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14 pages, 6742 KiB  
Article
Exploring Functional Gene XsPDAT1’s Involvement in Xanthoceras sorbifolium Oil Synthesis and Its Acclimation to Cold Stress
by Juan Wang, Hongqian Ren, Zetao Shi, Fesobi Olumide Phillip, Sisi Liu, Weiyang Zhang, Xingqiang Wang, Xueping Bao and Jinping Guo
Forests 2024, 15(10), 1822; https://doi.org/10.3390/f15101822 (registering DOI) - 18 Oct 2024
Abstract
Phospholipid: diacylglycerol acyltransferase (PDAT) is crucial in triacylglycerol (TAG) synthesis as it represents the final rate-limiting step of the acyl-CoA-independent acylation reaction. PDAT not only regulates lipid synthesis in plants, but also plays an important function in improving stress tolerance. In this study, [...] Read more.
Phospholipid: diacylglycerol acyltransferase (PDAT) is crucial in triacylglycerol (TAG) synthesis as it represents the final rate-limiting step of the acyl-CoA-independent acylation reaction. PDAT not only regulates lipid synthesis in plants, but also plays an important function in improving stress tolerance. In this study, the full-length coding sequence (CDS) of XsPDAT1, totaling 2022 base pairs and encoding 673 amino acids, was cloned from Xanthoceras sorbifolium. The relative expression of XsPDAT1 was significantly and positively correlated with oil accumulation during seed kernel development; there were some differences in the expression patterns under different abiotic stresses. Transgenic Arabidopsis thaliana plants overexpressing XsPDAT1 were obtained using the Agrobacterium-mediated method. Under low-temperature stress, the transgenic plants exhibited a smaller decrease in chlorophyll content, a smaller increase in relative conductivity, and a larger increase in POD enzyme activity and proline content in the leaves compared with the wild type. Additionally, lipid composition analysis revealed a significant increase in unsaturated fatty acids, such as oleic (C18:1) and linoleic (C18:2), in the seeds of transgenic plants compared to the wild type. These results suggest that XsPDAT1 plays a dual role in regulating the ratio of fatty acid composition and low-temperature stress in plants. Full article
(This article belongs to the Special Issue Abiotic and Biotic Stress Responses in Trees Species)
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Figure 1

Figure 1
<p>(<b>a</b>) Amino acid sequence alignments of PDAT1s in different plants. Black parts are highly homologous regions, grey parts are partially homologous regions, white parts are variable regions, and the regions between the red dotted lines are conserved structural domains of the lecithin-acyltransferase superfamily, and * represents an interval of ten amino acids. (<b>b</b>) Phylogenetic tree of PDATs from <span class="html-italic">X. sorbifolium</span> and other plants. (<b>c</b>) Predicted tertiary structure of XsPDAT1 protein.</p>
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<p>Expression pattern of <span class="html-italic">XsPDAT1</span> gene. (<b>a</b>) The oil content in different developmental stages of the seed kernel (46 d, 54 d, 62 d, and 78 d after anthesis). (<b>b</b>) Expression levels of <span class="html-italic">XsPDAT1</span> gene in different developmental stages of the seed kernel (46 d, 54 d, 62 d, and 78 d after anthesis). (<b>c</b>) Oil accumulation correlated with the expressions of <span class="html-italic">XsPDAT1</span>. (<b>d</b>) Expression levels of <span class="html-italic">XsPDAT1</span> before and after different abiotic stresses. Note: different lowercase letters in the figure indicate significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Genetic transformation and screening of <span class="html-italic">XsPDAT1</span>-overexpressing <span class="html-italic">A. thaliana</span>. (<b>a</b>) The transgenic process of <span class="html-italic">A. thaliana</span> with the floral-dip method, including infestation, seed harvest, screening, and transplantation. (<b>b</b>) Relative expression levels of <span class="html-italic">XsPDAT1</span> in the transgenic <span class="html-italic">A. thaliana</span> lines (OE-1–OE-7). Note: different lowercase letters in the figure indicate significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Morphology analysis of the wild-type (WT) and transgenic <span class="html-italic">A. thaliana</span> lines (OE-2, OE-4 and OE-7). (<b>a</b>) Phenotypes of leaf and fruit pod. (<b>b</b>) Phenotypic indexes, including leaf area, leaf circumference, leaf length, leaf width, plant height, and fruit pod length. Note: different lowercase letters in the figure indicate significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Changes in morphology and chlorophyll content of the wild-type (WT) and transgenic <span class="html-italic">A. thaliana</span> lines (OE-2, OE-4, OE-7) under low-temperature stress for 0 h, 24 h, and 48 h. (<b>a</b>) Morphology of all the lines under different treatments. (<b>b</b>) Total chlorophyll content of all <span class="html-italic">A. thaliana</span> lines under different treatments. (<b>c</b>) Chlorophyll a content of all <span class="html-italic">A. thaliana</span> lines under different treatments. (<b>d</b>) Chlorophyll b content of all <span class="html-italic">A. thaliana</span> lines under different treatments. Note: different lowercase letters in the figure indicate significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Analysis of physiological indexes of wild-type (WT) and transgenic <span class="html-italic">A. thaliana</span> lines (OE-2, OE-4, OE-7) under low-temperature stress for 0 h, 24 h, and 48 h. (<b>a</b>) POD activity of all <span class="html-italic">A. thaliana</span> lines under different treatments. (<b>b</b>) Free proline content of all <span class="html-italic">A. thaliana</span> lines under different treatments. (<b>c</b>) Relative conductivity of all <span class="html-italic">A. thaliana</span> lines under different treatments. Note: different lowercase letters in the figure indicate significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Fatty acid compositions of wild-type (WT) and transgenic <span class="html-italic">A. thaliana</span> lines (OE-2, OE-4, OE-7), including palmitic acid (C16:0), palmitoleic acid (C16:1), stearic acid (C18:0), oleic acid (C18:1), linoleic acid (C18:2), linolenic acid (C18:3), arachidic acid (C20:0), and eicosenoic acid (C20:1). Note: different lowercase letters in the figure indicate significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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13 pages, 4004 KiB  
Essay
Genome-Wide Identification and Expression Analysis of the PsTPS Gene Family in Pisum sativum
by Hao Yuan, Baoxia Liu, Guwen Zhang, Zhijuan Feng, Bin Wang, Yuanpeng Bu, Yu Xu, Zhihong Sun, Na Liu and Yaming Gong
Horticulturae 2024, 10(10), 1104; https://doi.org/10.3390/horticulturae10101104 (registering DOI) - 18 Oct 2024
Abstract
This study aimed to explore the role of the trehalose-6-phosphate synthase (TPS) gene family in the adaptation of peas to environmental stress. A comprehensive analysis of the PsTPS gene family identified 20 genes with conserved domains and specific chromosomal locations. Phylogenetic [...] Read more.
