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Plants, Volume 13, Issue 19 (October-1 2024) – 40 articles

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16 pages, 1023 KiB  
Article
Variation in Leaf Functional Traits of Populus laurifolia Ldb and Ulmus pumila L. across Five Contrasting Urban Sites in Ulaanbaatar, Mongolia
by Otgonsaikhan Byambasuren, Anujin Bat-Amgalan, Ser-Oddamba Byambadorj, Jonathan O. Hernandez, Tuguldur Nyam-Osor and Batkhuu Nyam-Osor
Plants 2024, 13(19), 2709; https://doi.org/10.3390/plants13192709 (registering DOI) - 27 Sep 2024
Abstract
Amid urbanization, studying leaf functional traits of woody plants in urban environments is essential for understanding how urban green spaces function and how they can be effectively managed sustainably. In this study, we investigated the effects of different growing conditions on the morpho-physiological [...] Read more.
Amid urbanization, studying leaf functional traits of woody plants in urban environments is essential for understanding how urban green spaces function and how they can be effectively managed sustainably. In this study, we investigated the effects of different growing conditions on the morpho-physiological traits of Populus laurifolia and Ulmus pumila across five contrasting urban sites. The leaf area (LA), leaf length (LL), leaf width (LW), leaf biomass (LB), specific leaf area (SLA), leaf chlorophyll concentration, chlorophyll fluorescence parameters, leaf water potential at predawn (Ψpd) and midday (Ψmd), leaf performance index (PIabs), and phenotypic plasticity index (PPI) were compared across five contrasting urban sites. The soil chemical and physical properties were also compared between sites. There were significant differences in soil physicochemical characteristics between sites. We found significant effects of site on most of the morpho-physiological traits measured, except for Ψmd. The leaf chlorophyll concentration of P. laurifolia and U. pumila varied significantly between sites. The Ψpd was significantly different between years and sites. In U. pumila, the mean PPI for morphological traits (0.20) was lower than that for physiological traits (0.21). In conclusion, we revealed significant variations in the morpho-physiological traits of P. laurifolia and U. pumila across the five urban sites. Hence, long-term, large-scale studies are recommended to examine how diverse species respond to different urban growing conditions and to include other ecologically important plant traits for a better understanding of urban trees in a changing environment. Full article
(This article belongs to the Section Plant Physiology and Metabolism)
17 pages, 3975 KiB  
Article
Identification of Genomic Regions Associated with Powdery Mildew Resistance in Watermelon through Genome-Wide Association Study
by Oak-Jin Lee, Koeun Han, Hye-Eun Lee, Hyo-Bong Jeong, Nari Yu and Wonbyoung Chae
Plants 2024, 13(19), 2708; https://doi.org/10.3390/plants13192708 - 27 Sep 2024
Abstract
Watermelon (Citrullus spp.) is an economically important crop globally, but it is susceptible to various diseases, including powdery mildew. Previous studies have identified genetic factors associated with powdery mildew resistance. However, further research using diverse genetic approaches is necessary to elucidate the [...] Read more.
Watermelon (Citrullus spp.) is an economically important crop globally, but it is susceptible to various diseases, including powdery mildew. Previous studies have identified genetic factors associated with powdery mildew resistance. However, further research using diverse genetic approaches is necessary to elucidate the underlying genetic mechanisms of this resistance. In this study, the germplasm collection comprising highly homozygous inbred lines was employed, which enabled the accumulation of consistent data and improved the reliability of the genome-wide association study (GWAS) findings. Our investigation identified two significant single-nucleotide polymorphisms (SNPs), pm2.1 and pm3.1, which were strongly associated with disease resistance. Moreover, several candidate genes were revealed within the linkage disequilibrium (LD) blocks surrounding the significant SNPs. In conclusion, the identification of significant SNPs and their additive effects, combined with the discovery of relevant candidate genes, expanded our understanding of the genetic basis of disease resistance and can pave the way for the development of more resilient watermelon cultivars through marker-assisted selection. Full article
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Figure 1
<p>Density of the single-nucleotide polymorphisms (SNPs) across the chromosomes within a 1 Mb window size.</p>
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<p>The correlation matrix of the four replicates, the averages of the replicates, and the best linear unbiased prediction (BLUP) values of each accession in powdery mildew resistance. Pearson’s correlation coefficients are indicated with asterisks, where *** denotes a <span class="html-italic">p</span> value less than 0.001, which indicates a highly significant correlation between the variables.</p>
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<p>Principal component analysis (PCA) plot (<b>a</b>) and neighbor-joining tree (<b>b</b>) of the germplasm collection. The red and orange dots indicate the resistant and susceptible wild-type accessions, respectively. The blue and green dots indicate the resistant and susceptible domesticated-type accessions, respectively.</p>
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<p>Population structure of the germplasm collection: (<b>a</b>) cross-validation (CV) error plot; (<b>b</b>) delta K plot; and (<b>c</b>) population clustering patterns for informative K values (K = 2 and K = 5).</p>
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<p>Manhattan plots and quantile–quantile (Q-Q) plots from a genome-wide association study (GWAS) of the best linear unbiased prediction (BLUP) values of each accession in powdery mildew resistance. (<b>a</b>,<b>b</b>) Manhattan plot and Q-Q plot using a mixed linear model (MLM). (<b>c</b>,<b>d</b>) Manhattan plot and Q-Q plot using a Bayesian-information and linkage-disequilibrium iteratively nested keyway (BLINK) model. The horizontal solid and dashed lines indicate the genome-wide and suggestive significance thresholds, respectively. Each chromosome is colored differently. The red line represents the expected distribution under the null hypothesis, where no association exists. The grey area indicates the 95% confidence interval.</p>
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<p>The phenotypic distribution of the best linear unbiased prediction (BLUP) values of each accession in powdery mildew resistance by allele for significant single-nucleotide polymorphisms (SNPs): <span class="html-italic">pm2.1</span> (<b>a</b>) and <span class="html-italic">pm3.1</span> (<b>b</b>). Blue and yellow represent the reference and alternative alleles, respectively. The asterisks denote the four levels of significant differences from the <span class="html-italic">t</span>-test results (**** <span class="html-italic">p</span> &lt; 0.0001, *** <span class="html-italic">p</span> &lt; 0.001).</p>
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<p>The phenotypic distribution of the best linear unbiased prediction (BLUP) values of each accession in powdery mildew resistance based on the allelic combinations of the significant single-nucleotide polymorphisms (SNPs): <span class="html-italic">pm2.1</span> and <span class="html-italic">pm3.1</span>. Different small letters refer to significant differences according to Duncan’s multiple range test.</p>
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<p>The distribution of the best linear unbiased prediction (BLUP) values across the accessions with susceptible alleles on <span class="html-italic">pm2.1</span> (<b>a</b>) and <span class="html-italic">pm3.1</span> (<b>b</b>).</p>
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<p>Interaction plot of the <span class="html-italic">pm2.1</span> and <span class="html-italic">pm3.1</span> genotypes on powdery mildew resistance.</p>
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15 pages, 1874 KiB  
Article
Azolla as a Safe Food: Suppression of Cyanotoxin-Related Genes and Cyanotoxin Production in Its Symbiont, Nostoc azollae
by Jonathan P. Bujak, Ana L. Pereira, Joana Azevedo, Alexandra A. Bujak, Victor Leshyk, Minh Pham Gia, Timo Stadtlander, Vitor Vasconcelos and Daniel J. Winstead
Plants 2024, 13(19), 2707; https://doi.org/10.3390/plants13192707 - 27 Sep 2024
Abstract
The floating freshwater fern Azolla is the only plant that retains an endocyanobiont, Nostoc azollae (aka Anabaena azollae), during its sexual and asexual reproduction. The increased interest in Azolla as a potential source of food and its unique evolutionary history have raised [...] Read more.
The floating freshwater fern Azolla is the only plant that retains an endocyanobiont, Nostoc azollae (aka Anabaena azollae), during its sexual and asexual reproduction. The increased interest in Azolla as a potential source of food and its unique evolutionary history have raised questions about its cyanotoxin content and genome. Cyanotoxins are potent toxins synthesized by cyanobacteria which have an anti-herbivore effect but have also been linked to neurodegenerative disorders including Alzheimer’s and Parkinson’s diseases, liver and kidney failure, muscle paralysis, and other severe health issues. In this study, we investigated 48 accessions of Azolla–Nostoc symbiosis for the presence of genes coding microcystin, nodularin, cylindrospermopsin and saxitoxin, and BLAST analysis for anatoxin-a. We also investigated the presence of the neurotoxin β-N-methylamino-L-alanine (BMAA) in Azolla and N. azollae through LC-MS/MS. The PCR amplification of saxitoxin, cylindrospermospin, microcystin, and nodularin genes showed that Azolla and its cyanobiont N. azollae do not have the genes to synthesize these cyanotoxins. Additionally, the matching of the anatoxin-a gene to the sequenced N. azollae genome does not indicate the presence of the anatoxin-a gene. The LC-MS/MS analysis showed that BMAA and its isomers AEG and DAB are absent from Azolla and Nostoc azollae. Azolla therefore has the potential to safely feed millions of people due to its rapid growth while free-floating on shallow fresh water without the need for nitrogen fertilizers. Full article
(This article belongs to the Section Plant Ecology)
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<p>Total ion chromatogram of six <span class="html-italic">Azolla</span> species for the detection of BMAA with method 2. From top to bottom: <span class="html-italic">A. caroliniana</span> (CA 3001), <span class="html-italic">A. filiculoides</span> (FI 1507), <span class="html-italic">A. pinnata</span> subsp. <span class="html-italic">pinnata</span> (PP 7001), <span class="html-italic">A. nilotica</span> (NI 5001), <span class="html-italic">A. mexicana</span> (ME 2026), <span class="html-italic">A. microphylla</span> (MI 4021), <span class="html-italic">A. pinnata</span> subsp. <span class="html-italic">imbricata</span> (PI 1).</p>
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<p>Total ion chromatogram (left) and CID spectra (right) of derivatized standards at 1 ppm. BMAA (top, RT = 13.92 min), AEG (middle, RT = 13.22 min), and 2,4-DAB (bottom, RT = 14.93 min).</p>
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35 pages, 4666 KiB  
Article
Ecogeographic Study of Ipomoea Species in Mauritius, Indian Ocean
by Yakshini Boyjnath, Mohammad Ehsan Dulloo, Vishwakalyan Bhoyroo and Vijayanti Mala Ranghoo-Sanmukhiya
Plants 2024, 13(19), 2706; https://doi.org/10.3390/plants13192706 - 27 Sep 2024
Abstract
The wild relatives of crops play a critical role in enhancing agricultural resilience and sustainability by contributing valuable traits for crop improvement. Shifts in climatic conditions and human activities threaten plant genetic resources for food and agriculture (PGRFA), jeopardizing contributions to future food [...] Read more.
The wild relatives of crops play a critical role in enhancing agricultural resilience and sustainability by contributing valuable traits for crop improvement. Shifts in climatic conditions and human activities threaten plant genetic resources for food and agriculture (PGRFA), jeopardizing contributions to future food production and security. Studies and inventories of the extant agrobiodiversity, in terms of numbers and distribution patterns of species and their genetic diversity, are primordial for developing effective and comprehensive conservation strategies. We conducted an ecogeographic study on Ipomoea species and assessed their diversity, distribution, and ecological preferences across different topographic, altitudinal, geographical, and climatic gradients, at a total of 450 sites across Mauritius. Species distribution maps overlaid with climatic data highlighted specific ecological distribution. Principal Component Analysis (PCA) revealed species distribution was influenced by geographical factors. Regional richness analyses indicated varying densities, with some species exhibiting localized distributions and specific ecological preferences while the other species showed diverse distribution patterns. Field surveys identified 14 species and 2 subspecies out of 21 species and 2 subspecies of Ipomoea reported in Mauritius. A gap in ex situ germplasm collections was observed and several species were identified as threatened. Further investigations and a more long-term monitoring effort to better guide conservation decisions are proposed. Full article
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<p>Maps of Mauritius demonstrating extant distribution of <span class="html-italic">Ipomoea</span> species: (<b>a</b>) <span class="html-italic">I. alba,</span> (<b>b</b>) <span class="html-italic">I. indica,</span> (<b>c</b>) <span class="html-italic">I. obscura,</span> (<b>d</b>) <span class="html-italic">I. cairica</span>, and (<b>e</b>) other rare species, around Mauritius based on the temperature dispersion over the island.</p>
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<p>Distribution of (<b>a</b>) <span class="html-italic">I. alba</span>, (<b>b</b>) <span class="html-italic">I. indica</span>, (<b>c</b>) <span class="html-italic">I. obscura</span>, (<b>d</b>) <span class="html-italic">I. cairica</span>, and (<b>e</b>) other rare species, among different soil types.</p>
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<p>Distribution of (<b>a</b>) <span class="html-italic">I. alba</span>, (<b>b</b>) <span class="html-italic">I. indica</span>, (<b>c</b>) <span class="html-italic">I. obscura</span>, (<b>d</b>) <span class="html-italic">I. cairica</span>, and (<b>e</b>) other rare species, among different soil types.</p>
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<p>Altitudinal variation of <span class="html-italic">Ipomoea</span> by taxa.</p>
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<p>Distribution of Ipomoea species in the different agro-climatic regions of Mauritius.</p>
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<p>PCA ordination diagram and plots of studied <span class="html-italic">Ipomoea</span> in relation to abiotic indexes.</p>
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<p>District-wise distribution of the species of <span class="html-italic">Ipomoea</span> within Mauritius.</p>
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<p>Pictures of <span class="html-italic">Ipomoea</span> species encountered during this ecogeographic study.</p>
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<p>Wet markets visited in 2022.</p>
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16 pages, 9124 KiB  
Article
Photosynthetic Performance of Oil Palm Genotypes under Drought Stress
by Carmenza Montoya, Edison Daza, Fernan Santiago Mejía-Alvarado, Arley Fernando Caicedo-Zambrano, Iván Ayala-Díaz, Rodrigo Ruiz-Romero and Hernán Mauricio Romero
Plants 2024, 13(19), 2705; https://doi.org/10.3390/plants13192705 - 27 Sep 2024
Abstract
Water deficiency and potential drought periods could be important ecological factors influencing cultivation areas and productivity once different crops are established. The principal supply of vegetable oil for oil crops is oil palm, and new challenges are emerging in the face of climatic [...] Read more.
