copolymers; (<b>B</b>) comparison of molar mass loss during degradation of <b>P2</b>/<b>P4</b> and <b>P2</b>/<b>P4</b>/<b>P6</b> gels (0.9% NaCl, 33 °C).</p>
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<p>IgG cumulative release from (<b>A</b>) <b>P2</b>/<b>P4,</b> (<b>B</b>) <b>P2</b>/<b>P4</b>/<b>P6</b>, (<b>C</b>) <b>P2M</b>/<b>P4M</b>, and (<b>D</b>) <b>P2M</b>/<b>P4M</b>/<b>P6</b> blends.</p>
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<p>CryoTEM images of (<b>A</b>) empty nanoparticles, and representative <b>P2M</b>/<b>P4M</b> nanoparticles loaded with IgG at a 33.6 mg/mL concentration via (<b>B</b>) the diffusion method (IgG_I) and (<b>C</b>) entrapment during formation method (IgG_II).</p>
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<p>DMA measurement results for (<b>A</b>) <b>P2M</b>/<b>P4M</b> nanoparticles suspended in <b>P3</b> thermogel with IgG (<b>P2M</b>/<b>P4M</b>/<b>P3</b>/IgG_II, 33.6 mg/mL IgG) and (<b>B</b>) blank nanoparticles suspended in <b>P3</b> thermogel. Storage modulus and loss modulus results were obtained at an oscillation frequency of 1 Hz.</p>
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<p>IgG release plots from the (<b>A</b>) <b>P2M</b>/<b>P4M</b>/<b>P3</b>/IgG_I (<b>B</b>) <b>P2M</b>/<b>P4M</b>/<b>P3</b>/IgG_II system.</p>
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<p>Effect of <b>P2M</b>/<b>P4M</b>/<b>P6</b> polymer and the extract of the <b>P2M</b>/<b>P4M</b>/<b>P6</b> gel on proliferation of normal human cells after 72 h (<span class="html-italic">p</span> &lt; 0.05 vs. control group).</p>
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<p>A schematic representation of the thermogel systems used for release of IgG antibody. Thermogels are composed of a two- or three-component blend of unmodified copolymers (<b>A</b>), unmodified with modified copolymers (<b>B</b>), or of crosslinked nanoparticles embedded in a polymer matrix (<b>C</b>).</p>
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20 pages, 5699 KiB  
Article
The ABA/LANCL1-2 Hormone/Receptors System Controls ROS Production in Cardiomyocytes through ERRα
by Sonia Spinelli, Lucrezia Guida, Mario Passalacqua, Mirko Magnone, Bujar Caushi, Elena Zocchi and Laura Sturla
Biomedicines 2024, 12(9), 2071; https://doi.org/10.3390/biomedicines12092071 - 11 Sep 2024
Viewed by 228
Abstract
Rat H9c2 cardiomyocytes overexpressing the abscisic acid (ABA) hormone receptors LANCL1 and LANCL2 have an increased mitochondrial proton gradient, respiration, and vitality after hypoxia/reoxygenation. Our aim was to investigate the role of the ABA/LANCL1-2 system in ROS turnover in H9c2 cells. H9c2 cells [...] Read more.
Rat H9c2 cardiomyocytes overexpressing the abscisic acid (ABA) hormone receptors LANCL1 and LANCL2 have an increased mitochondrial proton gradient, respiration, and vitality after hypoxia/reoxygenation. Our aim was to investigate the role of the ABA/LANCL1-2 system in ROS turnover in H9c2 cells. H9c2 cells were retrovirally infected to induce the overexpression or silencing of LANCL1 and LANCL2, without or with the concomitant silencing of the transcription factor ERRα. Enzymes involved in radical production or scavenging were studied by qRT-PCR and Western blot. The mitochondrial proton gradient and ROS were measured with specific fluorescent probes. ROS-generating enzymes decreased, ROS-scavenging enzymes increased, and mitochondrial ROS were reduced in LANCL1/2-overexpressing vs. control cells infected with the empty vector, while the opposite occurred in LANCL1/2-silenced cells. The knockdown of ERRα abrogated all beneficial effects on ROS turnover in LANCL1/2 overexpressing cells. Taken together, these results indicate that the ABA/LANCL1-2 system controls ROS turnover in H9c2 via ERRα. The ABA/LANCL system emerges as a promising target to improve cardiomyocyte mitochondrial function and resilience to oxidative stress. Full article
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<p>Signaling pathways and functions of ABA in cardiac and skeletal muscle and adipose tissue. The ABA/LANCL1-2 hormone/receptor system, by activating the AMPK/PGC-1α/Sirt1 axis and the orphan-receptor/transcription factor ERRα, stimulates several key mitochondrial functions, such as mitochondrial biogenesis, respiration, and oxidative phosphorylation uncoupling, leading to increased energy availability. Various hormonal/stress signals, including ABA, hypoxia, fasting, exercise, and cold, activate this signaling pathway under physiological conditions. We hypothesized a role for the ABA/LANCL signaling axis in protection against oxygen radicals, a by-product of intense mitochondrial activity.</p>