This study aimed to explore the role of the trehalose-6-phosphate synthase (TPS) gene family in the adaptation of peas to environmental stress. A comprehensive analysis of the PsTPS gene family identified 20 genes with conserved domains and specific chromosomal locations. Phylogenetic analysis delineated evolutionary relationships, while gene structure analysis revealed compositional insights, and motif analysis provided functional insights. Cis-regulatory element identification predicted gene regulation patterns. Tissue-specific and stress-induced expression profiling highlighted eight genes with ubiquitous expression, with PsTPS15 and PsTPS18 displaying elevated expression levels in roots, nodules, and young stems, and PsTPS13 and PsTPS19 expression downregulated in seeds. Transcriptome analysis identified a differential expression of 20 PsTPS genes, highlighting the significance of 14 genes in response to drought and salinity stress. Notably, under drought conditions, the expression of PsTPS4 and PsTPS6 was initially upregulated and then downregulated, whereas that of PsTPS15 and PsTPS19 was downregulated. Salinity stress notably altered the expression of PsTPS4, PsTPS6, and PsTPS19. Taken together, these findings elucidate the regulatory mechanisms of the PsTPS gene family and their potential as genetic targets for enhancing crop stress tolerance. Full article
(This article belongs to the Section Genetics, Genomics, Breeding, and Biotechnology (G2B2))
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<p>Chromosomal locations of the <span class="html-italic">PsTPS</span> genes on the seven pea chromosomes. The distribution of <span class="html-italic">PsTPS</span> genes is relatively sparse, and they are not distributed on every chromosome. The highest distribution of <span class="html-italic">PsTPS</span> genes is observed on Chr5, which contains seven genes.</p>
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<p>Phylogenetic tree incorporating TPS proteins from <span class="html-italic">Pisum sativum</span> L, <span class="html-italic">Arabidopsis</span>, and <span class="html-italic">Glycine max</span>. The tree of the <span class="html-italic">TPS</span> gene family was constructed by the IQ-TREE 2 software (Version 2.2.0) using the maximum likelihood (ML) method with 1000 bootstrap replicates. The color of the outer ring and branches denote <span class="html-italic">TPS</span> subfamilies.</p>
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<p>The phylogenetic relationship, conserved motifs, and gene structure of <span class="html-italic">PsTPSs</span>. (<b>a</b>) The maximum likelihood (ML) phylogenetic tree of PsTPS proteins was constructed using a full-length sequence with 1000 bootstrap replicates; (<b>b</b>) Distribution of conserved motifs in PsTPS proteins. A total of 10 motifs were predicted, and the scale bar represents 100 aa; (<b>c</b>) Distribution of the TPS domain in PsTPSs; (<b>d</b>) The gene structures of <span class="html-italic">PsTPSs</span>, including introns (black lines) and exons (green rectangles). The scale bar indicates 1000 bp.</p>
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<p>CREs on the putative promoters of <span class="html-italic">PsTPSs</span>. (<b>a</b>) Distribution of CREs identified in the 2000 bp upstream promoter region of <span class="html-italic">PsTPSs</span>; (<b>b</b>) The number of CREs on the putative promoters of <span class="html-italic">PsTPSs</span>. Numbers in the heatmap represent the number of elements.</p>
Full article ">Figure 5
<p>Syntenic analyses of <span class="html-italic">TPS</span> genes in <span class="html-italic">Pisum sativum</span>, <span class="html-italic">Arabidopsis</span>, <span class="html-italic">G. max</span>. (<b>a</b>) Seven chromosomes from <span class="html-italic">Pisum sativum</span> (Ps1–Ps7) are mapped, with chromosome length expressed as Mb. Lines denote syntenic <span class="html-italic">TPS</span> gene pairs on the chromosomes. (<b>b</b>) The seven chromosomes of <span class="html-italic">Pisum sativum</span> (Ps1–7), five chromosomes of <span class="html-italic">A. thaliana</span> (At1–5), and twenty chromosomes of <span class="html-italic">G. max</span> (Gm1–20) are mapped. Lines represent syntenic <span class="html-italic">TPS</span> gene pairs.</p>
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<p>Predicted protein–protein interaction networks of PsTPS proteins with other proteins using the STRING tool. Interactions between proteins are represented by gray lines.</p>
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<p>Expression profiles of the eight <span class="html-italic">PsTPS</span> genes. The color scale from blue to red indicates increasing log2-transformed FPKM values.</p>
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<p>Transcriptome analysis depicting the expression levels of 14 <span class="html-italic">PsTPS</span> genes in <span class="html-italic">Pisum sativum</span> under drought stress conditions induced by 10%, 20%, and 30% PEG6000 and salt stress induced by 100 mM, 200 mM, and 300 mM NaCl. Each experiment was conducted independently with a minimum of three replicates. “CK_0h” denotes the control group.</p>
Full article ">
19 pages, 508 KiB  
Article
Effect of Using Prickly Pear Seed Cake (Opuntia ficus indica L.) on Growth Performance, Digestibility, Physiological and Histometric Parameters in Rabbits
by Nadia Benali, Rafik Belabbas, Mounira Sais, Hacina AinBaziz, Baya Djellout, Fatima Nouara Ettouahria, Nadira Oulebsir, Gabriele Brecchia, Alda Quattrone, Giulio Curone and Laura Menchetti
Vet. Sci. 2024, 11(10), 513; https://doi.org/10.3390/vetsci11100513 - 17 Oct 2024
Abstract
Prickly pear (Opuntia ficus indica L.) could be used in rabbit nutrition in compliance with circular economy principles, global warming issues, and reduction of production costs. This study aims to evaluate the effects of dietary incorporation of prickly pear seed cake [...] Read more.
Prickly pear (Opuntia ficus indica L.) could be used in rabbit nutrition in compliance with circular economy principles, global warming issues, and reduction of production costs. This study aims to evaluate the effects of dietary incorporation of prickly pear seed cake (PPSC) on growth, physiological, and histometric parameters in rabbits. A total of 105 rabbits were divided into three experimental groups (n = 35) and fed different diets: a commercial feed (C group), the same feed with alfalfa replaced by PPSC at 10% (10PP group), and at 20% (20PP group). They were group-housed in cages with 5 animals per cage from weaning until slaughtering. While body weights and weight gains were similar in all groups, the coefficients of nutrient digestibility of dry matter, fibers, and ashes, as well as the characteristics of intestinal villi, were improved in the 10PP group compared to the others (p < 0.05). The 20PP group showed a reduction in perirenal and interscapular fat (p < 0.05), as well as lower plasma concentrations of triglycerides and cholesterol compared to the C group (p < 0.001). In conclusion, PPSC can be incorporated into the diets of growing rabbits up to 20% as a partial substitute for alfalfa without the impairment of growth performance. Additionally, the inclusion of PPSC enhanced nutrient digestibility and increased the intestinal absorption surface area. Full article
(This article belongs to the Section Veterinary Physiology, Pharmacology, and Toxicology)
21 pages, 1553 KiB  
Article
Selection and Effect of Plant Growth-Promoting Bacteria on Pine Seedlings (Pinus montezumae and Pinus patula)
by Francisco David Moreno-Valencia, Miguel Ángel Plascencia-Espinosa, Yolanda Elizabeth Morales-García and Jesús Muñoz-Rojas
Life 2024, 14(10), 1320; https://doi.org/10.3390/life14101320 - 17 Oct 2024
Abstract
Forest cover is deteriorating rapidly due to anthropogenic causes, making its restoration urgent. Plant growth-promoting bacteria (PGPB) could offer a viable solution to ensure successful reforestation efforts. This study aimed to select bacterial strains with mechanisms that promote plant growth and enhance seedling [...] Read more.
Forest cover is deteriorating rapidly due to anthropogenic causes, making its restoration urgent. Plant growth-promoting bacteria (PGPB) could offer a viable solution to ensure successful reforestation efforts. This study aimed to select bacterial strains with mechanisms that promote plant growth and enhance seedling development. The bacterial strains used in this study were isolated from the rhizosphere and endophyte regions of Pinus montezumae Lamb. and Pinus patula Schl. et Cham., two Mexican conifer species commonly used for reforestation purposes. Sixteen bacterial strains were selected for their ability to produce auxins, chitinase, and siderophores, perform nitrogen fixation, and solubilize inorganic phosphates; they also harbored genes encoding antimicrobial production and ACC deaminase. The adhesion to seeds, germination rate, and seedling response of P. montezumae and P. patula were performed following inoculation with 10 bacterial strains exhibiting high plant growth-promoting potential. Some strains demonstrated the capacity to enhance seedling growth. The selected strains were taxonomically characterized and belonged to the genus Serratia, Buttiauxella, and Bacillus. These strains exhibited at least two mechanisms of action, including the production of indole-3-acetic acid, biological nitrogen fixation, and phosphate solubilization, and could serve as potential alternatives for the reforestation of affected areas. Full article
(This article belongs to the Collection Feature Papers in Microbiology)
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<p>Phylogenetic tree based on 16S rDNA gene sequencing, the evolutionary relationship between the 10 growth-promoting isolates inferred using the Phylogeny platform is observed. Evolutionary distances were computed using the maximum likelihood method.</p>
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<p>Evaluation of stem elongation in <span class="html-italic">P. montezumae</span> and <span class="html-italic">P. patula</span> seedlings after treatment with growth-promoting strains.</p>
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13 pages, 9828 KiB  
Article
Examining Carotenoid Metabolism Regulation and Its Role in Flower Color Variation in Brassica rapa L.
by Guomei Liu, Liuyan Luo, Lin Yao, Chen Wang, Xuan Sun and Chunfang Du
Int. J. Mol. Sci. 2024, 25(20), 11164; https://doi.org/10.3390/ijms252011164 - 17 Oct 2024
Abstract
Carotenoids are vital organic pigments that determine the color of flowers, roots, and fruits in plants, imparting them yellow, orange, and red hues. This study comprehensively analyzes carotenoid accumulation in different tissues of the Brassica rapa mutant “YB1”, which exhibits altered flower and [...] Read more.