Water deficiency and potential drought periods could be important ecological factors influencing cultivation areas and productivity once different crops are established. The principal supply of vegetable oil for oil crops is oil palm, and new challenges are emerging in the face of climatic changes. This study investigated the photosynthetic performance of 12 genotypes of Elaeis exposed to drought stress under controlled conditions. The assay included genotypes of Elaeis guineensis, Elaeis oleifera, and the interspecific O×G hybrid (E. oleifera × E. guineensis). The principal results showed that the E. guineensis genotype was the most efficient at achieving photosynthesis under drought stress conditions, followed by the hybrid and E. oleifera genotypes. The physiological parameters showed good prospects for vegetal breeding with different O×G hybrids, mainly because of their ability to maintain the equilibrium between CO2 assimilation and stomatal aperture. We validated 11 genes associated with drought tolerance, but no differences were detected. These results indicate that no allelic variants were represented in the RNA during sampling for the validated genotypes. In conclusion, this study helps to define genotypes that can be used as parental lines for oil palm improvement. The gas exchange data showed that drought stress tolerance could define guidelines to incorporate the available genetic resources in breeding programs across the early selection in nursery stages. Full article
(This article belongs to the Special Issue Abiotic Stress Responses in Plants)
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<p>A: Net photosynthetic rate (µmol CO<sub>2</sub> m<sup>−2</sup>s<sup>−1</sup>). Treatment by genotype. Tukey’s mean comparison test shows that values with the same letter do not present statistically significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>E: transpiration rate (mmol H<sub>2</sub>O m<sup>−2</sup>s<sup>−1</sup>). Treatment by genotype. Tukey’s mean comparison test shows that values with the same letter do not present statistically significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>gs: stomatal conductance (mmol H<sub>2</sub>O m<sup>−2</sup>s<sup>−1</sup>). Treatment by genotype. Tukey’s mean comparison test shows that values with the same letter do not present statistically significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Δw: (leaf-to-air vapor pressure deficit). Treatment by genotype. Tukey’s mean comparison test shows that values with the same letter do not present statistically significant differences (<span class="html-italic">p</span> &lt; 0.05). No letter for Tukey’s mean comparison test does not present statistically significant differences.</p>
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<p>WUE: instantaneous water use efficiency (µmol CO<sub>2</sub>/mmol H<sub>2</sub>O). Treatment by genotype. Tukey’s mean comparison test shows that values with the same letter do not present statistically significant differences (<span class="html-italic">p</span> &lt; 0.05). No letter for Tukey’s mean comparison test does not present statistically significant differences.</p>
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<p>Ψleaf: Leaf water potential (bar). Tukey’s mean comparison test shows that values with the same letter do not present statistically significant differences (<span class="html-italic">p</span> &lt; 0.05). No letter for Tukey’s mean comparison test does not present statistically significant differences.</p>
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<p>Conventional PCR of 11 genes associated with drought tolerance in a 1.5% agarose gel stained with SYBR Safe. DNA (120 ng/µL) was used as a template for each reaction. Conclusions: M: Molecular weight marker. A. 1433; B. 1530; C. 1451; D. 1147; E. 1381; F. 1915. WRKY51: WRKY transcription factor 51; NAM-B2: NAC transcription factor NAM-B2-like_NAM-B2; XYL 2: β-xylosidase α-L-arabinofuranosidase 2-like; BAM1: leucine-rich repeat receptor-like serine; CCR4: serine, threonine-protein kinase-like protein; At5g39980: pentatricopeptide repeat-containing protein; MCTP2: multiple C2 and transmembrane domain-containing protein 2-like; NSLT: nonspecific lipid-transfer protein 2-like; SWEET14: bidirectional sugar transporter SWEET14-like; GOLS1: galactinol synthase 1-like_GOLS1; XTH22: xyloglucan endotransglucosylase/hydrolase protein 22-like XTH22.</p>
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14 pages, 4987 KiB  
Article
FtMYB163 Gene Encodes SG7 R2R3-MYB Transcription Factor from Tartary Buckwheat (Fagopyrum tataricum Gaertn.) to Promote Flavonol Accumulation in Transgenic Arabidopsis thaliana
by Hanmei Du, Jin Ke, Xiaoqian Sun, Lu Tan, Qiuzhu Yu, Changhe Wei, Peter R. Ryan, An’hu Wang and Hongyou Li
Plants 2024, 13(19), 2704; https://doi.org/10.3390/plants13192704 - 27 Sep 2024
Abstract
Tartary buckwheat (Fagopyrum tataricum Gaertn.) is a coarse grain crop rich in flavonoids that are beneficial to human health because they function as anti-inflammatories and provide protection against cardiovascular disease and diabetes. Flavonoid biosynthesis is a complex process, and relatively little is [...] Read more.
Tartary buckwheat (Fagopyrum tataricum Gaertn.) is a coarse grain crop rich in flavonoids that are beneficial to human health because they function as anti-inflammatories and provide protection against cardiovascular disease and diabetes. Flavonoid biosynthesis is a complex process, and relatively little is known about the regulatory pathways involved in Tartary buckwheat. Here, we cloned and characterized the FtMYB163 gene from Tartary buckwheat, which encodes a member of the R2R3-MYB transcription factor family. Amino acid sequence and phylogenetic analysis indicate that FtMYB163 is a member of subgroup 7 (SG7) and closely related to FeMYBF1, which regulates flavonol synthesis in common buckwheat (F. esculentum). We demonstrated that FtMYB163 localizes to the nucleus and has transcriptional activity. Expression levels of FtMYB163 in the roots, stems, leaves, flowers, and seeds of F. tataricum were positively correlated with the total flavonoid contents of these tissues. Overexpression of FtMYB163 in transgenic Arabidopsis enhanced the expression of several genes involved in early flavonoid biosynthesis (AtCHS, AtCHI, AtF3H, and AtFLS) and significantly increased the accumulation of several flavonoids, including naringenin chalcone, naringenin-7-O-glucoside, eriodictyol, and eight flavonol compounds. Our findings demonstrate that FtMYB163 positively regulates flavonol biosynthesis by changing the expression of several key genes in flavonoid biosynthetic pathways. Full article
(This article belongs to the Section Plant Molecular Biology)
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<p>Multiple sequence alignment of FtMYB163. The transcription factors were FeMYBF1 (LC369592) from common buckwheat, FtMYB6 (QPC96978) from Tartary buckwheat, AtMYB12 (AEC10843) from Arabidopsis, VvMYBF1 (FJ948477) from grape, and CsMYBF1 (KT727073) from citrus. Identical (100%), conservative (75–99%), and blocks (50–74%) of similar amino acid residues are shaded in deep blue, cherry red, and cyan, respectively. The R2/R3 SANT domain and SG7 motif1/2 are indicated in the red line and red box, respectively.</p>
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<p>Phylogenetic analysis of FtMYB163. GeneBack accession numbers are listed as follows: AtMYB58 (NP_173098), AtMYB63 (NP_001321204), LlMYB1 (ADY38393), ZmMYB31 (NP_001105949), ZmMYB42 (NP_001106009), MdMYB3 (AEX08668), AtMYB7 (NP_179263), AtMYB4 (AAC83582), AtMYB8 (NP_849749), AtMYB32 (NP_195225), AtMYB3 (NP_564176), FtMYB2 (AEC32976), FaMYB11 (USN17649), FaMYB9 (USN17648), MdMYB9 (NP_001280749), PtMYB134 (ACR83705), VvMYBPA1 (NP_001268160), VvMYBPA2 (NP_001267953), PpMYB10 (ADK73605), AN2 (BAP28593), ANT1 (WDP81135), IbMYB1 (BAG68211), AtMYB113 (NP_176811), AtMYB114 (NP_176812), GtMYBP3 (AB733016), MdMYB22 (DQ074470), AtMYB111 (AAK97396), AtMYB11 (NP_191820), MsMYB (AQR58379), CcMYB12 (AXF92691), AtMYB90 (AAG42002), AtPAP1 (NP_172830), AtMYB75 (NP_176057), AtMYB43 (NP_197163), AtMYB20 (NP_176797), FtMYB1 (AEC32973), FtMYB31 (AIZ97491). FtMYB163 is highlighted with a red dot.</p>
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<p>Subcellular localization and transcription activation activities of FtMYB163. (<b>A</b>) Subcellular localization of FtMYB163-GFP fusion protein in <span class="html-italic">Nicotiana benthamiana</span> leaves. GFP: Green fluorescent protein; DAPI: 4′,6-diamidino-2-phenylindole stain; 16318-hGFP was used as the control. Scale bar: 20 μm. (<b>B</b>) Transcription activation analysis of FtMYB163 in yeast AH109 cells. The transformed cells were plated on an (<b>a</b>) SD/-Leu/-Trp, (<b>b</b>) SD/-Ade/-His/-Leu/-Trp, and (<b>c</b>) SD/-Ade/-His/-Leu/-Trp/x-α-gal medium and incubated in an incubator at 30 °C for 3~5 d. pGBKT7, negative control; pGBKT7-<span class="html-italic">53</span>, positive control.</p>
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<p>Expression pattern of <span class="html-italic">FtMYB163</span> and the total flavonoid content in Tartary buckwheat. The expression of <span class="html-italic">FtMYB163</span> in (<b>A</b>) R (roots), ST (stems), L (leaves), and F (flowers) at sprouts, six-leaf stage, and maturation stage and (<b>B</b>) in seeds at different developmental stages. (<b>C</b>) Gene expression clustering heat map of <span class="html-italic">FtMYB163</span> and Tartary buckwheat flavonoid biosynthesis structure. (<b>D</b>) <span class="html-italic">FtMYB163</span> expression analysis and (<b>E</b>) the total flavonoid content detection in each tissue at the corresponding period (as in (<b>C</b>)). (<b>F</b>) The correlation between the <span class="html-italic">FtMYB163</span> expression in different tissues and the content of total flavonoids. D4~D30, the seeds 4~30 days after flowering; L1, top one leaf of adult plants; L2, top three leaves of adult plants; S1, seeds before grouting; S2, seeds at filling stage; S3, mature seeds. Blue asterisks indicated the structural genes co-expressed with <span class="html-italic">FtMYB163</span> during the three seed development periods. The expression levels were evaluated by the 2<sup>−ΔΔCT</sup> method, and <span class="html-italic">FtActin7</span> was used as a reference gene. The values are represented as mean ± SD (n = 5) and marked with different letters to indicate statistically significant differences at <span class="html-italic">p</span> &lt; 0.05 (Tukey’s test).</p>
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<p>Characterization of T<sub>3</sub> transgenic <span class="html-italic">Arabidopsis</span> lines expressing <span class="html-italic">FtMYB163.</span> (<b>A</b>) Relative expression levels of <span class="html-italic">FtMYB163</span> in independent homozygous T<sub>3</sub> lines using qRT-PCR. <span class="html-italic">AtACT2</span> was used as a reference gene. Data show the mean ± SD of three biological replicates. Different letters indicate significant differences at <span class="html-italic">p</span> &lt; 0.05 (Student’s <span class="html-italic">t</span>-test). (<b>B</b>) Total flavonoid content of WT <span class="html-italic">Arabidopsis</span> and three transgenic T<sub>3</sub> lines expressing <span class="html-italic">FtMYB163</span> (OE-2, OE-5, and OE-7). Values are mean ± SD (n = 10), and different letters indicate significant differences at <span class="html-italic">p</span> &lt; 0.01 (Tukey’s test).</p>
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<p>Assessing how the expression of <span class="html-italic">FtMYB163</span> in <span class="html-italic">Arabidopsis</span> leaves affects the expression of endogenous genes in the flavonoid synthesis pathway. The relative expression of Arabidopsis genes (<b>A</b>) flavonol synthase (<span class="html-italic">AtFLS</span>), (<b>B</b>) chalcone synthase (<span class="html-italic">AtCHS</span>), (<b>C</b>) chalcone isomerase (<span class="html-italic">AtCHI</span>), (<b>D</b>) flavonoid 3-hydroxylase (<span class="html-italic">AtF3H</span>), (<b>E</b>) flavonoid 3′-hydroxylase (<span class="html-italic">AtF3′H</span>), (<b>F</b>) dihydroflavonol 4-reductase (<span class="html-italic">AtDFR</span>), and (<b>G</b>) anthocyanidin reductase (<span class="html-italic">AtANS</span>) were measured in WT and transgenic lines (OE-2, OE-5, and OE-7). Gene expression in the WT was set to 1.0 to provide fold changes in expression. The <span class="html-italic">AtACT2</span> gene was used as a reference. Values represent mean ± SD (n = 5), and different letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.01) (Tukey’s test).</p>
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13 pages, 3720 KiB  
Article
Isolation and Plant Growth Promotion Effect of Endophytic Siderophore-Producing Bacteria: A Study on Halophyte Sesuvium portulacastrum
by Xinyi Cen, Hua Li, Yanhua Zhang, Lingfeng Huang and Yuanrong Luo
Plants 2024, 13(19), 2703; https://doi.org/10.3390/plants13192703 - 27 Sep 2024
Abstract
The objective of the present study was to isolate endophytes from the roots of the halophyte Sesuvium portulacastrum, which is applied for aquatic phytoremediation. From these endophytes, siderophore-producing bacteria were specifically isolated for their potential capacity to promote plant growth. The siderophore [...] Read more.