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<p>Overexpression and silencing of LANCL1/2 in H9c2 rat cardiomyocytes. LANCL1 and LANCL2 were overexpressed (<b>A</b>) or silenced (<b>B</b>) in H9c2 cells by lentiviral infection. (<b>A</b>) Left panel, representative Western blots of LANCL1 and LANCL2 protein expression in cells overexpressing both LANCL proteins (OVL1+2) or infected with the empty vector (PLV); right panel, densitometric quantitation of the LANCL proteins expression in the same cell types normalized on PLV control and relative to vinculin. Values are normalized against vinculin, as a housekeeping protein. (<b>B</b>) Left panel, representative Western blots of LANCL1 and LANCL2 in cells silenced for the expression of both proteins (SHL1+2) or infected with the scrambled sequences (SCR); upper right panel, densitometric quantitation of the LANCL proteins in the same cell types, normalized on SCR control and relative to vinculin; lower right panel, LANCL1/2 mRNA levels relative to control in LANCL1/2-silenced cells. Values are normalized against vinculin, as a housekeeping protein. The exposure times of the Western blots shown in (<b>A</b>,<b>B</b>) were different (30 s for (<b>A</b>) and 180 s for (<b>B</b>)), in order to visualize the much lower protein levels in LANCL1/2-silenced H9c2 cells. Data are the mean ± SD from at least three experiments. ** <span class="html-italic">p</span> &lt; 0.001 relative to control cells (PLV for overexpression or SCR for silencing) by unpaired Student’s <span class="html-italic">t</span>-test.</p>
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<p>Overexpression of LANCL1 and LANCL2 protects H9c2 cardiomyocytes from H<sub>2</sub>O<sub>2</sub> induced-oxidative stress. H9c2 overexpressing LANCL1 and LANCL2 (OVL1+2), double-silenced (SHL1+2), and control cells (PLV) were incubated in the absence or in the presence of 200 or 600 µM H<sub>2</sub>O<sub>2</sub> for 3 h. (<b>A</b>) The cell viability was determined by resazurin. Results are expressed as the percentage of cell survival relative to untreated cells. (<b>B</b>) Intracellular lipid hydroperoxides production was detected with the fluorescent probe C11-BODIPY 581/591. Results are expressed as the fluorescence intensity ratio at 590/510 nm. Histograms summarize the quantitative data of the mean ± SD of three independent experiments. * <span class="html-italic">p</span> &lt; 0.01, ** <span class="html-italic">p</span> &lt; 0.001 relative to the respective control cells by unpaired <span class="html-italic">t</span>-test.</p>
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<p>Radicals-generating enzymes are reduced in LANCL1/2-overexpressing vs. double-silenced H9c2 cells. (<b>A</b>) qRT-PCR analysis of the transcription of radicals-generating enzymes (COX2, XO, and NOX4) in cells overexpressing (OVL1+2) or silenced (SHL1+2) for LANCL1 and LANCL2 proteins and incubated in the absence or in the presence of 100 nM ABA for 4 h. Results are expressed relative to control ABA-untreated, PLV cells. ** <span class="html-italic">p</span> &lt; 0.001 relative to untreated control cells (PLV or SCR) and <span>$</span> <span class="html-italic">p</span> &lt; 0.02 relative to ABA-untreated OVL1+2 or SHL1+2 cells by unpaired <span class="html-italic">t</span>-test. (<b>B</b>) Upper panel, representative Western blot image of COX2 and XO proteins in LANCL1/2-overexpressing or silenced cells, treated or not with 100 nM ABA for 4 h. Lower panel, histograms are the mean ± SD from at least three experiments. Results are expressed relative to ABA-untreated OVL1+2 cells. Values are normalized against vinculin, as a housekeeping protein. Data are ** <span class="html-italic">p</span> &lt; 0.001 relative to untreated OVL1+2 cells and <span>$</span> <span class="html-italic">p</span> &lt; 0.02 relative to ABA-untreated SHL1+2 cells by unpaired <span class="html-italic">t</span>-test.</p>
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<p>Radicals-scavenging enzymes are increased in LANCL1/2-overexpressing vs. double-silenced H9c2 cells. (<b>A</b>) qRT-PCR analysis of the transcription of radicals-scavenging enzymes (SOD2 and GPX4) in cells overexpressing (OVL1+2) or silenced (SHL1+2) for LANCL1 and LANCL2 and incubated in the absence or in the presence of 100 nM ABA for 4 h. Results are expressed relative to control ABA-untreated PLV cells. ** <span class="html-italic">p</span> &lt;0.001 relative to untreated control cells (PLV or SCR) and <span>$</span> <span class="html-italic">p</span> &lt; 0.02 relative to ABA-untreated OVL1+2 or SHL1+2 cells by unpaired <span class="html-italic">t</span>-test. (<b>B</b>) Upper panel, a representative Western blot image of SOD2 and GPX4 in LANCL1/2-overexpressing or silenced cells, treated or not with 100 nM ABA for 4 h. Lower panel, histograms are the mean ± SD from at least three experiments. Results are expressed relative to ABA-untreated OVL1+2 cells. Values are normalized against vinculin, as a housekeeping protein. Data are ** <span class="html-italic">p</span> &lt; 0.001 relative to untreated OVL1+2 cells by unpaired <span class="html-italic">t</span>-test.</p>