Carotenoids are vital organic pigments that determine the color of flowers, roots, and fruits in plants, imparting them yellow, orange, and red hues. This study comprehensively analyzes carotenoid accumulation in different tissues of the Brassica rapa mutant “YB1”, which exhibits altered flower and root colors. Integrating physiological and biochemical assessments, transcriptome profiling, and quantitative metabolomics, we examined carotenoid accumulation in the flowers, roots, stems, and seeds of YB1 throughout its growth and development. The results indicated that carotenoids continued to accumulate in the roots and stems of YBI, especially in its cortex, throughout plant growth and development; however, the carotenoid levels in the petals decreased with progression of the flowering stage. In total, 54 carotenoid compounds were identified across tissues, with 30 being unique metabolites. Their levels correlated with the expression pattern of 22 differentially expressed genes related to carotenoid biosynthesis and degradation. Tissue-specific genes, including CCD8 and NCED in flowers and ZEP in the roots and stems, were identified as key regulators of color variations in different plant parts. Additionally, we identified genes in the seeds that regulated the conversion of carotenoids to abscisic acid. In conclusion, this study offers valuable insights into the regulation of carotenoid metabolism in B. rapa, which can guide the selection and breeding of carotenoid-rich varieties. Full article
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<p>Dynamics of the phenotype and total carotenoid content in the mutants. (<b>A</b>): YB1 flowers; (<b>B</b>): TY7 flowers; (<b>C</b>): comparison of total carotenoid content at different flowering stages; CB, CO and CA represent the bud stage, semi-open stage, and full bloom stage of TY7 petals, respectively; YB, YO, and YA represent the bud stage, semi-open stage, and full bloom stage of YB1 petals, respectively; (<b>D</b>): YB1 rhizomes; (<b>E</b>): TY7 rhizomes; (<b>F</b>): comparison of total carotenoids at different stages of rhizome fertility; CP and YP denote the TY7 and YB1 cortices, respectively, and CW and YW denote the TY7 and YB1 vascular bundles, respectively; November 2022 is referred to as the 11th, December 2022 as the 12th, January 2023 as the 1st, February 2023 as the 2nd, March 2023 as the 3rd, April 2023 as the 4th, and May 2023 as the 5th. Data are expressed as the mean of three biological replicates. Differences between the two varieties were considered statistically significant at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Identification and clustering analysis of carotenoid differential metabolites. (<b>A</b>): OPLS-DA supervised analysis; CK1, CK2, and CK3 denote the petal, rhizome, and seed samples of the TY7 variety, respectively; YB1, YB2, and YB3 denote the petal, rhizome, and seed samples of the YB1 variety, respectively; (<b>B</b>): metabolite Wayne plots; comparisons between CSM (TY7 seed) and YSM (YB1 seed); CRM (TY7 root) and YRM (YB1 root); and CFM (TY7 petal) and YFM (YB1 petal); (<b>C</b>): heatmap of carotenoid metabolite clustering in different tissues; CF1−1, CF1−2, and CF1−3 represent the three biological replicates of TY7 petal samples; CR2−1, CR2−2, and CR2−3 represent the three biological replicates of TY7 root samples; CS3−1, CS3−2, and CS3−3 represent the three biological replicates of TY7 seed samples; YF1−1, YF1−2, YF1−3 represent the three biological replicates of YB1 petal samples; YR2−1, YR2−2, and YR2−3 represent the three biological replicates of YB1 root samples; and YS3−1, YS3−2, and YS3−3 represent the three biological replicates of YB1 seed samples; (<b>D</b>): KEGG analysis of differential metabolites. Note: CF stands for TY7 flower, YF stands for YB1 flower, CR stands for TY7 rhizome, YR stands for YB1 rhizome, CS stands for TY7 seed, and YS denotes YB1 seed.</p>
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<p>Transcriptome analysis of different samples. (<b>A</b>): Wayne plots of differentially expressed genes (DEGs) in different tissues of the control and mutant plants; (<b>B</b>): transcriptome DEGs; CF_vs._YF denotes the comparison between petals of TY7 and YB1; CR_vs._YR denotes the comparison between the roots of TY7 and YB1; and CS_vs._YS denotes the comparison between the seeds of TY7 and YB1. (<b>C</b>): GO classification of DEGs. (<b>D</b>): KEGG pathway enrichment of the DEGs.</p>
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<p>Weighted gene co-expression network analysis of the genes associated with carotenoid metabolism. (<b>A</b>): Hierarchical clustering tree diagram of co-expressed genes in WGCNA, with each leaf corresponding to one gene, and the main branches from seven modules labeled in different colors; (<b>B</b>): relationship between modules and carotenoid metabolism-related DEGs, with each row representing one module. Each column represents the carotenoid biosynthesis-related DEGs; the value of each cell at the intersection of rows and columns represents the coefficient of correlation between the modules and carotenoid metabolism DEGs (shown on the right side of the color scale), whereas the value in parentheses in each cell represents the <span class="html-italic">p</span> value; (<b>C</b>): KEGG enrichment analysis of turquoise module DEGs; (<b>D</b>): KEGG enrichment analysis of green module DEGs; (<b>E</b>): KEGG enrichment analysis of yellow module DEGs.</p>
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<p>Pearson correlation analysis of DEGs with carotenoid differential metabolites (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Carotenoid regulatory networks in different tissues. Note: CF: TY7 petals; YF: YB1 petals; CR: TY7 rhizomes; YR: YB1 rhizomes; CS: TY7 seeds; YS: YB1 seeds; PDS: 15-cis-octahydroxylycopene desaturase; crtL2: lycopene e-cyclase; CYP97A3: β-cyclohydroxylase; crtZ: β-carotenoids 3-lightening enzyme; CCD8: carotenoid cleavage dioxygenase; NCED: 9-cis-epoxycarotenoid dioxygenase; ABA2: xanthoxin dehydrogenase; CYP707A: (+)−abscisic acid 8′-hydroxylase; ZEP, ABA1: zeaxanthin epoxidase. Orange color indicates upregulation and light blue color indicates downregulation.</p>
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<p>qRT-PCR assay for the differential expression profiles of genes in the seeds, petals, and roots of the control and mutant plants and transcriptome heat map. *** Significantly Note: CF: TY7 petals; YF: YB1 petals; CR: TY7 rhizomes; YR: YB1 rhizomes; CS: TY7 seeds; YS: YB1 seeds.</p>
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14 pages, 7220 KiB  
Article
Transcriptome Remodeling in Arabidopsis: A Response to Heterologous Poplar MSL-lncRNAs Overexpression
by Jinyan Mao, Qianhua Tang, Huaitong Wu and Yingnan Chen
Plants 2024, 13(20), 2906; https://doi.org/10.3390/plants13202906 - 17 Oct 2024
Abstract
Stamens are vital reproductive organs in angiosperms, essential for plant growth, reproduction, and development. The genetic regulation and molecular mechanisms underlying stamen development are, however, complex and varied among different plant species. MSL-lncRNAs, a gene specific to the Y chromosome of Populus deltoides [...] Read more.