The objective of the present study was to isolate endophytes from the roots of the halophyte Sesuvium portulacastrum, which is applied for aquatic phytoremediation. From these endophytes, siderophore-producing bacteria were specifically isolated for their potential capacity to promote plant growth. The siderophore production capacity of the isolated bacteria was quantified, and a high-yield siderophore-producing strain was selected for further investigation. A total of 33 endophytic bacteria were successfully isolated and identified using a culturable approach. Of these, 10 siderophore-producing bacteria were identified using the selective agar assay, displaying siderophore unit (SU) values ranging from 11.90% to 80.39%. It is noteworthy that Erwinia sp. QZ-E9 exhibited the highest siderophore production capacity, achieving an SU of 80.39%. A microcosm co-cultivation experiment was conducted with the strain QZ-E9 in iron-deficient conditions (2 μmol/L Fe3⁺). The results demonstrated that strain QZ-E9 significantly enhanced the growth of S. portulacastrum, by increases in both fresh weight (1.41 g) and root length (18.7 cm). Furthermore, fluorescence in situ hybridization (FISH) was utilized to ascertain the colonization pattern of strain QZ-E9 within the plant roots. The analysis demonstrated that strain QZ-E9 exhibited extensive colonization of the epidermal and outer cortical cells of S. portulacastrum roots, as well as the intercellular spaces and vascular tissues. This colonization indicated that Erwinia sp. QZ-E9 plays a crucial role in promoting the growth of S. portulacastrum, presumably through its siderophore-mediated iron acquisition mechanism. Full article
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<p>Phylogenetic analysis of isolated bacteria based on 16S rRNA gene sequences.</p>
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<p>Siderophore-producing bacteria determined by CAS agar plate. ((<b>A</b>): Strain QZ-E9; (<b>B</b>): Strain QZ-E23).</p>
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<p>OD<sub>600</sub> and SU values of <span class="html-italic">Erwinia</span> sp. QZ-E9 in different iron ion concentrations. The solid line is the change in the OD<sub>600</sub> value of the strain under different addition groups, and the dotted line is the change in SU value under different addition groups.</p>
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<p>Fresh weight variations of <span class="html-italic">S. portulacastrum</span> under different treatments.</p>
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<p>Root length variations of <span class="html-italic">S. portulacastrum</span> under different treatments.</p>
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<p><span class="html-italic">S. portulacastrum</span> inoculated with <span class="html-italic">Erwinia</span> sp. QZ-E9 in 0 μmol/L iron ion.</p>
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<p>FISH images inside the roots of <span class="html-italic">S. portulacastrum</span> under different treatments ((<b>A</b>) shows the excited image of the EUB338 probe labeled with Cy3 only in group P; (<b>B</b>) shows the excited image of the EUB338 probe labeled with red fluorescent Cy3 in group P + B; and (<b>C</b>) shows the excited image of the probe labeled with Cy3 and the probe labeled with green fluorescent FITC at the same time in group P + B. The magnification is 10× and all the lengths of the scales in the figure are 50 μmol/L).</p>
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20 pages, 5610 KiB  
Article
Biochemical and Proteomic Analyses in Drought-Tolerant Wheat Mutants Obtained by Gamma Irradiation
by Ayşe Şen, Tamer Gümüş, Aslıhan Temel, İrfan Öztürk and Özge Çelik
Plants 2024, 13(19), 2702; https://doi.org/10.3390/plants13192702 - 27 Sep 2024
Abstract
The bread wheat cultivar (Triticum aestivum L. cv. Sagittario) as a parental line and its mutant, drought-tolerant lines (Mutant lines 4 and 5) were subjected to polyethylene glycol (PEG)-induced drought. Drought stress resulted in decreased chlorophyll levels and the accumulation of proline [...] Read more.
The bread wheat cultivar (Triticum aestivum L. cv. Sagittario) as a parental line and its mutant, drought-tolerant lines (Mutant lines 4 and 5) were subjected to polyethylene glycol (PEG)-induced drought. Drought stress resulted in decreased chlorophyll levels and the accumulation of proline and TBARS, despite increases in activities of catalase, peroxidase, and superoxide dismutase enzymes. Transcription of the genes encoding these enzymes and delta-1-pyrroline 5-carboxylase synthetase was induced by drought. 2-DE gel electrophoresis analysis identified differentially expressed proteins (DEPs) in the mutant lines, which are distinguished by “chloroplast”, “mitochondrion”, “pyruvate dehydrogenase complex”, and “homeostatic process” terms. The drought tolerance of the mutant lines might be attributed to improved photosynthesis, efficient ATP synthesis, and modified antioxidant capacity. In addition to proteomics data, the drought tolerance of wheat genotypes might also be assessed by chlorophyll content and TaPOX gene expression. To our knowledge, this is the first proteomic analysis of gamma-induced mutants of bread wheat. These findings are expected to be utilized in plant breeding studies. Full article
(This article belongs to the Special Issue Abiotic Stress Responses in Plants)
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<p>Effects of PEG-induced osmotic stress on chlorophyll content (<b>A</b>), proline content (<b>B</b>), and TBARS content (<b>C</b>) of the parental line (commercial cultivar) and its mutant lines 4 and 5. Values are the arithmetic means of biological pentaplicates (<span class="html-italic">n</span> = 5), and the error bars correspond to the standard deviations (SD). Significant differences were determined according to Tukey’s HSD test performed after a 2-way ANOVA. The letters on the columns represent the statistical significance among the genotypes under the same conditions (control or PEG). Columns indicated by different letters are statistically different (<span class="html-italic">p</span> &lt; 0.05). Asterisks represent the statistical significance of a genotype between different conditions as follows: * <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.001, and ns (not significant).</p>
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<p>Effects of PEG-induced osmotic stress on the activities of the enzymes CAT (<b>A</b>), POX (<b>B</b>), and SOD (<b>C</b>) of the parental line (commercial cultivar) and its mutant lines 4 and 5. Values are the arithmetic means of biological pentaplicates (<span class="html-italic">n</span> = 5), and the error bars correspond to the standard deviations (SD). Significant differences were determined according to Tukey’s HSD test performed after a 2-way ANOVA. The letters on the columns represent the statistical significance among the genotypes under the same conditions (control or PEG). Columns indicated by different letters are statistically different (<span class="html-italic">p</span> &lt; 0.05). Asterisks represent the statistical significance of a genotype between different conditions as follows: *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Effects of PEG-induced osmotic stress on the expression of the genes <span class="html-italic">TaCAT</span> (<b>A</b>), <span class="html-italic">TaP5CS</span> (<b>B</b>), <span class="html-italic">TaPOX</span> (<b>C</b>), and <span class="html-italic">TaSOD2</span> (<b>D</b>) of the parental line (commercial cultivar) and its mutant lines 4 and 5. Values are the arithmetic means of biological triplicates (<span class="html-italic">n</span> = 3), and the error bars correspond to the standard deviations (sd). Columns indicated by different letters are statistically different (<span class="html-italic">p</span> &lt; 0.05) at the same conditions (control or PEG). Significant differences were determined according to Tukey’s HSD test at *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Gene ontology (GO) analyses. Pie charts show the PEG-induced GO terms of cellular component (<b>A</b>,<b>D</b>), molecular function (<b>B</b>,<b>E</b>) and biological process (<b>C</b>,<b>F</b>) in the parental line (<b>A</b>–<b>C</b>) and in the mutant lines (<b>D</b>–<b>F</b>).</p>
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<p>Gene ontology (GO) analyses. Pie charts show the PEG-repressed GO terms of cellular component (<b>A</b>,<b>D</b>), molecular function (<b>B</b>,<b>E</b>) and biological process (<b>C</b>,<b>F</b>) in the parental line (<b>A</b>–<b>C</b>) and in the mutant lines (<b>D</b>–<b>F</b>).</p>
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21 pages, 2695 KiB  
Article
Establishment of a Sensitive and Reliable Droplet Digital PCR Assay for the Detection of Bursaphelenchus xylophilus
by Yu Su, Xuedong Zhu, Haozheng Jing, Haiying Yu and Huai Liu
Plants 2024, 13(19), 2701; https://doi.org/10.3390/plants13192701 - 26 Sep 2024
Abstract
Pine wilt disease (PWD), which poses a significant risk to pine plantations across the globe, is caused by the pathogenic agent Bursaphelenchus xylophilus, also referred to as the pine wood nematode (PWN). A droplet digital PCR (ddPCR) assay was developed for the [...] Read more.
Pine wilt disease (PWD), which poses a significant risk to pine plantations across the globe, is caused by the pathogenic agent Bursaphelenchus xylophilus, also referred to as the pine wood nematode (PWN). A droplet digital PCR (ddPCR) assay was developed for the quick identification of the PWN in order to improve detection sensitivity. The research findings indicate that the ddPCR assay demonstrated significantly higher analysis sensitivity and detection sensitivity in comparison to traditional quantitative PCR (qPCR). However, it had a more limited dynamic range. High specificity was shown by both the ddPCR and qPCR techniques in the diagnosis of the PWN. Assessments of reproducibility revealed that ddPCR had lower coefficients of variation at every template concentration. Inhibition tests showed that ddPCR was less susceptible to inhibitors. There was a strong linear association between standard template measurements obtained using ddPCR and qPCR (Pearson correlation = 0.9317; p < 0.001). Likewise, there was strong agreement (Pearson correlation = 0.9348; p < 0.001) between ddPCR and qPCR measurements in the evaluation of pine wood samples. Additionally, wood samples from symptomatic (100% versus 86.67%) and asymptomatic (31.43% versus 2.9%) pine trees were diagnosed with greater detection rates using ddPCR. This study’s conclusions highlight the advantages of the ddPCR assay over qPCR for the quantitative detection of the PWN. This method has a lot of potential for ecological research on PWD and use in quarantines. Full article
(This article belongs to the Special Issue New Strategies for the Control of Plant-Parasitic Nematodes)
27 pages, 2419 KiB  
Review
Impact of Abiotic Stress on Rice and the Role of DNA Methylation in Stress Response Mechanisms
by Ming Yin, Shanwen Wang, Yanfang Wang, Ronghua Wei, Yawei Liang, Liying Zuo, Mingyue Huo, Zekai Huang, Jie Lang, Xiuqin Zhao, Fan Zhang, Jianlong Xu, Binying Fu, Zichao Li and Wensheng Wang
Plants 2024, 13(19), 2700; https://doi.org/10.3390/plants13192700 - 26 Sep 2024
Abstract
With the intensification of global climate change and the increasing complexity of agricultural environments, the improvement of rice stress tolerance is an important focus of current breeding research. This review summarizes the current knowledge on the impact of various abiotic stresses on rice [...] Read more.
With the intensification of global climate change and the increasing complexity of agricultural environments, the improvement of rice stress tolerance is an important focus of current breeding research. This review summarizes the current knowledge on the impact of various abiotic stresses on rice and the associated epigenetic responses (DNA methylation). Abiotic stress factors, including high temperature, drought, cold, heavy metal pollution, and high salinity, have a negative impact on crop productivity. Epigenetic changes are key regulatory factors in plant stress responses, and DNA methylation is one of the earliest discovered and thoroughly studied mechanisms in these epigenetic regulatory mechanisms. The normal growth of rice is highly dependent on the environment, and changes in the environment can lead to rice sterility and severe yield loss. Changes in the regulation of the DNA methylation pathway are involved in rice’s response to stress. Various DNA methylation-regulating protein complexes that function during rice development have been identified. Significant changes in DNA methylation occur in numerous stress-responsive genes, particularly those in the abscisic acid signaling pathway. These findings underscore the complex mechanisms of the abiotic stress response in rice. We propose the effective improvement of tolerance traits by regulating the epigenetic status of rice and emphasize the role of DNA methylation in abiotic stress tolerance, thereby addressing global climate change and ensuring food security. Full article
(This article belongs to the Special Issue Mechanisms of Plant Regulation against Environmental Stress)
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<p>Schematic diagram of the mechanism of abiotic stress response in rice.</p>
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<p>RNA-directed DNA methylation pathway model (<b>a</b>) POL IV-dependent siRNA biogenesis; (<b>b</b>) POL II-dependent siRNA biogenesis; (<b>c</b>) POL V-mediated de novo and maintenance methylation; (<b>d</b>) chromatin alterations.</p>
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<p>Specific DNA methyltransferases and demethylases mediate cytosine methylation in different sequence contexts.</p>
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<p>Schematic illustration of DNA methylation and abiotic stress tolerance in rice.</p>
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19 pages, 1840 KiB  
Article
Pomological and Molecular Characterization of Apple Cultivars in the German Fruit Genebank
by Lea Broschewitz, Stefanie Reim, Henryk Flachowsky and Monika Höfer
Plants 2024, 13(19), 2699; https://doi.org/10.3390/plants13192699 - 26 Sep 2024
Abstract
Traditional varieties are a valuable tool in modern apple breeding. However, the use of synonyms and missing source documentation hinder an effective identification and conservation of relevant cultivars. During several projects, the authenticity and diversity of the apple cultivar collection of the German [...] Read more.