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<p>LANCL1/2-overexpressing cells have a reduced ROS content compared with double-silenced H9c2 cells. (<b>A</b>) Intracellular ROS production measured by DCF fluorimetric analysis in H9c2 cells. Results are expressed as fluorescence intensity in arbitrary units. (<b>B</b>) Mitochondrial superoxide anions were detected by confocal microscopy on MitoSOX-loaded H9c2 overexpressing (OVL1+2) or silenced (SHL1+2) for LANCL1 and LANCL2 proteins and incubated in the absence or in the presence of 100 nM ABA for 4 h. Upper panel, representative images of the cells; lower panel, histograms represent the mean cell fluorescence recorded in three independent experiments, relative to ABA-untreated OVL1+2 cells. * <span class="html-italic">p</span> &lt;0.01 relative to untreated OVL1+2 cells and <span>$</span> <span class="html-italic">p</span> &lt; 0.02 relative to ABA-untreated SHL1+2 cells by unpaired <span class="html-italic">t</span>-test.</p>
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<p>ERRα silencing in LANCL1/2-overexpressing cells increases radicals-generating and decreases radicals-scavenging enzymes. (<b>A</b>) OVL1+2 cells were silenced for the expression of ERRα by lentiviral infection. Upper left panel, representative Western blot of ERRα in OVL1+2 cells silenced for the expression of ERRα (OVL1+2-SHERRα) or infected with the empty vector (OVL1+2-SCR); upper right panel, densitometric quantitation of ERRα in the same cell types. Values are normalized against vinculin, as a housekeeping protein; lower panel, ERRα mRNA levels relative to control OVL1+2-SCR cells in ERRα-silenced cells (OVL1+2-SHERRα). ** <span class="html-italic">p</span> &lt; 0.001 relative to control OVL1+2-SCR cells by unpaired <span class="html-italic">t</span>-test. (<b>B</b>) qRT-PCR analysis (upper panel) and Western blot analysis (lower panels) of radicals-generating enzymes (COX2, XO and NOX4) in OVL1+2 cells silenced for ERRα (OVL1+2-SHERRα) and incubated in the absence or in the presence of 100 nM ABA for 4 h. Results are expressed relative to ABA-untreated OVL1+2-SCR cells. ** <span class="html-italic">p</span> &lt;0.001 relative to ABA-untreated OVL1+2-SCR cells by unpaired <span class="html-italic">t</span>-test. (<b>C</b>) qRT-PCR analysis (upper panel) and Western blot analysis (lower panels) of radicals-scavenging enzymes (SOD2 and GPX4) in OVL1+2 cells silenced for ERRα (OVL1+2-SHERRα) and incubated in the absence or in the presence of 100 nM ABA for 4 h. Results are expressed relative to control ABA-untreated OVL1+2-SCR cells. ** <span class="html-italic">p</span> &lt;0.001 relative to untreated OVL1+2-SCR cells by unpaired <span class="html-italic">t</span>-test.</p>
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<p>ERRα silencing increases ROS production in LANCL1/2-overexpressing H9c2 cells. (<b>A</b>) Intracellular ROS production measured by DCF fluorimetric analysis in H9c2 cells. Results are expressed as fluorescence intensity in arbitrary units with respect to OVL1+2-SCR cells. (<b>B</b>) Mitochondrial superoxide anions were detected on MitoSOX-loaded cells by confocal microscopy in H9c2 cells overexpressing LANCL1 and LANCL2 proteins and silenced (OVL1+2-SHERRα) or not (OVL1+2) for ERRα and incubated in the absence or in the presence of 100 nM ABA for 4 h. Upper panel, representative confocal microscopy of the cells; lower panel, histogram summarized quantitative data of the mean ± SD of three independent experiments. * <span class="html-italic">p</span> &lt; 0.01 relative to untreated OVL1+2-SCR cells by unpaired <span class="html-italic">t</span>-test.</p>
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19 pages, 9897 KiB  
Article
Analysis of the Rice Raffinose Synthase (OsRS) Gene Family and Haplotype Diversity
by Jinguo Zhang, Dezhuang Meng, Jianfeng Li, Yaling Bao, Peng Yu, Guohui Dou, Jinmeng Guo, Chenghang Tang, Jiaqi Lv, Xinchen Wang, Xingmeng Wang, Fengcai Wu and Yingyao Shi
Int. J. Mol. Sci. 2024, 25(18), 9815; https://doi.org/10.3390/ijms25189815 - 11 Sep 2024
Viewed by 222
Abstract
Based on the genome information of rice (Nipponbare), this study screened and identified six raffinose synthase (RS) genes and analyzed their physical and chemical properties, phylogenetic relationship, conserved domains, promoter cis-acting elements, and the function and genetic diversity of the gene-CDS-haplotype (gcHap). The [...] Read more.