Stamens are vital reproductive organs in angiosperms, essential for plant growth, reproduction, and development. The genetic regulation and molecular mechanisms underlying stamen development are, however, complex and varied among different plant species. MSL-lncRNAs, a gene specific to the Y chromosome of Populus deltoides, is predominantly expressed in male flower buds. Heterologous expression of MSL-lncRNAs in Arabidopsis thaliana resulted in an increase in both stamen and anther count, without affecting pistil development or seed set. To reveal the molecular regulatory network influenced by MSL-lncRNAs on stamen development, we conducted transcriptome sequencing of flowers from both wild-type and MSL-lncRNAs-overexpressing Arabidopsis. A total of 678 differentially expressed genes were identified between wild-type and transgenic Arabidopsis. Among these, 20 were classified as transcription factors, suggesting a role for these regulatory proteins in stamen development. GO enrichment analysis revealed that the differentially expressed genes were significantly associated with processes such as pollen formation, polysaccharide catabolic processes, and secondary metabolism. KEGG pathway analysis indicated that MSL-lncRNAs might promote stamen development by upregulating genes involved in the phenylpropanoid biosynthesis pathway. The top three upregulated genes, all featuring the DUF295 domain, were found to harbor an F-box motif at their N-termini, which is implicated in stamen development. Additionally, in transgenic Arabidopsis flowers, genes implicated in tapetum formation and anther development were also observed to be upregulated, implying a potential role for MSL-lncRNAs in modulating pollen development through the positive regulation of these genes. The findings from this study establish a theoretical framework for elucidating the genetic control exerted by MSL-lncRNAs over stamen and pollen development. Full article
(This article belongs to the Section Plant Molecular Biology)
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<p>Transcriptome data analysis. (<b>a</b>) Correlation analysis among six samples. (<b>b</b>) Bar Chart of the number of differentially expressed genes. (<b>c</b>) Cluster analysis of DEGs collected in six samples. The normalized FPKM expression is indicated by the row Z-score, where red represents upregulated genes and blue represents downregulated genes in every sample.</p>
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<p>Bar chart displaying the top three upregulated and bottom three downregulated genes based on log-fold change (logFC) values.</p>
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<p>Validation of RNA-seq results using qRT-PCR analysis. The top three histograms depict the relative expression levels from qRT-PCR, with fold change values shown as the mean ± standard deviation across three independent experiments. The bottom three histograms illustrate the FPKM values derived from RNA-seq data.</p>
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<p>Heatmap of differentially expressed transcription factors based on FPKM values. Normalized transcription factor expression is indicated by the row Z-score where red represents upregulated genes and blue represents downregulated genes.</p>
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<p>GO enrichment analysis of DEGs. (<b>a</b>) Biological process enrichment analysis. (<b>b</b>) Cellular component enrichment analysis. (<b>c</b>) Molecular function enrichment analysis.</p>
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<p>KEGG enrichment analysis of DEGs. The <span class="html-italic">X</span>-axis represents the number of DEGs enriched in specific metabolic pathways. The color gradient from red to blue denotes adjusted <span class="html-italic">p</span>-values: red for the smallest (0.00), purple for moderate (0.10), and blue for the largest (0.20).</p>
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<p>Differential expression levels of genes related to phenylpropanoid biosynthesis identified by KEGG annotation. The enzymes marked with the red boxes are associated with the upregulation of proteins, while those marked with the green boxes are associated with the downregulation of proteins.</p>
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<p>Protein–protein interaction network in <span class="html-italic">Arabidopsis</span>. Each node represents a protein, with the protein name displayed inside. Arcs denote interactions between proteins, and color coding reflects interaction strength: red for high, orange for moderate, and yellow for low interaction degrees.</p>
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34 pages, 2722 KiB  
Review
Antimicrobial Properties and Therapeutic Potential of Bioactive Compounds in Nigella sativa: A Review
by Munawar Abbas, Mayank Anand Gururani, Amjad Ali, Sakeena Bajwa, Rafia Hassan, Syeda Wajiha Batool, Mahreen Imam and Dongqing Wei
Molecules 2024, 29(20), 4914; https://doi.org/10.3390/molecules29204914 - 17 Oct 2024
Abstract
Nigella sativa (N. sativa; Ranunculaceae), commonly referred to as black cumin, is one of the most widely used medicinal plants worldwide, with its seeds having numerous applications in the pharmaceutical and food industries. With the emergence of antibiotic resistance in pathogens [...] Read more.
Nigella sativa (N. sativa; Ranunculaceae), commonly referred to as black cumin, is one of the most widely used medicinal plants worldwide, with its seeds having numerous applications in the pharmaceutical and food industries. With the emergence of antibiotic resistance in pathogens as an important health challenge, the need for alternative microbe-inhibitory agents is on the rise, whereby black cumin has gained considerable attention from researchers for its strong antimicrobial characteristics owing to its high content in a wide range of bioactive compounds, including thymoquinone, nigellimine, nigellidine, quercetin, and O-cymene. Particularly, thymoquinone increases the levels of antioxidant enzymes that counter oxidative stress in the liver. Additionally, the essential oil in N. sativa seeds effectively inhibits intestinal parasites and shows moderate activity against some bacteria, including Bacillus subtilis and Staphylococcus aureus. Thymoquinone exhibits minimum inhibitory concentrations (MICs) of 8–16 μg/mL against methicillin-resistant Staphylococcus aureus (MRSA) and exhibits MIC 0.25 µg/mL against drug-resistant mycobacteria. Similarly, quercetin shows a MIC of 2 mg/mL against oral pathogens, such as Streptococcus mutans and Lactobacillus acidophilus. Furthermore, endophytic fungi isolated from N. sativa have demonstrated antibacterial activity. Therefore, N. sativa is a valuable medicinal plant with potential for medicinal and food-related applications. In-depth exploration of the corresponding therapeutic potential and scope of industrial application warrants further research. Full article
(This article belongs to the Special Issue Advances in Natural Products and Their Biological Activities)
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<p><span class="html-italic">Nigella sativa</span> used in different traditional medical systems for the treatment of various diseases.</p>
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<p>(<b>a</b>) Chemical structures of key bioactive compounds from <span class="html-italic">Nigella sativa</span>. (<b>b</b>) The graph illustrates the relative prevalence of thymoquinone, p-cymene, camphene, thymol, terpinol, and alpha-thujene, highlighting their significance among the wide range of bioactive constituents found in <span class="html-italic">Nigella sativa</span>. The compounds were quantified using different techniques, such as gas chromatography (GC) and mass spectrometry (MS), followed by different extraction methods, such as supercritical fluid and Soxhlet extraction.</p>
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<p>(<b>a</b>) Chemical structures of key bioactive compounds from <span class="html-italic">Nigella sativa</span>. (<b>b</b>) The graph illustrates the relative prevalence of thymoquinone, p-cymene, camphene, thymol, terpinol, and alpha-thujene, highlighting their significance among the wide range of bioactive constituents found in <span class="html-italic">Nigella sativa</span>. The compounds were quantified using different techniques, such as gas chromatography (GC) and mass spectrometry (MS), followed by different extraction methods, such as supercritical fluid and Soxhlet extraction.</p>
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<p>Schematic illustration of the proposed immunomodulatory pathways of thymoquinone. Thymoquinone stimulates B and T lymphocyte activation, promotes antibody production, regulates the release of cytokines (TNF, IL-1, and IL-6), and increases the cytotoxicity of NK cells.</p>
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<p><span class="html-italic">Nigella sativa</span> exhibits protective effects against bacteria, fungi, parasites, and viruses through diverse and potent defense mechanisms.</p>
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<p>The schematic illustration demonstrates how thymoquinone reduces nitric oxide (NO) and inflammatory mediators, potentially alleviating symptoms and decreasing the likelihood of additional health complications in tuberculosis (TB). Additionally, the image emphasizes targeting type 2 alveolar cells by mycobacterium tuberculosis (MTB), underscoring the promising potential of thymoquinone in combating this infectious respiratory disease.</p>
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13 pages, 1114 KiB  
Article
Artificial Light at Night Reduces the Surface Activity of Earthworms, Increases the Growth of a Cover Crop and Reduces Water Leaching
by Zenia Kavassilas, Marion Mittmannsgruber, Edith Gruber and Johann G. Zaller
Land 2024, 13(10), 1698; https://doi.org/10.3390/land13101698 - 17 Oct 2024
Abstract
Artificial light at night (ALAN), also known as light pollution, is a growing environmental problem worldwide. However, only a few studies have examined whether soil organisms that search for food at the surface at night can be affected by ALAN. We investigated the [...] Read more.