Traditional varieties are a valuable tool in modern apple breeding. However, the use of synonyms and missing source documentation hinder an effective identification and conservation of relevant cultivars. During several projects, the authenticity and diversity of the apple cultivar collection of the German Fruit Genebank (GFG) was evaluated extensively. The trueness-to-type of 7890 apple trees was assessed on a pomological and molecular level. Pomological evaluations were performed by at least two experienced experts to identify the original cultivar names. On the molecular level, a set of 17 SSR markers was used to determine a unique genetic profile for each apple cultivar. The pomological and molecular characterization was expressed in terms of a comprehensive trueness-to-type criterion and the results were previously published as a well-curated dataset. In this study, the published dataset was analyzed to evaluate the quality and diversity of the apple collection of the GFG and highlight new findings based on phylogenetic and parentage analysis. The dataset contains 1404 unique genetic profiles corresponding to unambiguous cultivar names. Of these 1404 cultivars, 74% were assessed as true-to-type. The collection of diploid apple cultivars showed a high degree of expected heterozygosity (Hexp = 0.84). Genetic diversity in terms of year and location of origin was investigated with a STRUCTURE analysis. It was hypothesized that genetic diversity might decline overtime due to restrictive breeding programs. The results showed a shift dynamic between older and newer cultivars in one specific cluster, but no significant decrease in genetic diversity was observed in this study. Lastly, a parentage analysis was performed to check parental relationships based on historical research. Out of 128 parent–child trios, 110 trios resulted in significant relationships and reconfirmed the information from the literature. In some cases, the information from the literature was disproven. This analysis also allowed for readjusting the trueness-to-type criteria for previously undetermined cultivars. Overall, the importance of authenticity evaluations for gene bank cultivars was highlighted. Furthermore, the direct use of the dataset was shown by relevant investigations on the genetic diversity and structure of the apple cultivar collections of the GFG. Full article
(This article belongs to the Section Plant Genetic Resources)
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<p>Representation of 1404 apple genotypes corresponding to cultivars in the German Fruit Genebank (GFG) sectioned by authenticity of cultivar identification (trueness-to-type) given in percentage and count.</p>
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<p>Combined effect of discrimination power of 17 SSR markers represented by average non-exclusion probability of identity of two unrelated individuals (PID) and average non-exclusion probability of identity of two siblings (PIDsib). The markers required for near 100% discrimination of the apple genotypes are marked in yellow.</p>
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<p>Genetic structure of 569 apple genotypes based on STRUCTURE analysis of 17 SSR markers at predefined K = 4. Each genotype is represented by a vertical bar that is partitioned in color according to the inferred membership fraction to clusters 1 to 4 (C1 to C4). The data are sorted by Q-value per age group. The expected heterozygosity (<span class="html-italic">H<sub>exp</sub></span>) per age group is given. Age groups refer to the respective age range of the cultivars.</p>
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<p>Phylogenetic tree of the genetic structure analysis results. Genetic data of 17 SSR markers of 569 apple cultivars were used for tree construction using the neighbor-joining method. Cultivars are marked according to their respective dominant cluster (C1 to C4; Clusters 1 to 4) based on the inferred membership fraction. In each cluster, the cultivar with the highest inferred membership fraction is marked with red font color and additional labels. A high-resolution image is provided in <a href="#app1-plants-13-02699" class="html-app">Supplementary Figure S2</a>.</p>
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17 pages, 12872 KiB  
Article
Altitude-Dependent Morphophysiological, Anatomical, and Metabolomic Adaptations in Rhodiola linearifolia Boriss.
by Nina V. Terletskaya, Malika Erbay, Aigerim Mamirova, Kazhybek Ashimuly, Nazym K. Korbozova, Aigerim N. Zorbekova, Nataliya O. Kudrina and Matthias H. Hoffmann
Plants 2024, 13(19), 2698; https://doi.org/10.3390/plants13192698 - 26 Sep 2024
Abstract
Rhodiola linearifolia Boriss., a perennial alpine plant from the Crassulaceae family, is renowned for its unique medicinal properties. However, existing research on this species is limited, particularly regarding the impact of altitude on its physiological and medicinal compounds. The current study employed morphophysiological [...] Read more.
Rhodiola linearifolia Boriss., a perennial alpine plant from the Crassulaceae family, is renowned for its unique medicinal properties. However, existing research on this species is limited, particularly regarding the impact of altitude on its physiological and medicinal compounds. The current study employed morphophysiological and anatomical methods to explore the adaptive mechanisms of R. linearifolia across different altitudinal gradients, while also examining photosynthetic pigments and metabolomic changes. Our results indicate that despite the simultaneous effects of various mountain abiotic factors, significant correlations can be identified between altitude and trait variation. An optimal growth altitude of 2687 m above sea level was identified, which is pivotal for sustainable ecosystem management and potential species introduction strategies. It is noted that increasing altitude stress enhances the synthesis of secondary antioxidant metabolites in R. linearifolia, enhancing its pharmaceutical potential. Full article
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<p><span class="html-italic">R. linearifolia</span> morphometric parameter alterations depending on altitude. Note: (<b>a</b>) plant height, cm; (<b>b</b>) leaf area, cm<sup>2</sup>. Different letters within one parameter show significant difference (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Anatomical structure of <span class="html-italic">R. linearifolia</span> leaves depending on altitude. Note: a—adaxial epidermis; b—abaxial epidermis; c—mesophyll; d—central vascular bundle. Scale bar = 100 µm.</p>
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<p>Anatomical structure of <span class="html-italic">R. linearifolia</span> stems depending on altitude. Note: a—phloem; b—xylem; c—chlorenchyma; d—parenchyma; e—sclerenchyma; f—collenchyma; g—epidermis. Scale bar = 100 µm.</p>
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<p>Chlorophyll pigments content (mg g<sup>−1</sup> FW) in <span class="html-italic">R. linearifolia</span> shoots depending on altitude. The bars are arranged with increasing altitude from left to right. Different letters within one parameter show significant difference (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p><span class="html-italic">R. linearifolia</span> flower color alterations depending on altitude.</p>
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<p>Changes in carotenoid content in flowers and shoots of <span class="html-italic">R. linearifolia</span> depending on altitude. The bars are arranged with increasing altitude from left to right. Different letters within one parameter show significant difference (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Metabolome alterations in flowers (<b>a</b>) and shoots (<b>b</b>) of <span class="html-italic">R. linearifolia</span> depending on altitude. Note: AA—amino acid; CA—carboxylic acid; D—derivatives; FA—fatty acid; SMCH—saturated monocyclic hydrocarbons.</p>
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<p>Pearson correlation heatmap (for the metabolite content in flowers). Note: Car—carotenoids; LHC—light-harvesting complex; D—derivatives; FA—fatty acid; SMCH—saturated monocyclic hydrocarbons.</p>
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<p>Pearson correlation heatmap (for the metabolite content in shoots). Note: Car—carotenoids; LHC—light-harvesting complex; D—derivatives; AA—amino acid; CA—carboxylic acid; FA—fatty acid.</p>
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21 pages, 14187 KiB  
Article
Functional Analysis of the PoSERK-Interacting Protein PorbcL in the Embryogenic Callus Formation of Tree Peony (Paeonia ostii T. Hong et J. X. Zhang)
by Yinglong Song, Jiange Wang, Jiale Zhu, Wenqian Shang, Wenqing Jia, Yuke Sun, Songlin He, Xitian Yang and Zheng Wang
Plants 2024, 13(19), 2697; https://doi.org/10.3390/plants13192697 - 26 Sep 2024
Abstract
SERK is a marker gene for early somatic embryogenesis. We screened and functionally verified a SERK-interacting protein to gain insights into tree-peony somatic embryogenesis. Using PoSERK as bait, we identified PorbcL (i.e., the large subunit of Rubisco) as a SERK-interacting protein from a [...] Read more.
SERK is a marker gene for early somatic embryogenesis. We screened and functionally verified a SERK-interacting protein to gain insights into tree-peony somatic embryogenesis. Using PoSERK as bait, we identified PorbcL (i.e., the large subunit of Rubisco) as a SERK-interacting protein from a yeast two-hybrid (Y2H) library of cDNA from developing tree-peony somatic embryos. The interaction between PorbcL and PoSERK was verified by Y2H and bimolecular fluorescence complementation analyses. PorbcL encodes a 586-amino-acid acidic non-secreted hydrophobic non-transmembrane protein that is mainly localized in the chloroplast and plasma membrane. PorbcL was highly expressed in tree-peony roots and flowers and was up-regulated during zygotic embryo development. PorbcL overexpression caused the up-regulation of PoSERK (encoding somatic embryogenesis receptor-like kinase), PoAGL15 (encoding agamous-like 15), and PoGPT1 (encoding glucose-6-phosphate translocator), while it caused the down-regulation of PoLEC1 (encoding leafy cotyledon 1) in tree-peony callus. PorbcL overexpression led to increased indole-3-acetic acid (IAA) content but decreasing contents of abscisic acid (ABA) and 6-benzyladenosine (BAPR). The changes in gene expression, high IAA levels, and increased ratio of IAA to ABA, BAPR, 1-Aminocyclopropanecarboxylic acid (ACC), 5-Deoxystrigol (5DS), and brassinolide (BL) promoted embryogenesis. These results provide a foundation for establishing a tree-peony embryogenic callus system. Full article
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<p>Yeast library construction ((<b>A</b>), Agarose gel electrophoresis of Total RNA (M, 250 bp NDA Ladder; 1, Total RNA); (<b>B</b>), Agarose gel electrophoresis of mRNA (M, 1 Kb Plus DNA Ladder; 1, mRNA); (<b>C</b>), Identification of primary library capacity; (<b>D</b>), Identification of inserted fragment length (M, 250 bp NDA Ladder; 1–24, Gene fragment); (<b>E</b>), Identification of secondary library capacity; (<b>F</b>), Identification of inserted fragment length (M, 250 bp NDA Ladder; 1–24, Gene fragment)).</p>
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<p>Sequence analysis of PorbcL ((<b>A</b>), Conserved domains; (<b>B</b>), Sequence alignment; (<b>C</b>), Signal peptide analysis; (<b>D</b>), Transmembrane domain analysis; (<b>E</b>), Phylogenetic analysis; (<b>F</b>), Secondary structure of protein; (<b>G</b>), Tertiary structure of protein).</p>
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<p>Subcellular localization of PorbcL ((<b>A</b>), Merged; (<b>B</b>), Chlorophyll; (<b>C</b>), GFP; (<b>D</b>), Merged (partial); (<b>E</b>), Co-localization). The location of the fusion protein is indicated by the presence of green fluorescence. Scale bar: (<b>A</b>–<b>C</b>) 20 μm, (<b>D</b>) 80 μm.</p>
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<p>Interaction validation of <span class="html-italic">PoSERK</span> with <span class="html-italic">PorbcL</span> by Y2H and BiFC (Y2H: (<b>A</b>), SD-TL; (<b>B</b>), SD-TLHA; (<b>C</b>), SD-TLHA+X-<span class="html-italic">α</span>-gal, the bacterial solution was diluted 10, 10<sup>2</sup>, 10<sup>3</sup>, 10<sup>4</sup> times respectively. BiFC: (<b>D</b>), Merged; (<b>E</b>), FITC (green); (<b>F</b>), TRITC (red); (<b>G</b>), Bright. Scale bar = 50 μm).</p>
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<p>Spatiotemporal expression levels of <span class="html-italic">PorbcL</span> in tree peony ((<b>A</b>), Different parts; (<b>B</b>), Different zygotic embryo development stages). Standard error indicated by vertical bars (<span class="html-italic">n</span> = 3). Significant differences were denoted by distinct letters, as determined by the Duncan’s multiple range test (<span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Positive identification and quantification of embryogenesis-related gene expression in <span class="html-italic">PorbcL</span> transgenic calli of tree peony via RT-qPCR analysis ((<b>A</b>), <span class="html-italic">PorbcL</span>; (<b>B</b>), <span class="html-italic">PoSERK</span>; (<b>C</b>), <span class="html-italic">PoLEC1</span>; (<b>D</b>), <span class="html-italic">PoAGL15</span>; (<b>E</b>), <span class="html-italic">PoGPT1</span>). Standard error indicated by vertical bars (<span class="html-italic">n</span> = 3). Significant differences were denoted by distinct letters, as determined by the Duncan’s multiple range test (<span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Morphological and histological observation of <span class="html-italic">PorbcL</span> transgenic callus ((<b>A</b>,<b><span class="html-italic">a</span></b>), WT: non-transformed callus; (<b>B</b>,<b><span class="html-italic">b</span></b>), CK: pCAMBIA1302; (<b>C</b>,<b><span class="html-italic">c</span></b>), <span class="html-italic">35s::PorbcL</span>; (<b>D</b>): Quantitative analysis of embryogenic callus cell mass. Standard error indicated by vertical bars (<span class="html-italic">n</span> = 3). Significant differences were denoted by distinct letters, as determined by the Duncan’s multiple range test (<span class="html-italic">p</span> ≤ 0.05). The black arrow points to the embryogenic callus mass. Scale bar: (<b>A</b>–<b>C</b>) 100 μm, (<b><span class="html-italic">a</span></b>–<b><span class="html-italic">c</span></b>) 1000 μm).</p>
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<p>Endogenous hormone contents of <span class="html-italic">PorbcL</span> transgenic callus ((<b>A</b>), IAA; (<b>B</b>), ABA; (<b>C</b>), BARP; (<b>D</b>), ACC; (<b>E</b>), 5DS; (<b>F</b>), BL). Standard error indicated by vertical bars (<span class="html-italic">n</span> = 3). Significant differences were denoted by distinct letters, as determined by the Duncan’s multiple range test (<span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Endogenous hormone ratios of <span class="html-italic">PorbcL</span> transgenic callus ((<b>A</b>), IAA/ABA; (<b>B</b>), IAA/BARP; (<b>C</b>), IAA/ACC; (<b>D</b>), IAA/5DS; (<b>E</b>), IAA/BL). Standard error indicated by vertical bars (<span class="html-italic">n</span> = 3). Significant differences were denoted by distinct letters, as determined by the Duncan’s multiple range test (<span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Interaction network diagram and enrichment analysis of SERK and its interacting proteins ((<b>A</b>), protein–protein interaction (PPI) network, where the diameter of the circle’s ranges from large to small and the color gradient transitions from deep to light, representing the degree values from high to low; (<b>B</b>), Gene Ontology (GO) enrichment analysis).</p>
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<p>Model map of <span class="html-italic">PorbcL</span> promotion of embryogenic callus formation in tree peony. The red frame represents up-regulation; the blue frame represents down-regulation.</p>
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22 pages, 13024 KiB  
Article
Developmental Morphology, Physiology, and Molecular Basis of the Pentagram Fruit of Averrhoa carambola
by Wanli Tuo, Chunmei Wu, Xuexuan Wang, Zirui Yang, Lianhuan Xu, Siyuan Shen, Junwen Zhai and Shasha Wu
Plants 2024, 13(19), 2696; https://doi.org/10.3390/plants13192696 - 26 Sep 2024
Abstract
Averrhoa carambola, a key tropical and subtropical economic tree in the Oxalidaceae family, is distinguished by its unique pentagram-shaped fruit. This study investigates the developmental processes shaping the polarity of A. carambola fruit and their underlying hormonal and genetic mechanisms. By analyzing [...] Read more.