Based on the genome information of rice (Nipponbare), this study screened and identified six raffinose synthase (RS) genes and analyzed their physical and chemical properties, phylogenetic relationship, conserved domains, promoter cis-acting elements, and the function and genetic diversity of the gene-CDS-haplotype (gcHap). The results showed that these genes play key roles in abiotic stress response, such as OsRS5, whose expression in leaves changed significantly under high salt, drought, ABA, and MeJA treatments. In addition, the OsRS genes showed significant genetic variations in different rice populations. The main gcHaps of most OsRS loci had significant effects on key agronomic traits, and the frequency of these alleles varied significantly among different rice populations and subspecies. These findings provide direction for studying the RS gene family in other crops. Full article
(This article belongs to the Special Issue Advance in Plant Abiotic Stress: 2nd Edition)
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<p>Characteristics of OsRS genes. (<b>a</b>) Chromosomal localization of OsRS genes. (<b>b</b>) Phylogenetic trees of OsRS genes from Nipponbare (Os), <span class="html-italic">Glycine max (Gm)</span>, <span class="html-italic">Arabidopsis thaliana (At)</span>, <span class="html-italic">Setaria viridis (Sv)</span>, <span class="html-italic">Sorghum bicolor (Sb)</span> and <span class="html-italic">Cucumis sativus (Cs)</span>. (<b>c</b>) Phylogenetic tree, motif prediction, domain, and exon-intron distribution of OsRS genes from left to right.</p>
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<p>Characteristics of OsRS genes. (<b>a</b>) Chromosomal localization of OsRS genes. (<b>b</b>) Phylogenetic trees of OsRS genes from Nipponbare (Os), <span class="html-italic">Glycine max (Gm)</span>, <span class="html-italic">Arabidopsis thaliana (At)</span>, <span class="html-italic">Setaria viridis (Sv)</span>, <span class="html-italic">Sorghum bicolor (Sb)</span> and <span class="html-italic">Cucumis sativus (Cs)</span>. (<b>c</b>) Phylogenetic tree, motif prediction, domain, and exon-intron distribution of OsRS genes from left to right.</p>
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<p>Analysis of cis-acting elements of the OsRS gene. (<b>a</b>) Distribution and proportion of cis-acting elements in the promoter region of the OsRS gene; different colors represent different proportions. (<b>b</b>) Heatmap analysis of cis-acting elements in the promoter region of OsRS genes. In the heatmap, the numerical values represent the quantity of different cis-acting elements, with darker colors indicating higher quantities.</p>
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<p>Collinear relationship between OsRS genes and genes from other species. The collinear regions of the genome of rice (Nipponbare) and other species are represented by grey lines and collinear gene pairs by blue lines. <span class="html-italic">Oryza sativa</span> L. (Nipponbare) is represented by <span class="html-italic">Os</span>; <span class="html-italic">Glycine max</span> is represented by <span class="html-italic">Gm</span>; <span class="html-italic">Sorghum bicolor</span> is represented by <span class="html-italic">Sb</span>; <span class="html-italic">Setaria viridis</span> is denoted by <span class="html-italic">Sv</span>; and <span class="html-italic">Cucumis sativus</span> is represented by <span class="html-italic">Cs</span>.</p>
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<p>Homologous relationship and chromosomal localization of OsRS genes. The line indicates a homologous relationship.</p>
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<p>Analysis of OsRS gene expression. Color markers indicate changes in gene expression. Red indicates high expression, and green indicates low expression. (<b>a</b>) Expression of OsRS genes in the leaf, root, seedling, stem, flower, embryo, shoot, meristem, male reproductive tissue, female reproductive tissue, panicle, and seed. (<b>b</b>) Expression levels of OsRS genes in the root and shoot after drought stress. (<b>c</b>) Expression levels of OsRS genes in the stem and leaf of rice after high salt stress. (<b>d</b>) Expression levels of OsRS genes in the root and shoot after ABA hormone treatment. (<b>e</b>) Expression levels of OsRS genes in the root and shoot after MeJA treatment. (<b>f</b>) Expression levels of OsRS genes in the stem and leaf after heat treatment. (<b>g</b>) Expression levels of OsRS genes in the root and shoot after cold stress.</p>
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<p>Analysis of expression levels of five genes of the OsRS family under different treatments: (<b>a</b>) 100 μmol/L ABA treatment; (<b>b</b>) 100 μmol/L MeJA treatment; (<b>c</b>) 20% PEG6000 simulated drought stress. (<b>d</b>) 200 mmol/L NaCl simulated salt stress; (<b>e</b>) 42 °C heat treatment; (<b>f</b>) 6 °C cold treatment. Statistical analysis of the data was performed using WPS2023 software, and IBM SPSS Statistics 25 statistics analysis software was used to perform analysis of variance; the significance level was defined as **** <span class="html-italic">p</span> &lt; 0.0001, *** <span class="html-italic">p</span> &lt; 0.001, ** <span class="html-italic">p</span> &lt; 0.01, * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Genetic diversity index (<span class="html-italic">I<sub>Nei</sub></span>) of OsRS genes in pairwise comparison of different populations calculated from gcHap data.</p>
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<p>Haplotype networks of four cloned OsRS genes and four associated agronomic traits in 3KRG. The letters indicate differences between haplotypes assessed by two-factor ANOVA, where different letters on the boxplot indicate statistically significant differences at <span class="html-italic">p</span> &lt; 0.05 based on Duncan’s multirange test. The bar chart on the right shows the difference in frequency of major gcHaps between local varieties (LANs) and modern varieties (MVs) of <span class="html-italic">Xian</span> and <span class="html-italic">Geng</span>. A chi-square test was used to determine significant differences in the proportion of a gcHap between different populations **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Favorable gcHap frequencies of six OsRS genes affecting TGW, GL, GW, PL, and CN in Xian/indica (XI), Geng/japonica rice (GJ), and different rice subpopulations.”#accession”indicates the number of accessions that possess the favorable gcHap.Five subpopulations of XI (XI—1A, XI—1B, XI—2, XI—3, and XI—adm) and four subpopulations of GJ (temperate GJ: GJ—tmp, subtropical GJ: GJ—sbtrp, tropical GJ: GJ—trp, and GJ—adm).</p>
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22 pages, 2456 KiB  
Review
Mechanism of Rice Resistance to Bacterial Leaf Blight via Phytohormones
by Qianqian Zhong, Yuqing Xu and Yuchun Rao
Plants 2024, 13(18), 2541; https://doi.org/10.3390/plants13182541 - 10 Sep 2024
Viewed by 318
Abstract
Rice is one of the most important food crops in the world, and its yield restricts global food security. However, various diseases and pests of rice pose a great threat to food security. Among them, bacterial leaf blight (BLB) caused by Xanthomonas oryzae [...] Read more.