Artificial light at night (ALAN), also known as light pollution, is a growing environmental problem worldwide. However, only a few studies have examined whether soil organisms that search for food at the surface at night can be affected by ALAN. We investigated the effects of ALAN on the above-ground foraging activity of anecic earthworms (Lumbricus terrestris), on the soil water infiltration and on the germination and growth of a cover crop (Phacelia tanacetifolia). In a full-factorial greenhouse experiment, we tested four factors: ALAN (about 5 lx during the night vs. total darkness), earthworms (two specimens vs. none), plant species (Phacelia alone vs. mixed with ragweed Ambrosia artemisiifolia) and sowing depth (surface-sown vs. sown in 5 cm depth). Data were analysed using multifactorial ANOVAs. Earthworms removed 51% less surface litter under ALAN than under dark conditions. ALAN had no effect on Phacelia germination but resulted in increased height growth and biomass production when the seeds were buried. Earthworms reduced Phacelia germination and biomass production. ALAN reduced water leaching through the experimental units, probably due to interactions between the subsurface casts and plant roots. We conclude that ALAN, as emitted from streetlights, can lead to complex ecological effects in ecosystems that merit further investigation. Full article
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<p>Mean brightness in lx measured throughout all experimental days comparing the light and dark treatments.</p>
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<p>Litter removal from the soil surface in response to (<b>A</b>) complete darkness at night (D) or ALAN (L) when earthworms were absent (EW−) or present (EW+) or (<b>B</b>) when two plant species where present (M) or only <span class="html-italic">Phacelia</span> was present (P) when earthworms were absent (EW−) or present (EW+). Each box represents the 1st and 3rd quartiles, the median as the horizontal line and the whiskers as minimum and maximum values. N = 6. Asterisks denote statistical significances: *** &lt;0.001; NS—not significant.</p>
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<p><span class="html-italic">Phacelia</span> germination (<b>A</b>), height growth (<b>B</b>) and biomass production (<b>C</b>,<b>D</b>) in response to complete darkness at night (D) or ALAN (L) when earthworms were absent (EW−) or present (EW+) (<b>A</b>) or when two plant species where present (M) or only <span class="html-italic">Phacelia</span> was present (P). Each box represents the 1st and 3rd quartiles, the median as the horizontal line and the whiskers as minimum and maximum values. N = 6. Asterisks denote statistical significances: ** &lt;0.01, * &lt;0.05; NS—not significant.</p>
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<p>Water infiltration (<b>A</b>) and leachate amount (<b>B</b>) in response to complete darkness at night (D) or ALAN (L) when earthworms were absent (EW−) or present (EW+) (<b>A</b>) or when two plant species were present (M) or only <span class="html-italic">Phacelia</span> was present (P). Each box represents the 1st and 3rd quartiles, the median as the horizontal line and the whiskers as minimum and maximum values. N = 6. Asterisks denote statistical significances: *** &lt;0.001, ** &lt;0.01, NS—not significant.</p>
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10 pages, 246 KiB  
Article
Seed Germination Responses to Temperature and Osmotic Stress Conditions in Brachiaria Forage Grasses
by Francuois L. Müller, Jabulile E. Leroko, Clement F. Cupido, Igshaan Samuels, Nothando Ngcobo, Elizabeth L. Masemola, Fortune Manganyi-Valoyi and Tlou Julius Tjelele
Grasses 2024, 3(4), 264-273; https://doi.org/10.3390/grasses3040019 - 17 Oct 2024
Abstract
Brachiaria forages are known to be drought-tolerant as mature plants, but no information about drought tolerance at the seed germination stage is currently available. This study aimed to determine the impacts of different temperature and moisture conditions on the seed germination characteristics of [...] Read more.
Brachiaria forages are known to be drought-tolerant as mature plants, but no information about drought tolerance at the seed germination stage is currently available. This study aimed to determine the impacts of different temperature and moisture conditions on the seed germination characteristics of five Brachiaria genotypes. Brachiaria seeds were germinated under constant temperatures of 5 °C–45 °C at increments of 5 °C. Within each temperature treatment, five osmotic treatments (0 MPa, −0.1 MPa, −0.3 MPa, −0.5 MPa, and −0.7 MPa) were applied, and germination was recorded daily for 20 days. The results showed that seed germination in all Brachiaria species was significantly negatively impacted (p < 0.05) by osmotic stress as well as by high and low temperatures. For all species, germination only occurred between 15 and 40 °C. Under optimum moisture conditions (0 MPa), the optimum germination temperatures for B. humidicola were 15 to 35 °C, for B. brizantha and B. nigropedata, they were 15 to 20 °C, for B. decumbens, they were 15 to 25 °C, and for the hybrid Brachiaria species, the optimum germination temperature was only 20 °C. In all species, seed germination decreased as moisture conditions became more limiting. Only B. humidicola germinated optimally at a high temperature (35 °C). At these temperatures, the species had more than 82% germination when moisture was not a limiting factor (0 MPa), but at low osmotic stress conditions (−0.1 MPa) at 30 °C, the germination of this species decreased to 67%. In conclusion, the results from this study indicate that the seed germination and early seedling establishment stages of Brachiaria grasses are only moderately tolerant to drought stress. Further work on early seedling responses to temperature and moisture stresses is needed to quantify early seedling responses to these stresses and to develop more detailed planting time guidelines for farmers. Full article
12 pages, 2742 KiB  
Article
Effects of OsLPR2 Gene Knockout on Rice Growth, Development, and Salt Stress Tolerance
by Ying Gu, Chengfeng Fu, Miao Zhang, Changqiang Jin, Yuqi Li, Xingyu Chen, Ruining Li, Tingting Feng, Xianzhong Huang and Hao Ai
Agriculture 2024, 14(10), 1827; https://doi.org/10.3390/agriculture14101827 - 17 Oct 2024
Viewed by 146
Abstract
Rice (Oryza sativa L.), a globally staple food crop, frequently encounters growth, developmental, and yield limitations due to phosphate deficiency. LOW PHOSPHATE ROOT1/2 (LPR1/2) are essential genes in plants that regulate primary root growth and respond [...] Read more.
Rice (Oryza sativa L.), a globally staple food crop, frequently encounters growth, developmental, and yield limitations due to phosphate deficiency. LOW PHOSPHATE ROOT1/2 (LPR1/2) are essential genes in plants that regulate primary root growth and respond to local phosphate deficiency signals under low phosphate stress. In rice, five LPR genes, designated OsLPR1OsLPR5 based on their sequence identity with AtLPR1, have been identified. OsLPR3 and OsLPR5 are specifically expressed in roots and induced by phosphate deficiency, contributing to rice growth, development, and the maintenance of phosphorus homeostasis under low phosphate stress. In contrast, OsLPR2 is uniquely expressed in shoots, suggesting it may have distinct functions compared with other family members. This study employed Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-associated protein 9 (CRISPR/Cas9) gene editing technology to generate oslpr2 mutant transgenic lines and subsequently investigated the effect of OsLPR2 gene knockout on rice growth, phosphate utilization, and salt stress tolerance in the seedling stage, as well as the effect of OsLPR2 gene knockout on rice development and agronomic traits in the maturation stage. The results indicated that the knockout of OsLPR2 did not significantly impact rice seedling growth or phosphate utilization, which contrasts significantly with its homologous genes, OsLPR3 and OsLPR5. However, the mutation influenced various agronomic traits at maturity, including plant height, tiller number, and seed setting rate. Moreover, the OsLPR2 mutation conferred enhanced salt stress tolerance in rice. These findings underscore the distinct roles of OsLPR2 compared with other homologous genes, establishing a foundation for further investigation into the function of the OsLPR family and the functional differentiation among its members. Full article
(This article belongs to the Section Crop Genetics, Genomics and Breeding)
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<p>Transcript level of <span class="html-italic">OsLPR2</span> under different nutrient deficiencies. Wild type rice seedlings of Nipponbare were cultivated for 7 days in complete nutrient solution (CK) or in nutrient-deficiency solution, which excluded nitrogen (−N), phosphorus (−P), potassium (−K), magnesium (−Mg), or iron (−Fe). Relative expression levels of OsLPR2 in shoot (<b>A</b>) and root (<b>B</b>) were determined via qRT-PCR. Values are presented as means ± SE (<span class="html-italic">n</span> = 3). Different letters above the bars indicate significant differences in the relative expression levels of OsLPR2 (<span class="html-italic">p</span> &lt; 0.05, one-way ANOVA).</p>
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<p>Construction and identification of <span class="html-italic">OsLPR2</span> mutant material. (<b>A</b>) Schematic diagram of the <span class="html-italic">oslpr2</span> target sites. (<b>B</b>) Identification of positive <span class="html-italic">oslpr2</span> seedlings. (<b>C</b>) Sequencing sequences and chromatograms of homozygous <span class="html-italic">oslpr2</span> mutant lines. (<b>D</b>) Cas9 segregation identification of mutant lines.</p>
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<p>The effect of <span class="html-italic">OsLPR2</span> mutation on the plant height and tillers per plant at maturity. (<b>A</b>) Plant types. (<b>B</b>) Plant height. (<b>C</b>) Number of tillers per plant. Scale bar: 20 cm. Values are means ± SE (<span class="html-italic">n</span> = 15). Different letters above the bars indicate significant differences (<span class="html-italic">p</span> &lt; 0.05, one-way ANOVA).</p>
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<p>The effect of <span class="html-italic">OsLPR2</span> mutation on panicle type of rice. (<b>A</b>) Panicle types. (<b>B</b>) Panicle length. (<b>C</b>) Number of primary branches. (<b>D</b>) Number of secondary branches. (<b>E</b>) Number of grains per panicle. (<b>F</b>) Seed setting rate. Scale bar: 5 cm. Values are means ± SE (<span class="html-italic">n</span> = 15). Different letters above the bars indicate significant differences (<span class="html-italic">p</span> &lt; 0.05, one-way ANOVA).</p>
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<p>The effect of <span class="html-italic">OsLPR2</span> mutation on the lengths of shoots and roots. (<b>A</b>,<b>B</b>) Images showing the relative growth performances of WT and <span class="html-italic">oslpr2</span> mutant lines under +P and −P conditions (bar = 10 cm). (<b>C</b>,<b>E</b>) Lengths and biomass of shoots or roots under phosphate sufficiency. (<b>D</b>,<b>F</b>) Lengths and biomass of shoots and roots under phosphate deficiency. Values are presented as means ± SE (<span class="html-italic">n</span> = 6). Same letters above the bars indicate no significant differences (<span class="html-italic">p</span> &lt; 0.05, one-way ANOVA).</p>
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<p>The effect of <span class="html-italic">OsLPR2</span> mutation on soluble Pi concentration of rice. (<b>A</b>) <span class="html-italic">OsLPR2</span> transgenic materials and wild type plants with consistent growth under normal phosphate supply. (<b>B</b>) After 21 days of phosphate deficiency treatment, sampling of different plant parts (leaves, leaf sheaths, roots) for extractable phosphate content measurement. Values are means ± SE (<span class="html-italic">n</span> = 3). Different letters above the bars indicate significant differences (<span class="html-italic">p</span> &lt; 0.05, one-way ANOVA).</p>
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<p>Assessment of <span class="html-italic">oslpr2</span> mutant survival and physiological responses under saline conditions. (<b>A</b>) Phenotypes of WT and <span class="html-italic">oslpr2</span> mutants after 200 mM NaCl treatment. (<b>B</b>) Survival rate statistics. (<b>C</b>) POD activity after 150 mM NaCl treatment. (<b>D</b>) MDA content after 150 mM NaCl treatment. Values are means ± SE (<span class="html-italic">n</span> = 3). Different letters above the bars indicate significant differences (<span class="html-italic">p</span> &lt; 0.05, one-way ANOVA).</p>
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20 pages, 8133 KiB  
Article
Light Regulated CoWRKY15 Acts on CoSQS Promoter to Promote Squalene Synthesis in Camellia oleifera Seeds
by Aori Li, Qinhui Du, Yanling Zeng, Rui Yang, Luyao Ge, Ziyan Zhu, Chenyan Li and Xiaofeng Tan
Int. J. Mol. Sci. 2024, 25(20), 11134; https://doi.org/10.3390/ijms252011134 - 17 Oct 2024
Viewed by 167
Abstract
Squalene synthase (SQS) is the most direct key enzyme regulating squalene synthesis. To better understand the regulatory mechanisms of squalene biosynthesis, a 1423-bp long promoter region of the CoSQS gene was isolated from Camellia oleifera. Plant CARE and PLACE analysis affirmed the [...] Read more.