Averrhoa carambola, a key tropical and subtropical economic tree in the Oxalidaceae family, is distinguished by its unique pentagram-shaped fruit. This study investigates the developmental processes shaping the polarity of A. carambola fruit and their underlying hormonal and genetic mechanisms. By analyzing the Y1, Y2, and Y3 developmental stages—defined by the fruit diameters of 3–4 mm, 4–6 mm, and 6–12 mm, respectively—we observed that both cell number and cell size contribute to fruit development. Our findings suggest that the characteristic pentagram shape is established before flowering and is maintained throughout development. A hormonal analysis revealed that indole-3-acetic acid (IAA) and abscisic acid (ABA) show differential distribution between the convex and concave regions of the fruit across the developmental stages, with IAA playing a crucial role in polar auxin transport and shaping fruit morphology. A transcriptomic analysis identified several key genes, including AcaGH3.8, AcaIAA20, AcaYAB2, AcaXTH6, AcaYAB3, and AcaEXP13, which potentially regulate fruit polarity and growth. This study advances our comprehension of the molecular mechanisms governing fruit shape, offering insights for improving fruit quality through targeted breeding strategies. Full article
(This article belongs to the Section Plant Physiology and Metabolism)
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Figure 1
<p>Observation on the pentacle ovary structure at three development stages of <span class="html-italic">A. carambola</span> flower bud: (<b>A</b>–<b>C</b>): microscopic view of the bud of <span class="html-italic">A. carambola</span> blossom; (<b>A-1</b>,<b>B-1</b>,<b>C-1</b>): visual micro-anatomical observation of the bud of <span class="html-italic">A. carambola</span> blossom; and (<b>A-2</b>,<b>B-2</b>,<b>C-2</b>): microstructure of paraffin section of <span class="html-italic">A. carambola</span> blossom.</p>
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<p>Observation of cell morphology in three developmental stages of <span class="html-italic">A. carambola</span> fruit: (<b>A</b>–<b>C</b>): microscopical view of the fruit of <span class="html-italic">A. carambola</span>; (<b>A-1</b>,<b>B-1</b>,<b>C-1</b>): microstructure of paraffin section of <span class="html-italic">A. carambola</span> fruit; (<b>A-2</b>,<b>B-2</b>,<b>C-2</b>): microstructure diagram of the convex of <span class="html-italic">A. carambola</span> fruit; and (<b>A-3</b>,<b>B-3</b>,<b>C-3</b>): microstructure diagram of the concave of <span class="html-italic">A. carambola</span> fruit.</p>
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<p>Observation of cell morphology in three developmental stages of <span class="html-italic">A. carambola</span> fruit: (<b>A</b>–<b>C</b>): microscopical view of the fruit of <span class="html-italic">A. carambola</span>; (<b>A-1</b>,<b>B-1</b>,<b>C-1</b>): microstructure of paraffin section of <span class="html-italic">A. carambola</span> fruit; (<b>A-2</b>,<b>B-2</b>,<b>C-2</b>): microstructure diagram of the convex of <span class="html-italic">A. carambola</span> fruit; and (<b>A-3</b>,<b>B-3</b>,<b>C-3</b>): microstructure diagram of the concave of <span class="html-italic">A. carambola</span> fruit.</p>
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<p>Determination of cell morphological indexes in three developmental stages of <span class="html-italic">A. carambola</span>. (<b>A</b>) Cell length changes in <span class="html-italic">A. carambola</span> fruit. (<b>B</b>) Cell width changes in <span class="html-italic">A. carambola</span> fruit. (<b>C</b>) Cell area changes in <span class="html-italic">A. carambola</span> fruit. (<b>D</b>) Cell morphology changes in <span class="html-italic">A. carambola</span> fruit. (<b>E</b>) Cell number changes in <span class="html-italic">A. carambola</span> fruit. XT, ZT, and JT represent the convex parts of the Y1, Y2, and Y3 periods of <span class="html-italic">A. carambola</span> fruit. XA, ZA, and JA represent the concave parts of the Y1, Y2, and Y3 periods of <span class="html-italic">A. carambola</span> fruit.</p>
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<p>Scanning Electron Microscopy (SEM) observation of epidermal cells in three developmental stages of <span class="html-italic">A. carambola</span> ovary. (<b>A</b>) SEM observation of y1 stage of <span class="html-italic">A. carambola</span> bud. (<b>B</b>) SEM observation of y2 stage of <span class="html-italic">A. carambola</span> bud. (<b>C</b>) SEM observation of y3 stage of <span class="html-italic">A. carambola</span> bud.</p>
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<p>Changes in auxin content in <span class="html-italic">A. carambola</span> fruit at different periods. XT, ZT, and JT represent the convex parts of the Y1, Y2, and Y3 periods of <span class="html-italic">A. carambola</span> fruit. XA, ZA, and JA represent the concave parts of the Y1, Y2, and Y3 periods of <span class="html-italic">A. carambola</span> fruit. Results of the group comparisons are shown. (<b>A</b>) ZR content map. (<b>B</b>) IAA content chart. (<b>C</b>) GA3 content map. (<b>D</b>) GA4 content map; (<b>E</b>) BR content map. (<b>F</b>) JA-ME content map. (<b>G</b>) IPA content chart. (<b>H</b>) DHZR content map. (<b>I</b>) ABA content chart. Groups with different letters indicate statistically significant differences (<span class="html-italic">p</span> &lt; 0.05). Statistical analysis was performed using one-way ANOVA followed by multiple comparison tests.</p>
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<p>Transcriptomic analysis of <span class="html-italic">A. carambola</span> fruit. (<b>A</b>) Statistical analysis of the results of the DEGs (<span class="html-italic">q</span> &lt; 0.05). (<b>B</b>) Differential gene Ven diagram. (<b>C</b>) Enriched Gene ontology (GO) terms of DEGs between ZT and ZA. (<b>D</b>) KEGG enrichment map of DEGS between ZT and ZA.</p>
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<p>Expression analysis of related functional genes in the pentagram fruit of <span class="html-italic">A. carambola</span>. The expression levels are represented by the colors: red shows up-regulated, and blue shows down-regulated gene expression.</p>
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<p>Analysis of the expression of key genes in different stages of <span class="html-italic">A. carambola</span> fruit development. (<b>A</b>) Expression of <span class="html-italic">AcaGH3.8</span> at different stages. (<b>B</b>) Expression of <span class="html-italic">AcaYAB8</span> at different stages. (<b>C</b>) Expression of <span class="html-italic">AcaYAB2</span> at different stages. (<b>D</b>) Expression of <span class="html-italic">AcaYAB1</span> at different stages. (<b>E</b>) Expression of <span class="html-italic">AcaYAB3</span> at different stages. (<b>F</b>) Expression of <span class="html-italic">AcaIAA20</span> at different stages. (<b>G</b>) Expression of <span class="html-italic">AcaEXP1</span> at different stages. (<b>H</b>) Expression of <span class="html-italic">AcaEXP10</span> at different stages. (<b>I</b>) Expression of <span class="html-italic">AcaEXP13</span> at different stages. (<b>J</b>) Expression of <span class="html-italic">AcaXTH5</span> at different stages. (<b>K</b>) Expression of <span class="html-italic">AcaXTH6</span> at different stages. (<b>L</b>) Expression of <span class="html-italic">AcaXTH15</span> at different stages.</p>
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<p>Analysis of the expression of key genes in different tissues of <span class="html-italic">A. carambola</span> fruit. (<b>A</b>) Expression of <span class="html-italic">AcaGH3.8</span> at different tissues. (<b>B</b>) Expression of <span class="html-italic">AcaYAB8</span> at different tissues. (<b>C</b>) Expression of <span class="html-italic">AcaYAB2</span> at different tissues. (<b>D</b>) Expression of <span class="html-italic">AcaYAB1</span> at different tissues. (<b>E</b>) Expression of <span class="html-italic">AcaYAB3</span> at different tissues. (<b>F</b>) Expression of <span class="html-italic">AcaIAA20</span> at different tissues. (<b>G</b>) Expression of <span class="html-italic">AcaEXP1</span> at different tissues. (<b>H</b>) Expression of <span class="html-italic">AcaEXP10</span> at different tissues. (<b>I</b>) Expression of <span class="html-italic">AcaEXP13</span> at different tissues. (<b>J</b>) Expression of <span class="html-italic">AcaXTH5</span> at different tissues. (<b>K</b>) Expression of <span class="html-italic">AcaXTH6</span> at different tissues. (<b>L</b>) Expression of <span class="html-italic">AcaXTH15</span> at different tissues.</p>
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<p>Subcellular localization of <span class="html-italic">AcaGH3.8</span>, <span class="html-italic">AcaIAA20</span>, <span class="html-italic">AcaYAB2</span>, <span class="html-italic">AcaXTH6</span>, <span class="html-italic">AcaYAB3</span>, and <span class="html-italic">AcaEXP13</span> in <span class="html-italic">A. carambola</span>.</p>
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<p>Regulatory expression network of the pentagram of <span class="html-italic">A. carambola</span> fruit.</p>
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21 pages, 3266 KiB  
Review
Finally Freed—Cannabis in South Africa: A Review Contextualised within Global History, Diversity, and Chemical Profiles
by Valencia V. Ndlangamandla, Adeola Salawu-Rotimi, Vuyiswa S. Bushula-Njah, Nompilo L. Hlongwane, Gugu F. Sibandze, Fikisiwe C. Gebashe and Nokuthula P. Mchunu
Plants 2024, 13(19), 2695; https://doi.org/10.3390/plants13192695 - 26 Sep 2024
Abstract
Cannabis sativa L. is a monotypic genus belonging to the family Cannabaceae. It is one of the oldest species cultivated by humans, believed to have originated in Central Asia. In pivotal judgements in 2016 and 2018, the South African Constitutional Court legalised the [...] Read more.