Rice is one of the most important food crops in the world, and its yield restricts global food security. However, various diseases and pests of rice pose a great threat to food security. Among them, bacterial leaf blight (BLB) caused by Xanthomonas oryzae pv. oryzae (Xoo) is one of the most serious bacterial diseases affecting rice globally, creating an increasingly urgent need for research in breeding resistant varieties. Phytohormones are widely involved in disease resistance, such as auxin, abscisic acid (ABA), ethylene (ET), jasmonic acid (JA), and salicylic acid (SA). In recent years, breakthroughs have been made in the analysis of their regulatory mechanism in BLB resistance in rice. In this review, a series of achievements of phytohormones in rice BLB resistance in recent years were summarized, the genes involved and their signaling pathways were reviewed, and a breeding strategy combining the phytohormones regulation network with modern breeding techniques was proposed, with the intention of applying this strategy to molecular breeding work and playing a reference role for how to further improve rice resistance. Full article
(This article belongs to the Special Issue Rice Genetics and Molecular Design Breeding)
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<p>The process of <span class="html-italic">Xoo</span> infection of rice.</p>
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<p>Phytohormone-mediated BLB resistance genes and the crosstalk network between phytohormones. ABA: abscisic acid; ET: ethylene; JA: jasmonic acid; SA: salicylic acid.</p>
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<p>Phytohormones regulatory network in response to BLB combined with modern technology for breeding strategy. MAS: marker-assisted selection; GMB: genetically modified breeding; ABA: abscisic acid; ET: ethylene; JA: jasmonic acid; SA: salicylic acid; P: plasmid; A: agrobacterium.</p>
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14 pages, 3382 KiB  
Article
Characterization of the Regulatory Network under Waterlogging Stress in Soybean Roots via Transcriptome Analysis
by Yo-Han Yoo, Seung-Yeon Cho, Inhye Lee, Namgeol Kim, Seuk-Ki Lee, Kwang-Soo Cho, Eun Young Kim, Ki-Hong Jung and Woo-Jong Hong
Plants 2024, 13(18), 2538; https://doi.org/10.3390/plants13182538 - 10 Sep 2024
Viewed by 292
Abstract
Flooding stress caused by climate change is a serious threat to crop productivity. To enhance our understanding of flooding stress in soybean, we analyzed the transcriptome of the roots of soybean plants after waterlogging treatment for 10 days at the V2 growth stage. [...] Read more.
Flooding stress caused by climate change is a serious threat to crop productivity. To enhance our understanding of flooding stress in soybean, we analyzed the transcriptome of the roots of soybean plants after waterlogging treatment for 10 days at the V2 growth stage. Through RNA sequencing analysis, 870 upregulated and 1129 downregulated differentially expressed genes (DEGs) were identified and characterized using Gene Ontology (GO) and MapMan software (version 3.6.0RC1). In the functional classification analysis, “alcohol biosynthetic process” was the most significantly enriched GO term in downregulated DEGs, and phytohormone-related genes such as ABA, cytokinin, and gibberellin were upregulated. Among the transcription factors (TFs) in DEGs, AP2/ERFs were the most abundant. Furthermore, our DEGs encompassed eight soybean orthologs from Arabidopsis and rice, such as 1-aminocyclopropane-1-carboxylate oxidase. Along with a co-functional network consisting of the TF and orthologs, the expression changes of those genes were tested in a waterlogging-resistant cultivar, PI567343. These findings contribute to the identification of candidate genes for waterlogging tolerance in soybean, which can enhance our understanding of waterlogging tolerance. Full article