Squalene synthase (SQS) is the most direct key enzyme regulating squalene synthesis. To better understand the regulatory mechanisms of squalene biosynthesis, a 1423-bp long promoter region of the CoSQS gene was isolated from Camellia oleifera. Plant CARE and PLACE analysis affirmed the existence of the core promoter elements such as TATA and CAAT boxes and transcription factor binding sites like W-box and MYB in the isolated sequence. Exogenous factors regulating the CoSQS promoter were obtained by using Yeast one-hybrid screening, and the key transcription factor CoWRKY15 was found. AOS (Antibody Optimization System) analysis showed that CoWRKY15 had the highest interactions with a confidence level of 0.9026. Bioinformatics analysis showed that CoWRKY15 belonged to class 2 of the WRKY gene family. The results of subcellular localization showed that CoWRKY15 functioned in the nucleus. The results of CoWRKY15 promoter analysis showed that 8 out of 14 cis-elements with annotatable functions were related to the light response. The region of the CoSQS promoter that interacts with CoWRKY15 is −186 bp~−536 bp. The histochemical assay and squalene content suggested that the CoSQS promoter could drive the expression of GUS gene and specific promotion of CoSQS expression. It was found that CoWRKY15 could act on the −186 bp~−536 bp CoSQS promoter to regulate the expression of CoSQS and the content of squalene in C. oleifera seed kernels. Full article
(This article belongs to the Special Issue Advances in Tea Tree Genetics and Breeding: 2nd Edition)
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<p>Gene enrichment analysis of <span class="html-italic">pCoSQS</span> yeast single hybrid interaction. (<b>A</b>) Functional enrichment analysis of GO genes. The longer the horizontal axis represents the number, the greater the number of genes enriched to this function, and the redder the column color, the more significant it is. (<b>B</b>) KEGG enrichment pathway analysis. The dot represents the larger the number of origins, the greater the number of genes from the negative electrode to this pathway, and the redder the origin, the more significant it is.</p>
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<p>Yeast one-hybrid screen library. (<b>A</b>) The metabolic pathways to which NGS belongs; (<b>B</b>–<b>F</b>) AOS predicted interaction sites between CoWRKY15 and CoSQS promoters. The green color in the background represents the CoWRKY15 protein. The red–blue DNA molecular structure represents the CoSQS promoter. The highlighted part represents different interaction sites between CoWRKY15 and CoSQS promoters.</p>
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<p>Comparison of amino acid sequences from CoWRKY15 CDS sequence with homologs of other plants WRKY sequences. The red box represents the conservative domain. * indicates consistent sequence.</p>
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<p>Subcellular localization of CoWRKY15. (<b>A</b>) pCAMBIA1300-CoWRKY15-GFP; (<b>B</b>) pCAMBIA1300-GFP.</p>
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<p>Expression pattern analysis of <span class="html-italic">CoWRKY15</span>. (<b>A</b>) Squalene content in <span class="html-italic">C. oleifera</span> at different developmental stages; (<b>B</b>) The relative expression of <span class="html-italic">CoWRKY15</span> in different periods of <span class="html-italic">C. oleifera</span>; (<b>C</b>) Squalene content of <span class="html-italic">C. oleifera</span> kernel oil under different light quality conditions; (<b>D</b>) <span class="html-italic">Co WRKY15</span> relative expression in <span class="html-italic">C. oleifera</span> kernels under different light quality conditions. The light quality conditions were as follows: natural light: White, Blue, Red, and Red:Blue = 1:1. The lowercase letters a–g in the histogram represent significant differences; different letters represent significant differences between groups, <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Detection of CoWRKY15 acting at the <span class="html-italic">CoSQS</span> promoter. (<b>A</b>) Yeast one-hybrid validation; (<b>B</b>) Schematic diagram of promoter truncation; (<b>C</b>) The effect of CoWRKY15 on the activity of <span class="html-italic">CoSQS</span> promoter was determined by dual luciferase assay; (<b>D</b>) The effect of CoWRKY15 on the activity of <span class="html-italic">CoSQS</span> promoter truncated P1, P2, and P3 was determined by dual luciferase assay. ‘****’: <span class="html-italic">p</span> &lt; 0.01.’ns’: <span class="html-italic">p</span> &gt; 0.01.</p>
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<p>Functional identification of CoSQS promoter transgenic tobacco. (<b>A</b>) Universal primer PCR test results (lane 1 is pCAMBIA 1304-<span class="html-italic">pCoSQS</span>, lane 2 is pCAMBIA 1304-<span class="html-italic">pCoSQS-CoSQS-NOS</span>, lane 3 is pCAMBIA 1304-<span class="html-italic">pCoSQS-CoSQS</span>). (<b>B</b>) Schematic diagram of 1304-<span class="html-italic">pCoSQS-CoSQS-NOS</span> recombinant expression plasmid. (<b>C</b>) GUS staining status of T2 generation tobacco leaves (Figure a is the detection result of pCAMBIA 1304-pCoSQS-CoSQS-NOS transgenic tobacco; Figure b is the detection result of wild-type tobacco; Figure c is the detection result of pCAMBIA 1304-pCoSQS transgenic tobacco). (<b>D</b>) Tobacco genomic DNA PCR detection results (1 is the detection result of pCAMBIA 1304-pCoSQS-CoSQS-NOS genetically modified tobacco; 2 and 3 are wild-type tobacco detection results; 4 and 5 are the detection results of pCAMBIA 1304-pCoSQS genetically modified tobacco). (<b>E</b>) Comparison of squalene content in wild-type tobacco and genetically modified tobacco.</p>
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<p>Schematic diagram of MVA synthesis pathway of squalene and pCoSQS expression regulated by transcription factor CoWRKY, light, and hormone.</p>
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13 pages, 1033 KiB  
Article
Adaptive Seedling Strategies in Seasonally Dry Tropical Forests: A Comparative Study of Six Tree Species
by Carlos Ivan Espinosa, Elvia Esparza and Andrea Jara-Guerrero
Plants 2024, 13(20), 2900; https://doi.org/10.3390/plants13202900 - 17 Oct 2024
Viewed by 166
Abstract
This study examines seed germination strategies and seedling establishment in six tree species typical of seasonally dry tropical forests. We focused on how interspecific and intraspecific differences in seed size and germination speed influence biomass allocation and seedling growth. Using generalized linear models, [...] Read more.