Cannabis sativa L. is a monotypic genus belonging to the family Cannabaceae. It is one of the oldest species cultivated by humans, believed to have originated in Central Asia. In pivotal judgements in 2016 and 2018, the South African Constitutional Court legalised the use of Cannabis within the country for medicinal and recreational purposes, respectively. These decrees opened opportunities for in-depth research where previously there had been varying sentiments for research to be conducted on the plant. This review seeks to examine the history, genetic diversity, and chemical profile of Cannabis. The cultivation of Cannabis by indigenous people of southern Africa dates back to the eighteenth century. Indigenous rural communities have been supporting their livelihoods through Cannabis farming even before its legalisation. However, there are limited studies on the plant’s diversity, both morphologically and genetically, and its chemical composition. Also, there is a lack of proper documentation of Cannabis varieties in southern Africa. Currently, the National Centre for Biotechnology Information (NCBI) has 15 genome assemblies of Cannabis obtained from hemp and drug cultivars; however, none of these are representatives of African samples. More studies are needed to explore the species’ knowledge gaps on genetic diversity and chemical profiles to develop the Cannabis sector in southern Africa. Full article
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<p>The brief history and movement of <span class="html-italic">Cannabis</span> (illustration adapted from [<a href="#B4-plants-13-02695" class="html-bibr">4</a>,<a href="#B10-plants-13-02695" class="html-bibr">10</a>,<a href="#B12-plants-13-02695" class="html-bibr">12</a>]).</p>
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<p>Morphological differences between 2 (<b>A</b>) female, 2 (<b>B</b>) male plants, and 2 (<b>C</b>) hermaphrodite plants of <span class="html-italic">Cannabis</span> (illustration by V.V Ndlangamandla).</p>
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<p>The morphological variation of <span class="html-italic">C. sativa</span> subsp. <span class="html-italic">sativa</span>, <span class="html-italic">C. sativa.</span> subsp. <span class="html-italic">Indica,</span> and <span class="html-italic">C. sativa</span> subsp<span class="html-italic">. ruderalis</span> (illustration from [<a href="#B55-plants-13-02695" class="html-bibr">55</a>]).</p>
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<p>Glandular trichomes on the surface of the female flower, stem, and leaf of <span class="html-italic">Cannabis</span>, adapted from [<a href="#B77-plants-13-02695" class="html-bibr">77</a>].</p>
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<p>The biosynthesis pathway leading to the synthesis of cannabinoids, the adapted form from the previous studies [<a href="#B28-plants-13-02695" class="html-bibr">28</a>,<a href="#B67-plants-13-02695" class="html-bibr">67</a>].</p>
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18 pages, 5856 KiB  
Article
Alleviating Effects of Methyl Jasmonate on Pepper (Capsicum annuum L.) Seedlings under Low-Temperature Combined with Low-Light Stress
by Kaiguo Pu, Nenghui Li, Yanqiang Gao, Miao Zhang, Tiantian Wang, Jianming Xie and Jing Li
Plants 2024, 13(19), 2694; https://doi.org/10.3390/plants13192694 - 26 Sep 2024
Abstract
Low temperature combined with low light (LL) is an important factor limiting pepper quality and yield. ‘Hang Jiao No. 2′ were used as experimental materials, and different concentrations of MeJA (T1 (0 μM), T2 (100 μM), T3 (150 μM), T4 (200 μM), T5 [...] Read more.
Low temperature combined with low light (LL) is an important factor limiting pepper quality and yield. ‘Hang Jiao No. 2′ were used as experimental materials, and different concentrations of MeJA (T1 (0 μM), T2 (100 μM), T3 (150 μM), T4 (200 μM), T5 (250 μM) and T6 (300 μM)) were sprayed under LL stress to explore the positive effect of exogenous methyl jasmonate (MeJA) on peppers under LL stress. The photosynthetic properties, osmoregulatory substance, reactive oxygen species, antioxidant enzyme activities, and related gene expressions of the peppers were measured. Our results demonstrated that 200 μM MeJA treatment significantly increased chlorophyll content, light quantum flux per active RC electron transfer (Eto/RC), maximum captured photonic flux per active RC (TRo/RC), energy flux for electron transfer in the excitation cross section (Eto/CSm), energy flux captured by absorption in the excitation cross section (TRo/CSm), soluble protein, and soluble sugar content. Moreover, it significantly improved the maximum photochemical efficiency of PSII (Fv/Fm) and performance index based on absorbed light energy (PI (abs)) by 56.77% and 67.00%, respectively, and significantly decreased malondialdehyde (MDA) content and relative conductivity by 30.55% and 28.17%, respectively. Additionally, antioxidant enzyme activities were elevated, and the expression of the related genes was activated in pepper seedlings under stress, leading to a significant reduction in reactive oxygen species content. In conclusion, our findings confirmed that 200 μM MeJA could reduce the injury of LL to pepper leaves to the photosynthetic organs of pepper leaves, protect the integrity of the cell membrane, and further improve the tolerance of pepper seedlings to LL. Full article
(This article belongs to the Section Plant Response to Abiotic Stress and Climate Change)
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<p>Effect of different concentrations of MeJA on stem height and stem thickness of pepper seedlings under LL stress. T1, 0 μM MeJA. T2, 100 μM MeJA. T3, 150 μM MeJA. T4, 200 μM MeJA. T5, 250 μM MeJA. T6, 300 μM MeJA. The results are expressed as the mean ± SE of five replicates, and the different letters denote the significant difference among treatments (<span class="html-italic">p</span> &lt; 0.05), according to Duncan’s multiple tests. (<b>A</b>) Stem height and stem thickness. (<b>B</b>) Fresh weight. (<b>C</b>) Dry weight.</p>
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<p>Effects of different concentrations of MeJA on MDA and relative conductivity of pepper seedlings under low temperature combined with low-light stress. The results are expressed as the mean ± SE of five replicates, and the different letters denote the significant difference among treatments (<span class="html-italic">p</span> &lt; 0.05), according to Duncan’s multiple tests. (<b>A</b>) MDA content. (<b>B</b>) Relative electrical conductivity.</p>
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<p>Effect of different concentrations of MeJA on the superoxide anion content of pepper seedlings under low temperature combined with low-light stress: The results are expressed as the mean ± SE of five replicates, and the different letters denote the significant difference among treatments (<span class="html-italic">p</span> &lt; 0.05), according to Duncan’s multiple tests. (<b>A</b>) NBT histochemical staining. (<b>B</b>) O<sub>2</sub><sup>−</sup> content.</p>
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<p>Effect of different concentrations of MeJA on chlorophyll content of pepper seedlings under low temperature combined with low-light stress. The results are expressed as the mean ± SE of five replicates, and the different letters denote the significant difference among treatments (<span class="html-italic">p</span> &lt; 0.05), according to Duncan’s multiple tests. (<b>A</b>) Chlorophyll a content. (<b>B</b>) Chlorophyll b content. (<b>C</b>) Chlorophyll a+b content.</p>
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<p>Visual analysis of chlorophyll fluorescence parameters of low temperature combined with low light pepper seedlings treated with different concentrations of MeJA. Images of Fv/Fm, Y(II), qP and qN.</p>
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<p>Effects of different concentrations of MeJA on chlorophyll fluorescence kinetics (OJIP curves), JIP-test parameters of pepper seedlings, energy allocation to individual active reaction centers, and energy allocation per unit cross section in pepper seedlings under low temperature combined with low-light stress: The results are expressed as the mean ± SE of five replicates, and the different letters denote the significant difference among treatments (<span class="html-italic">p</span> &lt; 0.05), according to Duncan’s multiple tests. (<b>A</b>) OJIP curves. (<b>B</b>) JIP-test parameters. (<b>C</b>) Electron absorption, capture, transfer, and dissipation energy per unit active reaction center. (<b>D</b>) Electron absorption, capture, transfer, and dissipation energies per unit cross section.</p>
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<p>Effects of different concentrations of MeJA on soluble proteins and soluble sugars in pepper seedlings at low temperature combined with low-light stress. The results are expressed as the mean ± SE of five replicates, and the different letters denote the significant difference among treatments (<span class="html-italic">p</span> &lt; 0.05), according to Duncan’s multiple tests. (<b>A</b>) Soluble protein content. (<b>B</b>) Soluble sugar content.</p>
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<p>Effects of different concentrations of MeJA on antioxidant enzyme activities and expression of related genes in pepper seedlings under low temperature combined with low-light stress: (<b>A</b>–<b>C</b>) SOD, POD, CAT activity. (<b>D</b>–<b>F</b>) <span class="html-italic">CaSOD</span>, <span class="html-italic">CaPOD</span>, <span class="html-italic">CaCAT</span> relative expression.</p>
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24 pages, 14977 KiB  
Article
Metabolite and Transcriptomic Changes Reveal the Cold Stratification Process in Sinopodophyllum hexandrum Seeds
by Rongchun Ning, Caixia Li, Tingting Fan, Tingting Ji and Wenhua Xu
Plants 2024, 13(19), 2693; https://doi.org/10.3390/plants13192693 - 26 Sep 2024
Abstract
Sinopodophyllum hexandrum (Royle) Ying, an endangered perennial medicinal herb, exhibits morpho-physiological dormancy in its seeds, requiring cold stratification for germination. However, the precise molecular mechanisms underlying this transition from dormancy to germination remain unclear. This study integrates transcriptome and plant hormone-targeted metabolomics techniques [...] Read more.
Sinopodophyllum hexandrum (Royle) Ying, an endangered perennial medicinal herb, exhibits morpho-physiological dormancy in its seeds, requiring cold stratification for germination. However, the precise molecular mechanisms underlying this transition from dormancy to germination remain unclear. This study integrates transcriptome and plant hormone-targeted metabolomics techniques to unravel these intricate molecular regulatory mechanisms during cold stratification in S. hexandrum seeds. Significant alterations in the physicochemical properties (starch, soluble sugars, soluble proteins) and enzyme activities (PK, SDH, G-6-PDH) within the seeds occur during stratification. To characterize and monitor the formation and transformation of plant hormones throughout this process, extracts from S. hexandrum seeds at five stratification stages of 0 days (S0), 30 days (S1), 60 days (S2), 90 days (S3), and 120 days (S4) were analyzed using UPLC-MS/MS, revealing a total of 37 differential metabolites belonging to seven major classes of plant hormones. To investigate the biosynthetic and conversion processes of plant hormones related to seed dormancy and germination, the transcriptome of S. hexandrum seeds was monitored via RNA-seq, revealing 65,372 differentially expressed genes associated with plant hormone synthesis and signaling. Notably, cytokinins (CKs) and gibberellins (GAs) exhibited synergistic effects, while abscisic acid (ABA) displayed antagonistic effects. Furthermore, key hub genes were identified through integrated network analysis. In this rigorous scientific study, we systematically elucidate the intricate dynamic molecular regulatory mechanisms that govern the transition from dormancy to germination in S. hexandrum seeds during stratification. By meticulously examining these mechanisms, we establish a solid foundation of knowledge that serves as a scientific basis for facilitating large-scale breeding programs and advancing the artificial cultivation of this highly valued medicinal plant. Full article
(This article belongs to the Special Issue Metabolomics in Medicinal Plants and Agricultural Research)
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<p>Changes in seed morphological, physiological, and biochemical features of <span class="html-italic">S</span>. <span class="html-italic">hexandrum</span> at five different stratification stages: Morphology of seed embryo (<b>A</b>,<b>B</b>). Changes in embryo rate and germination rate (<b>C</b>). Contents of soluble protein, starch, and soluble sugar (<b>D</b>–<b>F</b>). Activities of pyruvate kinase (PK), glucose-6-phosphate dehydrogenase (G-6-PDH), and succinate dehydrogenase (SDH) (<b>G</b>–<b>I</b>). Values are average with their standard deviations (<span class="html-italic">n</span> = 3) with three biological replicates. Different lowercase represents a significant difference (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The metabolite analysis of <span class="html-italic">S. hexandrum</span> seeds during different stratification stages: the heatmap visualizes the total metabolites with each metabolite’s content normalized for complete linkage hierarchical clustering, where red indicates high abundance and green indicates low abundance (<b>A</b>). Bar graph analysis of total DEMs (<b>B</b>). PCA analysis of metabolites (<b>C</b>). DEMs Venn diagram (<b>D</b>). Correlation heat map between DEMs (<b>E</b>).</p>
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<p>KEGG pathway analysis of DEMs in SM1_vs_SM0 (<b>A</b>). KEGG pathway analysis of DEMs in SM2_vs_SM0 (<b>B</b>). KEGG pathway analysis of DEMs in SM3_vs_SM0 (<b>C</b>). KEGG pathway analysis of DEMs in SM4_vs_SM0 (<b>D</b>). The Rich factor refers to the ratio of the number of differentially expressed genes enriched in a particular pathway to the total number of genes annotated to that pathway. A higher Rich factor indicates a greater degree of enrichment. A smaller Q-value indicates a more significant enrichment.</p>
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<p>Analysis of total Unigenes and annotation status of Unigenes in various databases (<b>A</b>). Distribution and probability density display of stratified sample data (<b>B</b>). Assessment of biological replication correlation among samples using r. The closer the absolute value of r is to 1 (depicted in redder shades), the stronger the correlation (<b>C</b>). Legend shows the number of annotated orthologous clusters and genes, with different clusters represented by distinct colors (<b>D</b>). The horizontal axis represents the secondary GO terms, while the vertical axis represents the number of genes annotated to each GO term (<b>E</b>). The horizontal axis represents the functional categories of KOG IDs, while the vertical axis represents the number of genes within each category. The categories are distinguished by unique colors, and the legend provides the code and its functional description (<b>F</b>).</p>
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<p>Volcano plot of differentially expressed genes between ST1_vs_ST0 (<b>A</b>), ST2_vs_ST0 (<b>B</b>), ST3_vs_ST0 (<b>C</b>), and ST4_vs_ST0 (<b>D</b>); red and green dots represent the significantly upregulated and downregulated genes. Heat map of differentially expressed genes based on hierarchical clustering analysis between ST1_vs_ST0 (<b>E</b>), ST2_vs_ST0 (<b>F</b>), ST3_vs_ST0 (<b>G</b>), and ST4_vs_ST0 (<b>H</b>) as follows: darker colors represent higher expression levels of differentially expressed genes, while lighter colors indicate the opposite.</p>
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<p>DEGs enriched on different GO terms and KEGG pathways: GO terms of DEGs in ST1_vs_ST0 (<b>A</b>). GO terms of DEGs in ST2_vs_ST0 (<b>B</b>). GO terms of DEGs in ST3_vs_ST0 (<b>C</b>). GO terms of DEGs in ST4_vs_ST0 (<b>D</b>). KEGG pathway analysis of DEGs in ST1_vs_ST0 (<b>E</b>). KEGG pathway analysis of DEGs in ST2_vs_ST0 (<b>F</b>). KEGG pathway analysis of DEGs in ST3_vs_ST0 (<b>G</b>). KEGG pathway analysis of DEGs in ST4_vs_ST0 (<b>H</b>). The Rich factor refers to the ratio of the number of differentially expressed genes enriched in a particular pathway to the total number of genes annotated to that pathway. A higher Rich factor indicates a greater degree of enrichment. A smaller Q-value indicates a more significant enrichment.</p>
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<p>DEGs enriched on different GO terms and KEGG pathways: GO terms of DEGs in ST1_vs_ST0 (<b>A</b>). GO terms of DEGs in ST2_vs_ST0 (<b>B</b>). GO terms of DEGs in ST3_vs_ST0 (<b>C</b>). GO terms of DEGs in ST4_vs_ST0 (<b>D</b>). KEGG pathway analysis of DEGs in ST1_vs_ST0 (<b>E</b>). KEGG pathway analysis of DEGs in ST2_vs_ST0 (<b>F</b>). KEGG pathway analysis of DEGs in ST3_vs_ST0 (<b>G</b>). KEGG pathway analysis of DEGs in ST4_vs_ST0 (<b>H</b>). The Rich factor refers to the ratio of the number of differentially expressed genes enriched in a particular pathway to the total number of genes annotated to that pathway. A higher Rich factor indicates a greater degree of enrichment. A smaller Q-value indicates a more significant enrichment.</p>
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<p>The KEGG combined analysis of DEMs and DEGs: Combined analysis of DEGs and DEMs involved in S1_vs_S0 (<b>A</b>). Combined analysis of DEGs and DEMs involved in S2_vs_S0 (<b>B</b>). Combined analysis of DEGs and DEMs involved in S3_vs_S0 (<b>C</b>). Combined analysis of DEGs and DEMs involved in S4_vs_S0 (<b>D</b>). The horizontal coordinate represents the enrichment factor of the pathway in different histologies, and the vertical coordinate represents the name of the KEGG pathway; the gradient of red-yellow-green represents the change in the significance of enrichment from high-moderate-low, indicated by <span class="html-italic">p</span>-value; the shape of bubbles represents different omics, and the size of the bubbles represents the number of DEMs or DEGs—the larger the number, the bigger the point.</p>
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<p>Weighted gene co-expression network analysis (WGCNA) of genes during stratification stages: Clustering dendrogram of samples based on their Euclidean distance (<b>A</b>). Hierarchical cluster tree showing co-expression modules identified by WGCNA and heat map analysis of the samples with different modules (<b>B</b>). Module–metabolite association; each row corresponds to a module, and each column represents a specific hormone (<b>C</b>). The color of each cell at the row–column intersection indicates the correlation coefficient between a module and the hormones.</p>
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<p>The co-expression network analysis of DEMs and DEGs based on Pearson correlation: Interaction network of DEGs and DEMs involved in ST1_vs_ST0 (<b>A</b>). Interaction network of DEGs and DEMs in ST2_vs_ST0 (<b>B</b>). Interaction network of DEGs and DEMs involved in ST3_vs_ST0 (<b>C</b>). Interaction network of DEGs and DEMs in ST4_vs_ST0 (<b>D</b>). Edges colored in pink and blue represent DEMs and DEGs, respectively; solid line and dotted line represent positive and negative correlations, The length of the lines in the network diagram does not have any practical significance. As determined by a Pearson correlation coefficient &gt; 0.80, <span class="html-italic">p</span> &lt; 0.05.</p>
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18 pages, 2783 KiB  
Article
The Ectopic Expression of the MpDIR1(t) Gene Enhances the Response of Plants from Arabidopsis thaliana to Biotic Stress by Regulating the Defense Genes and Antioxidant Flavonoids
by Mingzheng Duan, Liuyuan Bao, Momina Eman, Duo Han, Yongzhi Zhang, Bingsong Zheng, Shunqiang Yang and Muhammad Junaid Rao
Plants 2024, 13(19), 2692; https://doi.org/10.3390/plants13192692 - 25 Sep 2024
Abstract
The Defective in Induced Resistance 1 (DIR1) gene, a member of the lipid transferase proteins (LTPs), plays a crucial role in plant defense against pathogens. While previous transcriptomic studies have highlighted the significant expression of citrus LTPs during biotic stress, functional [...] Read more.