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<p>Physiological responses of soybean roots to waterlogging stress and heat-map of differentially expressed genes. Plants at the V2 stage were exposed to waterlogging stress for 10 days and allowed to recover for 15 days (<b>A</b>–<b>C</b>). Each of the images indicates before waterlogging (<b>A</b>), after 10 days of waterlogging (<b>B</b>), and 15 days of recovery (<b>C</b>). Scale bar = 5 cm. N = 3 for (<b>A</b>–<b>C</b>). Quantitative data of the root length (<b>D</b>) and the number of adventitious roots (<b>E</b>) are illustrated. Genes differentially expressed in roots during waterlogging stress were identified (<b>F</b>). Processing RNA-seq data under the criteria of FPKM &gt; 4, <span class="html-italic">p</span>-value &lt; 0.05, and |log2(fold change)| over 2 for roots exposed to waterlogging vs. mock-treated soybean roots (control) identified 1999 differentially expressed genes (DEGs). In the left panel, red indicates upregulation in the waterlogging/control comparison and green indicates downregulation in the waterlogging/control comparison. The right panel shows the average normalized FPKM values from RNA-seq experiments; blue indicates the lowest expression level and yellow indicates the highest level. Detailed data for the RNA-seq analysis are presented in <a href="#app1-plants-13-02538" class="html-app">Supplementary Table S2</a>.</p>
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<p>Gene Ontology (GO) enrichment analysis in the “biological process” category for genes up- and downregulated in response to waterlogging. Overall, 21 GO terms were highly over-represented, and in the downregulated gene group, 15 GO terms were significantly enriched (<span class="html-italic">p</span> &lt; 0.05 and fold-enrichment values of &gt;2 log2-fold). Details of the GO assignments are presented in <a href="#app1-plants-13-02538" class="html-app">Supplementary Table S3</a>.</p>
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<p>MapMan analysis of genes associated with the response to waterlogging. Overviews: (<b>A</b>) metabolism; (<b>B</b>) cellular response. Red and blue boxes indicate up- and downregulated genes, respectively; green boxes highlight the pathways related to waterlogging stress response. Detailed information is presented in <a href="#app1-plants-13-02538" class="html-app">Supplementary Table S4</a>.</p>
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<p>Expression analysis of major transcription factors in DEGs. Among the transcription factors, the number of upregulated genes is presented in light gray, and the number of downregulated genes is presented in dark gray (<b>A</b>). The expression of four randomly selected genes from the up- or downregulated genes was compared (<b>B</b>). C: control (light gray), WL: waterlogging (dark gray). *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Construction and expression validation of co-functional networks associated with transcription factors regulated under waterlogging stress. (<b>A</b>) Co-functional network consists of various transcription factors retrieved from SoyNet. There are 19 AP2/ERF (white nodes), 1 bHLH (pink node), 1 bZIP (orange node), 4 C2H2 (ZF, yellow nodes), 14 NAC (green nodes), 9 WRKY (light blue nodes), 9 MYB (dark blue nodes), and 2 ortholog (gray nodes) in the network. Circular nodes represent upregulated genes; square nodes represent downregulated genes. The raw networks initially created in SoyNet are shown in <a href="#app1-plants-13-02538" class="html-app">Supplementary Figure S2</a>. (<b>B</b>) The expression of nine TF genes was significantly upregulated in a resistant variety, PI 567343, than in Daewonkong under waterlogging stress, as shown by qRT-PCR. The expression levels were normalized to that of <span class="html-italic">Act11</span> using a real-time polymerase chain reaction. DW: Daewonkong (light gray), PI 567343: waterlogging-resistant varieties (dark gray). ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001.</p>
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14 pages, 2839 KiB  
Article
OsMBF1a Facilitates Seed Germination by Regulating Biosynthesis of Gibberellic Acid and Abscisic Acid in Rice
by Xin Wang, Ziyun Chen, Jinghua Guo, Xiao Han, Xujian Ji, Meicheng Ke, Feng Yu and Pingfang Yang
Int. J. Mol. Sci. 2024, 25(18), 9762; https://doi.org/10.3390/ijms25189762 - 10 Sep 2024
Viewed by 340
Abstract
Seed germination is a pivotal stage in the plant life cycle, orchestrated by a myriad of internal and external factors, notably plant hormones. The underlying molecular mechanisms governing rice seed germination remain largely unelucidated. Herein, we uncover OsMBF1a as a crucial regulatory factor [...] Read more.