This study examines seed germination strategies and seedling establishment in six tree species typical of seasonally dry tropical forests. We focused on how interspecific and intraspecific differences in seed size and germination speed influence biomass allocation and seedling growth. Using generalized linear models, we analyzed the effects of these traits on root/shoot ratios and growth rates. Our findings reveal two main strategies: slow germination, high root/shoot ratio, and low growth rate in Erythrina velutina Willd and Terminalia valverdeae A.H. Gentry, associated with enhanced drought tolerance. In contrast, Cynophalla mollis (Kunth) J. Presl and Coccoloba ruiziana Lindau exhibited rapid germination, lower root/shoot ratios, and low to moderate growth rates, favoring competition during early establishment. Centrolobium ochroxylum Rose ex Rudd partially aligned with this second strategy due to its fast growth. Vachellia macracantha (Humb. & Bonpl. ex Willd.) Seigler & Ebinger presented a unique case, displaying slow germination and a broad range in both root/shoot ratios and growth rates. At the intraspecific level, significant variation in biomass allocation and growth rate was observed, influenced by germination speed and seed weight. We discuss the adaptive significance of seed traits in SDTFs and their role in seedling establishment under varying environmental conditions, providing insights for strategies for conservation and restoration in these ecosystems. Full article
(This article belongs to the Section Plant Ecology)
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<p>Curve of the accumulated proportion of seed germination along the time. The lines represent the proportion of seeds that germinated over time for each species. Upper curves represent higher germination proportion, and more pronounced slopes represent higher germination velocity.</p>
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<p>Generalized linear models of seed weight (<b>a</b>), germination speed (<b>b</b>), and competition strategies defined by biomass allocation (<b>c</b>), and growth rate (<b>d</b>). The letters show differences between species according to post hoc analysis.</p>
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<p>Variation in seed weight (g) of the six studied species. The size of the seed image illustrates the difference in seed weight between species, while the circles depict each species’ minimum, average, and maximum seed weight in proportion.</p>
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21 pages, 868 KiB  
Review
Citrus Seed Waste and Circular Bioeconomy: Insights on Nutritional Profile, Health Benefits, and Application as Food Ingredient
by S. Seyyedi-Mansour, M. Carpena, P. Donn, P. Barciela, A. Perez-Vazquez, J. Echave, A. G. Pereira and M. A. Prieto
Appl. Sci. 2024, 14(20), 9463; https://doi.org/10.3390/app14209463 - 16 Oct 2024
Viewed by 322
Abstract
Citrus fruits are widely grown, processed, and distributed in more than 140 countries, with annual global production exceeding 124.3 million metric tons. This substantial consumption generates significant organic waste, accounting for approximately 50–60% of the total fruit mass, primarily in the form of [...] Read more.
Citrus fruits are widely grown, processed, and distributed in more than 140 countries, with annual global production exceeding 124.3 million metric tons. This substantial consumption generates significant organic waste, accounting for approximately 50–60% of the total fruit mass, primarily in the form of peel, pulp, and seeds. Often discarded or reused as animal feed, these wastes contribute to significant environmental pollution and economic losses. Therefore, the valorization of these by-products represents an important opportunity to mitigate these challenges and improve the sustainability of the Citrus-related industry. This review highlights Citrus seed waste concerning its invaluable bioactive compounds, including fatty acids, phenolic compounds, limonoids, dietary fibers, vitamins, and carotenoids. Chemical compositions of Citrus seed biowaste differ depending on a variety of factors, such as Citrus variety, fruit maturity, environmental conditions, waste storage conditions, and extraction methods. The extraction and purification of phytochemicals from Citrus seed biowaste are one of the major procedures for valorizing waste. The two types of effective extraction methods are traditional (conventional extraction) and innovative (green extraction). Furthermore, Citrus seeds have been demonstrated to exhibit several biological activities and health-promoting properties including antioxidative, anti-inflammatory, and anti-cancer activities. Therefore, these wastes are safe and beneficial compounds used in the production of functional foods, nutraceuticals, pharmaceuticals, and cosmetics. A conclusion can be reached by emphasizing the abundance of bioactive compounds in Citrus seed wastes, which makes them an excellent opportunity for increased environmental and economic utilization. Full article
(This article belongs to the Special Issue Novel Food Technologies and Applications)
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<p>Examples of <span class="html-italic">Citrus</span> seeds waste applications in food and non-food industry. Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p>
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25 pages, 10670 KiB  
Article
Study on a Novel Reseeding Device of a Precision Potato Planter
by Jiarui Wang, Min Liao, Hailong Xia, Rui Chen, Junju Li, Junmin Li and Jie Yang
Agriculture 2024, 14(10), 1824; https://doi.org/10.3390/agriculture14101824 - 16 Oct 2024
Viewed by 254
Abstract
In order to address the problem of a high miss-seeding rate in mechanized potato planting work, a novel reseeding device is designed and analyzed. Based on dynamic and kinematic principles, the seed potato’s motion analysis model in the seed preparation process was constructed. [...] Read more.
In order to address the problem of a high miss-seeding rate in mechanized potato planting work, a novel reseeding device is designed and analyzed. Based on dynamic and kinematic principles, the seed potato’s motion analysis model in the seed preparation process was constructed. The analysis results indicate that the seed preparation performance is positively related to the seed preparation opening length l1 and inclination angle of the seed-returning pipe θ. Then, the potato’s motion analysis model in the reseeding process was constructed. The analysis showed that the displacement of seeding potatoes in the horizontal direction ds is influenced by the initial seeding potato’s speed v0t, dropping height hs, and the angle between the seeding pipe and the horizontal ground βs. The horizontal moving distance xr of the reseeding potatoes is influenced by the angle between the bottom of the reseeding pipe and horizontal ground βs2, the distance from its centroid to the reseeding door d, and the dropping height of the potato hr. The analysis results indicated that the reseeding potato can be effectively discharged into the furrow. Then, a prototype of a reseeding control system was constructed based on the STM32 microcontroller, electric pushers, and through-beam laser sensors. The simulation analysis was conducted to verify the theoretical analysis by using EDEM2020 software. The simulation results indicated that with the increase in the seeding chain speed, the seed preparation success rate initially increased slowly and then decreased gradually. The seed preparation performance can be increased by increasing the seed preparation opening length or decreasing the seed-returning pipe inclination angle. The impact on the successful seed preparation rate is ranked by significance as follows: seed preparation opening length > seed-returning pipe inclination angle > chain speed. Then, the prototype reseeding device and the corresponding seed metering device were manufactured and a series of bench tests and field tests were conducted. The bench test results showed an average successful seed preparation rate of 93.6%. The average qualified-seeding rate, miss-seeding rate, and multi-seeding rate in the field test were 89.6%, 2.46%, and 7.94%, respectively. This study can provide a theoretical reference for the design of potato reseeding devices. Full article
(This article belongs to the Section Agricultural Technology)
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Figure 1
<p>(<b>a</b>) Schematic diagram of precision potato planter; (<b>b</b>) structure of right precision seed metering device; (<b>c</b>) schematic diagram of seed scoop.</p>
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<p>(<b>a</b>) Motion analysis of seed potato in seed-returning process; (<b>b</b>) optimized structure of the seed-returning pipe.</p>
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<p>The influence of factors on seed-returning speed. (<b>a</b>) The influence of seeding chain speed on seed preparation speed; (<b>b</b>) the influence of seed-returning pipe inclination angle on seed preparation speed.</p>
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<p>The relationship between seed-returning pipe angle and seed preparation opening length.</p>
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<p>Analysis of seed potato moving process of the reseeding device. (<b>a</b>) Analysis of seeding potato moving process, (<b>b</b>) analysis of the reseeding potato moving process.</p>
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<p>Relationship between initial speed <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>v</mi> </mrow> <mrow> <mn>0</mn> <mi>t</mi> </mrow> </msub> </mrow> </semantics></math> and horizontal displacement of seeding potatoes <span class="html-italic">d<sub>s</sub></span>.</p>
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<p>Relationship between the horizontal moving distance of the reseeding potatoes <span class="html-italic">x<sub>r</sub></span> and the distance from the reseeding potato’s centroid to the reseeding door <span class="html-italic">d</span>.</p>
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<p>Detecting points of sensors. (<b>a</b>) Detecting point of seed preparation sensor; (<b>b</b>) detection points of vacant scoop sensor.</p>
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<p>Main program flow of the reseeding device control system.</p>
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<p>(<b>a</b>) Connecting relationship of reseeding control system; (<b>b</b>) the constructed potato reseeding main control system.</p>
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<p>Constructed simulation model. (<b>a</b>) Seed potato model; (<b>b</b>) seed preparation model.</p>
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<p>Test bench.</p>
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<p>Single factor simulation analysis results. (<b>a</b>) The relationship between successful seed preparation rate and seeding chain speed; (<b>b</b>) the relationship between successful seed preparation rate and seed-returning pipe inclination angle; (<b>c</b>) the relationship between successful seed preparation rate and seed preparation opening length.</p>
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<p>The influence of each factor on the successful seed preparation rate. (<b>a</b>) Influence of the seed-returning pipe inclination angle and seeding chain speed on the successful seed preparation rate; (<b>b</b>) influence of the seed preparation opening length and seeding chain speed on the successful seed preparation rate; (<b>c</b>) influence of the seed-returning pipe inclination angle and the seed preparation opening length on the successful seed preparation rate.</p>
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<p>Field tests of entire precision potato planter. (<b>a</b>) Field seeding tests; (<b>b</b>) planting effect picture.</p>
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19 pages, 5186 KiB  
Article
Development of Natural Fungicidal Agricultural Defensives Using Microbial Glycolipid and Vegetable Oil Blends
by Anderson O. de Medeiros, Maria da Gloria C. da Silva, Attilio Converti, Fabiola Carolina G. de Almeida and Leonie A. Sarubbo
Surfaces 2024, 7(4), 879-897; https://doi.org/10.3390/surfaces7040058 - 16 Oct 2024
Viewed by 185
Abstract
The use of pesticides causes significant environmental problems, which drives the search for natural and non-toxic alternatives. In this study, a glycolipid biosurfactant (BS), produced by the yeast Starmerella bombicola ATCC 22214, was utilized as an active ingredient in natural agricultural defensive blends. [...] Read more.