The Defective in Induced Resistance 1 (DIR1) gene, a member of the lipid transferase proteins (LTPs), plays a crucial role in plant defense against pathogens. While previous transcriptomic studies have highlighted the significant expression of citrus LTPs during biotic stress, functional annotations of LTPs in the Citrus genera remain limited. In this study, we cloned the Murraya paniculata DIR1 (MpDIR1(t)) gene and overexpressed it in Arabidopsis thaliana to evaluate its stress response mechanisms against biotic stress. The transgenic Arabidopsis lines showed fewer disease symptoms in response to Pseudomonas syringae (Pst DC3000) compared to wild-type Arabidopsis. Defense and pathogenesis-responsive genes such as PR1, PR4, PR5, and WRKY12 were significantly induced, showing a 2- to 12-fold increase in all transgenic lines compared to the wild type. In addition, the Pst DC3000-infected transgenic Arabidopsis lines demonstrated elevated levels of flavonoids and salicylic acid (SA), along with higher expression of SA-related genes, compared to the wild type. Moreover, all transgenic lines possessed lower reactive oxygen species levels and higher activity of antioxidant defense enzymes such as superoxide dismutase, peroxidase, and catalase under Pst DC3000 stress compared to the wild type. The up-regulation of defense genes, activation of the SA pathway, accumulation of flavonoids, and reinforcement of antioxidant defense mechanisms in transgenic Arabidopsis lines in response to Pst DC3000 underscore the critical role of MpDIR1(t) in fortifying plant immunity. Thus, MpDIR1(t) constitutes a promising candidate gene for improving bacterial disease resistance in commercial citrus cultivars. Full article
(This article belongs to the Special Issue Plant Defense against Pathogens: Micro- to Molecular Insights)
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Figure 1

Figure 1
<p>Gene expression and amino acid sequence analysis of <span class="html-italic">DIR1</span> gene. (<b>A</b>) Our gene expression pattern of the <span class="html-italic">DIR1</span> gene in six different citrus species (with and without <span class="html-italic">C</span>Las inoculation); (<b>B</b>) an amino acid sequence analysis of the <span class="html-italic">DIR1</span> homologous genes cloned from <span class="html-italic">Citrus reticulata</span> (<span class="html-italic">CsDIR1</span>), <span class="html-italic">Atalantia buxifolia</span> (<span class="html-italic">AbDIR1</span>), and <span class="html-italic">Citrus maxima</span> (<span class="html-italic">CmDIR1</span>); (<b>C</b>) the <span class="html-italic">MpDIR1(t)</span> amino acid sequence compared with its homologous genes from other plants (MR: <span class="html-italic">Morella rubra</span>, AT: <span class="html-italic">Arabidopsis thaliana</span>) Five-pointed star * in (<b>B</b>,<b>C</b>) marks every 10th amino acid for sequence counting. Healthy control: without <span class="html-italic">C</span>Las; <span class="html-italic">C</span>Las-infected: 4 weeks post inoculation with <span class="html-italic">C</span>Las bacteria. Each value is the mean of three biological replicates. A Student’s <span class="html-italic">t</span>-test was used to compare the gene expression of healthy and <span class="html-italic">C</span>Las-infected citrus at ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Phylogenetic analysis of the deduced protein sequence of the <span class="html-italic">MpDIR1(t)</span> gene with its homolog genes from <span class="html-italic">Arabidopsis thaliana</span>.</p>
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<p>Inoculation of <span class="html-italic">Pst</span> DC3000 bacteria and gene expression analyses in the wild-type and transgenic lines. (<b>A</b>) Gene expression of the <span class="html-italic">Pst</span> DC3000 bacterial pathogen in <span class="html-italic">Arabidopsis</span> at different time points after inoculation, (<b>B</b>) Expression of the <span class="html-italic">MpDIR1(t)</span> gene in <span class="html-italic">Arabidopsis</span> at different time intervals after <span class="html-italic">Pst</span> DC3000 inoculation. CK: healthy control; WT-I: infected plants from the transgenic wild type (control); TG1-I: infected plants from transgenic line 1; TG3-I: infected plants from transgenic line 3; TG6-I: infected plants from transgenic line 6. Each value is the mean of three biological replicates. A Student’s <span class="html-italic">t</span>-test was used to compare transgenic <span class="html-italic">Arabidopsis</span> expressing <span class="html-italic">MpDIR1(t)</span>-TG and WT at * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Expression pattern of pathogen-responsive and SA-mediated defense genes. (<b>A</b>) Protease inhibitors 1, <span class="html-italic">AtPI1</span>; (<b>B</b>) pathogenesis-related protein 1, <span class="html-italic">AtPR1</span>; (<b>C</b>) pathogenesis-related protein 2, <span class="html-italic">AtPR2</span>; (<b>D</b>) pathogenesis-related protein 4, <span class="html-italic">AtPR4</span>; (<b>E</b>) pathogenesis-related protein 5, <span class="html-italic">AtPR5</span>; (<b>F</b>) pathogenesis-related protein 10, <span class="html-italic">AtPR10</span>; (<b>G</b>) <span class="html-italic">AtWRKY12</span>; (<b>H</b>) phenylalanine ammonia lyase <span class="html-italic">AtPAL</span>. At: <span class="html-italic">Arabidopsis thaliana</span>; CK: healthy control; WT-I: infected plants from the wild type; TG1-I: infected plants from transgenic line 1; TG3-I: infected plants from transgenic line 3; TG6-I: infected plants from transgenic line 6. Each value is the mean of three biological replicates. A Student’s <span class="html-italic">t</span>-test was used to compare transgenic <span class="html-italic">Arabidopsis</span> expressing <span class="html-italic">MpDIR1(t)</span>-TG and WT at * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Salicylic acid and antioxidant enzymatic activities of <span class="html-italic">Arabidopsis</span> leaves injected with <span class="html-italic">Pst</span> DC3000. (<b>A</b>) Salicylic acid; (<b>B</b>) superoxide dismutase, SOD; (<b>C</b>) peroxidase, POD; (<b>D</b>) catalase, CAT; (<b>E</b>) antioxidant capacity (mM Trolox/100 mg); (<b>F</b>) antioxidant activity (%). CK: healthy control; WT-I: infected plants from the wild type; TG1-I: infected plants from transgenic line 1; TG3-I: infected plants from transgenic line 3; TG6-I: infected plants from transgenic line 6. Each value is the mean of three biological replicates. A Student’s <span class="html-italic">t</span>-test was used to compare transgenic <span class="html-italic">Arabidopsis</span> expressing <span class="html-italic">MpDIR1(t)</span>-TG and WT at * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01.</p>
Full article ">Figure 5 Cont.
<p>Salicylic acid and antioxidant enzymatic activities of <span class="html-italic">Arabidopsis</span> leaves injected with <span class="html-italic">Pst</span> DC3000. (<b>A</b>) Salicylic acid; (<b>B</b>) superoxide dismutase, SOD; (<b>C</b>) peroxidase, POD; (<b>D</b>) catalase, CAT; (<b>E</b>) antioxidant capacity (mM Trolox/100 mg); (<b>F</b>) antioxidant activity (%). CK: healthy control; WT-I: infected plants from the wild type; TG1-I: infected plants from transgenic line 1; TG3-I: infected plants from transgenic line 3; TG6-I: infected plants from transgenic line 6. Each value is the mean of three biological replicates. A Student’s <span class="html-italic">t</span>-test was used to compare transgenic <span class="html-italic">Arabidopsis</span> expressing <span class="html-italic">MpDIR1(t)</span>-TG and WT at * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p><span class="html-italic">Arabidopsis</span> leaf flavonoids infected with <span class="html-italic">Pst</span> DC3000 bacteria. (<b>A</b>) Hierarchical cluster analysis (HCA), where the columns signify Arabidopsis WT and transgenic lines and the rows represent flavonoid compounds (rows were normalized). (<b>B</b>) Principal component analysis (PCA). (*) means isomers of compound; WT-I: infected plants from the wild type; TG1-I: infected plants from transgenic line 1; TG3-I: infected plants from transgenic line 3; TG6-I: infected plants from transgenic line 6.</p>
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<p>Biochemical variables of <span class="html-italic">Arabidopsis</span> leaves infected from <span class="html-italic">Pst</span> DC3000. (<b>A</b>) Superoxide radicals (SOR), (<b>B</b>) hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) contents, (<b>C</b>) reactive oxygen species (ROS), (<b>D</b>) electrolytic leakage (%), (<b>E</b>) malondialdehyde (MDA). CK: healthy control; WT-I: infected plants from the wild type; TG1-I: infected plants from transgenic line 1; TG3-I: infected plants from transgenic line 3; TG6-I: infected plants from transgenic line 6. Each value is the means of three biological replicates. A Student’s <span class="html-italic">t</span>-test was used to compare transgenic <span class="html-italic">Arabidopsis</span> expressing <span class="html-italic">MpDIR1(t)</span>-TG and WT at * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01.</p>
Full article ">Figure 7 Cont.