Seed germination is a pivotal stage in the plant life cycle, orchestrated by a myriad of internal and external factors, notably plant hormones. The underlying molecular mechanisms governing rice seed germination remain largely unelucidated. Herein, we uncover OsMBF1a as a crucial regulatory factor that employs a dual strategy to promote seed germination: positively activating genes involved in gibberellin (GA) biosynthesis pathways, while negatively regulating key genes responsible for abscisic acid (ABA) synthesis. Furthermore, OsMBF1a modulates the endogenous levels of ABA and GA in rice seeds, reinforcing its central role in the germination process. The expression of ZmMBF1a and ZmMBF1b, the homologous genes in maize, in rice seeds similarly affects germination, indicating the conserved functionality of MBF1 family genes in regulating seed germination. This study provides novel insights into the molecular mechanisms underlying rice seed germination and underscores the significance of MBF1 family genes in plant growth and development. Full article
(This article belongs to the Section Molecular Plant Sciences)
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<p><span class="html-italic">OsMBF1a</span> expression profiles in rice tissues and during seed germination. (<b>a</b>) Relative expression levels of the <span class="html-italic">OsMBF1a</span> gene in various tissues of Nipponbare (NIP) rice, including stamen, pistil, seed, embryo, leaf, root, and stem, as determined by real-time quantitative reverse transcript PCR (RT-qPCR). (<b>b</b>) Expression dynamics of <span class="html-italic">OsMBF1a</span> throughout the germination process of NIP seeds, assessed using RT-qPCR. Data are presented as mean ± standard deviation (SD) from three biological replicates.</p>
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<p>Phenotypic analysis of seed germination in <span class="html-italic">OsMBF1a</span> transgenic rice lines. (<b>a</b>) Sequences of <span class="html-italic">OsMBF1a</span> mutants under the Nipponbare (NIP) rice background, including wild-type (WT) NIP and two mutants, <span class="html-italic">OsMBF1a</span>-1 and <span class="html-italic">OsMBF1a</span>-2, with mutations in the first exon, chromatogram, and amino acid sequences. In the gene map, the green squares represent domains. The CRISPR/Cas9 target sites are represented by arrows. In the chromatogram, the red curve represents base T, the green curve represents base A, the blue curve represents base C, and the black curve represents base G. (<b>b</b>) Relative expression levels of <span class="html-italic">OsMBF1a</span> in NIP and two independent overexpression lines, as measured by RT-qPCR. (<b>c</b>) Morphological comparison of germinated seeds for NIP and transgenic lines over 1, 2, 3, and 5 day post-germination. (<b>d</b>) Percentage germination rate of NIP and transgenic lines over a six-day period. (<b>e</b>) Shoot length of NIP and transgenic lines after six days of germination. Data are presented as mean ± standard deviation (SD) from three biological replicates. Statistical significance was determined using a <span class="html-italic">t</span>-test. * indicates <span class="html-italic">p</span> &lt; 0.05, ** indicates <span class="html-italic">p</span> &lt; 0.01, and *** indicates <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Expression network regulated by <span class="html-italic">OsMBF1a</span>. (<b>a</b>) Volcano plot illustrating differentially expressed genes (DEGs) in plants overexpressing <span class="html-italic">OsMBF1a</span>. (<b>b</b>) Gene Ontology analysis highlighting the enriched biological processes among the DEGs. (<b>c</b>) Schematic representation of the biosynthesis pathway of gibberellin (GA) and abscisic acid (ABA) from geranylgeranyl pyrophosphate (GGPP), with gene expression levels depicted in a heatmap derived from transcriptome data. (<b>d</b>) Expression levels of genes involved in GA and ABA biosynthesis in germinated seeds (0 and 24 h) of <span class="html-italic">OsMBF1a</span> overexpression plants compared to NIP, as determined by transcriptome analysis. (<b>e</b>) Schemes showed the genomic regions of the CTAGA motif of GA and ABA biosynthesis genes in DAP-qPCR. Stars represent the potential binding motifs of <span class="html-italic">OsMBF1a</span>. (<b>f</b>) DAP-qPCR analysis of OsMBF1a association with the promoters of the tested genes. The fold enrichment was calculated as bound/input by normalization with IgG control set as 1. <span class="html-italic">Actin</span> was used as a negative control. The experiments were repeated three times independently, with similar results. Data are presented as mean ± standard deviation (SD) from three biological replicates. Statistical significance was determined using a <span class="html-italic">t</span>-test. * indicates <span class="html-italic">p</span> &lt; 0.05, ** indicates <span class="html-italic">p</span> &lt; 0.01, and *** indicates <span class="html-italic">p</span> &lt; 0.001. NIP, Nipponbare. ACT, actin, ns denotes no significant difference.</p>
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<p>Quantification of ABA and GA content in <span class="html-italic">OsMBF1a</span> transgenic rice lines and Nipponbare (NIP). (<b>a</b>) Linear regression analysis depicting the relationship between the standard concentration of gibberellin 3 (GA3) and its peak area. (<b>b</b>) Linear regression analysis showing the relationship between the standard concentration of abscisic acid (ABA) and its peak area. (<b>c</b>) GA3 content measured in seeds of <span class="html-italic">OsMBF1a</span> transgenic lines at various germination stages. (<b>d</b>) Endogenous ABA content in seeds of <span class="html-italic">OsMBF1a</span> transgenic lines at different germination stages. Data are presented as mean ± standard deviation (SD) from three biological replicates. Statistical significance was determined using a <span class="html-italic">t</span>-test. * indicates <span class="html-italic">p</span> &lt; 0.05, ** indicates <span class="html-italic">p</span> &lt; 0.01, and ns indicates no significant.</p>