The use of pesticides causes significant environmental problems, which drives the search for natural and non-toxic alternatives. In this study, a glycolipid biosurfactant (BS), produced by the yeast Starmerella bombicola ATCC 22214, was utilized as an active ingredient in natural agricultural defensive blends. The mixtures were tested for their fungicidal potential against phytopathogenic fungi isolated from fruits such as papaya, orange, and banana, demonstrating strong inhibition of fungal growth. The genera Penicillium, Colletotrichum, and Aspergillus were the pathogens present in the deterioration of the fruits used in the experiment. The biosurfactant was produced in a fermenter, yielding 10 g/L and reducing the surface tension to 31.56 mN/m, with a critical micelle concentration (CMC) of 366 mg/L. Blends of BS with oleic acid (T1) and lemongrass oil (T2) were found to be effective in controlling fungi. Additionally, the phytotoxicity of these formulations was assessed using Cucumis anguria (gherkin) seeds, where the blend of BS with castor oil (T4) showed the best performance, promoting seed germination. These results indicate the potential of such mixtures as natural alternatives for fungal control in plants and for application in sustainable agricultural systems. Full article
(This article belongs to the Collection Featured Articles for Surfaces)
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Graphical abstract

Graphical abstract
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<p>Results of the critical micelle concentration of the glycolipid biosurfactant produced by <span class="html-italic">Starmerella bombicola</span> ATCC 22214.</p>
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<p>Results of TLC chromatogram showing the separation of components of glycolipid biosurfactant produced by <span class="html-italic">Starmerella bombicola</span> ATCC 22214.</p>
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<p>Antifungal activity of the tested agricultural biodefensive blends against at least three different filamentous fungi isolated from contaminated fruits (papaya, orange, and banana). The results presented in this figure demonstrate the general and/or partial inhibition of fungal growth on Petri dishes after treatment with different formulations. Blends T1 (biosurfactant + oleic acid) and T2 (biosurfactant + lemongrass oil) exhibited strong antifungal activity, significantly reducing the growth of all tested fungi. In contrast, T3 (biosurfactant + tea tree oil) and T4 (biosurfactant + castor oil) allowed partial fungal growth, while T5 (biosurfactant + neem oil) and T6 (commercial neem) showed varying degrees of inhibition. Control samples (T7, distilled water and T8, biosurfactant) displayed no inhibition, with fungal species growing abundantly. These observations indicate the potential of T1 and T2 as effective natural fungicidal formulations.</p>
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<p>Results of phytotoxicity test on <span class="html-italic">Cucumis anguria</span> (gherkin) seeds 5 days after treatment with the tested agricultural biodefensive blends. T1 = biosurfactant + oleic acid + distilled water, T2 = biosurfactant + lemongrass oil + distilled water, T3 = biosurfactant + tea tree oil + distilled water, T4 = biosurfactant + castor oil + distilled water, T5 = biosurfactant + commercial neem, T6 = commercial neem, T7 = distilled water, and T8 = biosurfactant + distilled water. The arrows point to the seeds that suffered inhibition in the presence of the evaluated formulation.</p>
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<p>Germination index of <span class="html-italic">Cucumis anguria</span> (gherkin) seeds in Petri dishes after treatment with the tested agricultural biodefensive blends. T1 = biosurfactant + oleic acid + distilled water, T2 = biosurfactant + lemongrass oil + distilled water, T3 = biosurfactant + tea tree oil + distilled water, T4 = biosurfactant + castor oil + distilled water, T5 = biosurfactant + commercial neem, T6 = commercial neem, T7 = distilled water, and T8 = biosurfactant + distilled water. Different letters indicate significantly different values according to the least significant difference test at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Evaluation of the germination and growth of <span class="html-italic">Cucumis anguria</span> (gherkin) in a seedbed in the presence of a light source. Photographs were taken before (0 h) and after (120 h) treatment with the tested agricultural biodefensive blends. T1 = biosurfactant + oleic acid + distilled water, T2 = biosurfactant + lemongrass oil + distilled water, T3 = biosurfactant + tea tree oil + distilled water, T4 = biosurfactant + castor oil + distilled water, T5 = biosurfactant + commercial neem, T6 = commercial neem, T7 = distilled water, and T8 = biosurfactant + distilled water.</p>
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<p>Germination index of <span class="html-italic">Cucumis anguria</span> (gherkin) seeds in seedbeds after treatment with the tested agricultural biodefensive blends. T1 = biosurfactant + oleic acid + distilled water, T2 = biosurfactant + lemongrass oil + distilled water, T3 = biosurfactant + tea tree oil + distilled water, T4 = biosurfactant + castor oil + distilled water, T5 = biosurfactant + commercial neem, T6 = commercial neem, T7 = distilled water, and T8 = biosurfactant + distilled water. Different letters indicate significantly different values according to the least significant difference test at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Procedure for measuring the spreading (scattering diameter) of a 40 µL volume of agricultural biodefensives on a paraffin sheet. T1 = biosurfactant + oleic acid + distilled water, T2 = biosurfactant + lemongrass oil + distilled water, T3 = biosurfactant + tea tree oil + distilled water, T4 = biosurfactant + castor oil + distilled water, T5 = biosurfactant + commercial neem, T6 = commercial neem, T7 = distilled water, and T8 = biosurfactant + distilled water. The values of the dispersion capacity, expressed as drop diameter measured with a digital caliper, are indicated in the figure.</p>
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<p>Dispersion percentages for the tested agricultural biodefensive blends. T1 = biosurfactant + oleic acid + distilled water, T2 = biosurfactant + lemongrass oil + distilled water, T3 = biosurfactant + tea tree oil + distilled water, T4 = biosurfactant + castor oil + distilled water, T5 = biosurfactant + commercial neem, T6 = commercial neem, T7 = distilled water, and T8 = biosurfactant + distilled water. Different letters indicate significantly different values according to the least significant difference test at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Result of the post-harvest shelf-life test on <span class="html-italic">Lactuca sativa</span> var. <span class="html-italic">capitata</span> (lettuce) leaves before (0 h) and after (72 h) treatment with the tested agricultural biodefensive blends. T1 = biosurfactant + oleic acid + distilled water, T2 = biosurfactant + lemongrass oil + distilled water, T3 = biosurfactant + tea tree oil + distilled water, T4 = biosurfactant + castor oil + distilled water, T5 = biosurfactant + commercial neem, T6 = commercial neem, T7 = distilled water and T8 = biosurfactant + distilled water. The arrows indicate points of injury possibly caused by the components of the formulation.</p>
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