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<p>Seed germination phenotypes in <span class="html-italic">ZmMBF1a</span> and <span class="html-italic">ZmMBF1b</span> transgenic rice lines. (<b>a</b>) Comparative images of germinated seeds for NIP (wild-type) and transgenic lines over a period of 1, 2, 3, and 5 days, illustrating the developmental progression post-germination. (<b>b</b>) Cumulative germination rate of NIP and transgenic lines, measured daily for a duration of six days, highlighting the temporal dynamics of seed germination. (<b>c</b>) Quantitative assessment of shoot length for NIP and transgenic lines at the six-day mark post-germination. Data are presented as mean ± standard deviation (SD) from three biological replicates. Statistical significance was determined using a <span class="html-italic">t</span>-test. * indicates <span class="html-italic">p</span> &lt; 0.05, ** indicates <span class="html-italic">p</span> &lt; 0.01, and *** indicates <span class="html-italic">p</span> &lt; 0.001. NIP, Nipponbare; <span class="html-italic">ZmMBF1a</span>-OE1 and <span class="html-italic">ZmMBF1a</span>-OE2, two independent transgenic lines that overexpressed <span class="html-italic">ZmMBF1a</span>; <span class="html-italic">ZmMBF1b</span>-OE1 and <span class="html-italic">ZmMBF1b</span>-OE2, two independent transgenic lines that overexpressed <span class="html-italic">ZmMBF1b</span>.</p>
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<p>Expression and hormone content analysis in <span class="html-italic">ZmMBF1a</span> and <span class="html-italic">ZmMBF1b</span> transgenic lines. (<b>a</b>) Expression levels of genes involved in GA and ABA biosynthesis in transgenic lines overexpressing <span class="html-italic">ZmMBF1a</span> and <span class="html-italic">ZmMBF1b</span>. (<b>b</b>) Quantitative analysis of GA3 content in seeds of transgenic lines overexpressing <span class="html-italic">ZmMBF1a</span> and <span class="html-italic">ZmMBF1b</span> during various germination stages. (<b>c</b>) Measurement of endogenous ABA content in seeds of the same transgenic lines at different germination stages. Data are presented as mean ± standard deviation (SD) from three biological replicates. Statistical significance was determined using a <span class="html-italic">t</span>-test. * indicates <span class="html-italic">p</span> &lt; 0.05, ** indicates <span class="html-italic">p</span> &lt; 0.01, *** indicates <span class="html-italic">p</span> &lt; 0.001, and ns denotes no significant difference.</p>
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14 pages, 2675 KiB  
Article
Management of Spartina alterniflora: Assessing the Efficacy of Plant Growth Regulators on Ecological and Microbial Dynamics
by Chenyan Sha, Zhixiong Wang, Jiajie Cao, Jing Chen, Cheng Shen, Jing Zhang, Qiang Wang and Min Wang
Sustainability 2024, 16(17), 7848; https://doi.org/10.3390/su16177848 - 9 Sep 2024
Viewed by 354
Abstract
Spartina alterniflora is recognized as one of the most detrimental invasive species along China’s coastlines, highlighting the need for effective and environmentally safe management strategies to preserve intertidal zones. This study assessed the effectiveness of combining plant growth regulators (PRGs) with physical cutting [...] Read more.
Spartina alterniflora is recognized as one of the most detrimental invasive species along China’s coastlines, highlighting the need for effective and environmentally safe management strategies to preserve intertidal zones. This study assessed the effectiveness of combining plant growth regulators (PRGs) with physical cutting to manage S. alterniflora, using 16S rRNA and 18S rRNA gene sequencing to evaluate the impacts on the plant and associated soil micro-organisms. The results showed that compared to the control (CK), the regeneration numbers for treatments with abscisic acid (ABA), gibberellin (GA), paclobutrazol (PP333), garcinol (GC), and glyphosate (GP) decreased by 29.75%, 23.25%, 15.75%, 94.50%, and 40.50%, respectively. Comparative analysis revealed no statistically significant variation in the inhibitory effects of ABA and GP on the germination of S. alterniflora (p > 0.05). Additionally, applying PRGs and herbicides increased the diversity indices of soil bacteria and fungi. Principal Coordinates Analysis (PCoA) showed that the impact of PRGs on the fungal community was less pronounced than that of herbicides. Significant differences were also noted in the abundance of microbial functional genes related to methanotrophy, hydrocarbon degradation, and denitrification compared to the control (p < 0.05). This study aimed to assess the potential of PRGs in controlling the invasion of S. alterniflora and to elucidate their impacts on soil microbial communities and functional gene expression. Full article
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Figure 1

Figure 1
<p>The experimental site of this study. Note: This map provides a comprehensive geographical reference for the experimental area, with the experimental sites marked by <span class="html-fig-inline" id="sustainability-16-07848-i001"><img alt="Sustainability 16 07848 i001" src="/sustainability/sustainability-16-07848/article_deploy/html/images/sustainability-16-07848-i001.png"/></span> to ensure precise location identification for the study.</p>
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<p>Germination number of <span class="html-italic">S. alterniflora</span> treated with mowing and different PRGs/herbicides in the next year. Note: Different lowercase letters indicate significant differences in the number of regenerated <span class="html-italic">S. alterniflora</span> among different treatments (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Post-treatment comparative analysis of the relative abundance of dominant microbial taxa at the phylum and genus levels within the microbial communities. (<b>a</b>) Comparative abundance of bacterial phyla among the microbial communities; (<b>b</b>) comparative abundance of fungal phyla among the microbial communities; (<b>c</b>) comparative abundance of bacterial genera among the microbial communities; (<b>d</b>) comparative abundance of fungal genera among the microbial communities.</p>
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<p>Weighted UniFrac distances were employed to perform Principal Coordinates Analysis (PCoA), illustrating the variations in the soil microbiome’s bacterial. (<b>a</b>) Bacterial community; (<b>b</b>) fungal community.</p>
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<p>Functional gene prediction map (<b>a</b>) Mantel test diagram between functional genes and species; (<b>b</b>) bubble map of bacteria functional gene abundance; (<b>c</b>) bubble map of fungal functional gene abundance.</p>
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