<p>Biochemical variables of <span class="html-italic">Arabidopsis</span> leaves infected from <span class="html-italic">Pst</span> DC3000. (<b>A</b>) Superoxide radicals (SOR), (<b>B</b>) hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) contents, (<b>C</b>) reactive oxygen species (ROS), (<b>D</b>) electrolytic leakage (%), (<b>E</b>) malondialdehyde (MDA). CK: healthy control; WT-I: infected plants from the wild type; TG1-I: infected plants from transgenic line 1; TG3-I: infected plants from transgenic line 3; TG6-I: infected plants from transgenic line 6. Each value is the means of three biological replicates. A Student’s <span class="html-italic">t</span>-test was used to compare transgenic <span class="html-italic">Arabidopsis</span> expressing <span class="html-italic">MpDIR1(t)</span>-TG and WT at * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01.</p>
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24 pages, 3840 KiB  
Article
Comparative Plastomics of Plantains (Plantago, Plantaginaceae) as a Tool for the Development of Species-Specific DNA Barcodes
by Furrukh Mehmood, Mingai Li, Alessio Bertolli, Filippo Prosser and Claudio Varotto
Plants 2024, 13(19), 2691; https://doi.org/10.3390/plants13192691 - 25 Sep 2024
Abstract
Plantago (plantains, Plantaginaceae) is a cosmopolitan genus including over 250 species used as functional foods, forage, and traditional medicine. Among them, Plantago lanceolata is commonly used as an ingredient of herbal products, but the close similarity to other Plantago species can cause misidentifications [...] Read more.
Plantago (plantains, Plantaginaceae) is a cosmopolitan genus including over 250 species used as functional foods, forage, and traditional medicine. Among them, Plantago lanceolata is commonly used as an ingredient of herbal products, but the close similarity to other Plantago species can cause misidentifications with potentially serious consequences for product safety/quality. To test the possibility of developing species-specific barcoding markers, we de novo assembled plastome sequences of individuals of Plantago argentea, Plantago atrata, P. lanceolata, and Plantago maritima. These genomes were characterized in comparison with both previously sequenced conspecific accessions and other publicly available plastomes, thus providing an assessment of both intraspecific and interspecific genetic variation in Plantago plastomes. Additionally, molecular evolutionary analyses indicated that eleven protein-coding genes involved in different plastid functions in Plantago plastomes underwent positive selection, suggesting they might have contributed to enhancing species’ adaptation during the evolutionary history of Plantago. While the most variable mutational hotspots in Plantago plastomes were not suitable for the development of species-specific molecular markers, species-specific polymorphisms could discriminate P. lanceolata from its closest relatives. Taken together, these results highlight the potential of plastome sequencing for the development of molecular markers to improve the identification of species with relevance in herbal products. Full article
(This article belongs to the Special Issue Plant Molecular Phylogenetics and Evolutionary Genomics III)
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Figure 1

Figure 1
<p>(<b>A</b>) Structural comparison of the four studied <span class="html-italic">Plantago</span> plastomes showing a high level of synteny and the lack of large rearrangements. The start and end points of the sequences are marked by green and orange blocks. The colored blocks outside the sequences refer to the score/max bit score ratio, with green ≤0.50, orange ≤0.75, and red &gt;0.75. Blue blocks and chords represent the inverted repeats (IRs). (<b>B</b>) Consensus circular genome map of four studied <span class="html-italic">Plantago</span> plastomes. Genes drawn inside the circle are transcribed counterclockwise and those outside are clockwise. Different colors indicate the genes belonging to various functional groups. GC and AT content of the genome are plotted in light grey and dark, respectively, in the inner circle. Large single copy (LSC), inverted repeat A (IRa), and inverted repeat B (IRb) highlighted with color and small single copy (SSC) are shown in the circular diagram.</p>
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<p>(<b>A</b>) Assessment of amino acid groups and (<b>B</b>) amino acid frequency comparison among <span class="html-italic">Plantago</span> species. (<b>C</b>) The codon content and RSCU value of 20 amino acids and stop codons in all protein-coding genes in the plastomes of <span class="html-italic">Plantago</span> species. The color of the histogram in (<b>C</b>) is consistent with the color of codons in the same panel. * and *** indicate the end of the protein and the stop codon, respectively.</p>
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<p>Polymorphism level and Ka/Ks ratios of different plastome regions: (<b>A</b>) Average π value for each coding and intergenic region of the 4 studied <span class="html-italic">Plantago</span> plastomes. (<b>B</b>) Ratio of Ka and Ks substitutions in 75 protein-coding genes of the plastomes of the four <span class="html-italic">Plantago</span> species.</p>
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<p>Comparative analysis of microsatellite repeats among four studied <span class="html-italic">Plantago</span> species: (<b>A</b>) Total number of microsatellites and their classification according to the number of repeat units. (<b>B</b>) The distribution of microsatellites among structural regions of the plastome. (<b>C</b>) Repeat unit composition of four studied <span class="html-italic">Plantago</span> microsatellites.</p>
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<p>(<b>A</b>) Total number of oligonucleotides repeat among the four studied <span class="html-italic">Plantago</span> species and their distribution according to specific characteristics. (<b>B</b>) The distribution of repeats in size ranges. (<b>C</b>) The number of repeats grouped according to their location in each structural region. (<b>D</b>) The distribution of repeats in intergenic spacer regions (IGS), genes, coding DNA sequences (CDS), and introns and their proportionate occurrence.</p>
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<p>Assessment of tandem repeats: (<b>A</b>) Total number of tandem repeats and (<b>B</b>) their distribution among functional regions of the plastome. (<b>C</b>) Tandem repeat number, size, and distribution.</p>
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<p>Schematic representation of junction sites in the plastomes of different <span class="html-italic">Plantago</span> species belonging to subgenera <span class="html-italic">Psyllium</span> and <span class="html-italic">Coronopus</span> (accession numbers listed in <a href="#app1-plants-13-02691" class="html-app">Table S12</a>). The junction between LSC and IR is indicated as JL, while the junction between IR and SSC is indicated as JS. Genes above and below the different plastome regions are, respectively, in forward and reverse orientation. The number of bases in each region is reported for genes at the boundaries.</p>
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<p>Maximum likelihood phylogenetic reconstruction of 45 <span class="html-italic">Plantago</span> species based on fully sequenced plastomes.</p>
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<p>Amplification patterns with plastome markers: (<b>A</b>) The amplicon obtained with primer combination PlaLan_1F + PlaLan_2F is specific for <span class="html-italic">P. lanceolata</span>. (<b>B</b>) The amplicon obtained with the Pla_CTRL_F + Pla_CTRL_R primer combination amplifies from three <span class="html-italic">Plantago</span> species.</p>
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12 pages, 3410 KiB  
Article
Genome-Wide Identification of the COMT Gene Family in Juglans regia L. and Response to Drought Stress
by Xiaolan Ma, Hongjia Luo, Jianhong Li, Zhiyue Wei, Yanlong Gao, Zhongxing Zhang and Yanxiu Wang
Plants 2024, 13(19), 2690; https://doi.org/10.3390/plants13192690 - 25 Sep 2024
Abstract
Caffeic acid O-methyltransferase (COMT), as a multifunctional enzyme involved in various physiological and biochemical processes in lignin metabolism, plays an important role in a plant’s response to stress. In this study, we isolated COMT family members from the walnut genome by [...] Read more.
Caffeic acid O-methyltransferase (COMT), as a multifunctional enzyme involved in various physiological and biochemical processes in lignin metabolism, plays an important role in a plant’s response to stress. In this study, we isolated COMT family members from the walnut genome by bioinformatics and analyzed their physicochemical properties and their expression under drought stress to provide gene resources for drought resistance in walnut. The results showed that 33 COMT genes were identified from walnuts and distributed on different chromosomes. The molecular weight of proteins varies greatly. According to the phylogenetic tree, the family can be divided into seven subgroups, which are relatively conservative in evolution and closely related to Arabidopsis thaliana. Promoter analysis showed that the promoter of the walnut COMT gene contains rich cis-elements of plant hormone response and stress response, and the real-time fluorescence scale name can be significantly induced by drought stress. Compared with wild-type Arabidopsis, overexpression JrCOMT19 significantly increased the enzyme activity (SOD, POD, and CAT) and proline content. Meanwhile, overexpression of JrCOMT19 significantly increased the lignin content and expression of related genes. Therefore, JrCOMT plays an important role in responding to drought in walnuts, and overexpression JrCOMT19 can improve the resistance to drought stress by increasing lignin content, antioxidant enzyme activity, and osmotic substance content. Full article
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Figure 1
<p>Bioinformatics analysis of <span class="html-italic">JrCOMT</span> gene family. (<b>a</b>) Domain specific to the COMT family. (<b>b</b>) Chromosome mapping of <span class="html-italic">JrCOMT</span> family genes. (<b>c</b>) Evolutionary tree analysis of <span class="html-italic">JrCOMT</span> and <span class="html-italic">AtCOMT</span> genes. The phylogenetic tree was generated using MEGA. The different colors in the figure represent the evolutionary tree groups (A–G) of <span class="html-italic">JrCOMT</span> and <span class="html-italic">AtCOMT</span>; the green dots represent the <span class="html-italic">JrCOMTs</span> gene, and the red five-pointed stars represent the <span class="html-italic">AtCOMTs</span> gene. (<b>d</b>) Cis-acting element analysis of <span class="html-italic">JrCOMT</span> family genes. Two kb 5′ upstream regions of all the identified <span class="html-italic">JrCOMT</span> genes were retrieved and analyzed through the PlantCARE database to identify the presence. The different cis-regulatory elements on each of the promoters were represented with different colors. (<b>e</b>) Intraspecific collinearity analysis of <span class="html-italic">JrCOMT</span> family genes. Sixteen chromosomes are represented in partial circles with different colors. Jrcomt genes in different chromosomes are indicated by black labels. Same colored lines connecting two chromosomal regions indicate the duplicated gene pairs in Medicago. The illustration was generated using CIRCOS-0.69-9 software.</p>
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<p>Heat map of <span class="html-italic">JrCOMT</span>s expression under drought stress. The expressive level of 9 different <span class="html-italic">JrCOMT</span>s genes at 0, 6, 12, 24, 48, and 72 h. Expression data were retrieved from genevestigator (<a href="https://genevestigator.com/gv/" target="_blank">https://genevestigator.com/gv/</a>, accessed on 5 August 2024), and the heatmap was created with hierarchical clustering of Manhattan distance correlation using MeV 4.9.0 software package. A color scale is provided along with the heat map to recognize the differential pattern of expression.</p>
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<p>Bioinformatics analysis of <span class="html-italic">JrCOMT</span>19 gene. (<b>a</b>) Secondary structure of JrCOMT. (<b>b</b>) Evolutionary tree analysis of <span class="html-italic">JrCOMT</span> and other species. Protein sequences for other species were downloaded from NCBI. The phylogenetic tree was generated using MEGA. (<b>c</b>) The tertiary structure of JrCOMT. (<b>d</b>) Cis-acting element of <span class="html-italic">JrCOMT</span>.</p>
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<p>Amplification and expression of <span class="html-italic">JrCOMT</span>19 gene in transgenic <span class="html-italic">Arabidopsis thaliana</span>. (<b>a</b>) Glue map of <span class="html-italic">JrCOMT</span>19 gene clone. (<b>b</b>) Expression levels of <span class="html-italic">JrCOMT</span> gene in WT and transgenic <span class="html-italic">Arabidopsis thaliana</span>. “*” means that the difference between samples is significant at the level of 0.05 (<span class="html-italic">p</span> &lt; 0.05). Data are means of three replicates with SE. Values not followed by the same letter indicate significant differences between treatments, according to Duncan method of single-factor ANOVA (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The phenotypes of <span class="html-italic">JrCOMT</span>19-OE and wild-type (WT) <span class="html-italic">A. thaliana</span> under normal conditions (CK) and drought stress (T). (<b>a</b>) Phenotypes of wild and transgenic <span class="html-italic">Arabidopsis thaliana</span> under drought stress. H, S, and G represent three different types of lignin. (<b>b</b>) Lignin content. (<b>c</b>) Relative water content. (<b>d</b>) Relative expression level of genes resulting in lignin. “ns” means that the difference between samples is not significant, and “*” means that the difference between samples is significant at the level of 0.05 (<span class="html-italic">p</span> &lt; 0.05). Data are means of three replicates with SE. Values not followed by the same letter indicate significant differences between treatments, according to Duncan method of single-factor ANOVA (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Physiological indices of <span class="html-italic">JrCOMT</span>19 and wild-type (WT) <span class="html-italic">A. thaliana</span> under normal conditions (CK) and drought stress (T). (<b>a</b>) The Chl content. (<b>b</b>) The CAT activities. (<b>c</b>) The Pro content. (<b>d</b>) The POD activities. (<b>e</b>) The SOD activities. (<b>f</b>) The REC conductivity. “ns” means that the difference between samples is not significant, and “*” means that the difference between samples is significant at the level of 0.05 (<span class="html-italic">p</span> &lt; 0.05). Data are means of three replicates with SE. Values not followed by the same letter indicate significant differences between treatments, according to Duncan method of single-factor ANOVA (<span class="html-italic">p</span> &lt; 0.05).</p>
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12 pages, 2892 KiB  
Article
Enhancing Red Table Grape Coloration Using Tsikoudia: A Novel and Sustainable Approach
by Emmanouil Kontaxakis, Dimitrios Lydakis and Ioannis Fisarakis
Plants 2024, 13(19), 2689; https://doi.org/10.3390/plants13192689 - 25 Sep 2024
Abstract
Achieving optimal coloration in red table grapes, especially in warm-climate regions, presents significant challenges due to high temperatures that inhibit anthocyanin biosynthesis. Conventional methods to enhance grape coloration, including the use of abscisic acid (ABA), ethephon, foliar nutrient supplementation, and viticultural practices like [...] Read more.
Achieving optimal coloration in red table grapes, especially in warm-climate regions, presents significant challenges due to high temperatures that inhibit anthocyanin biosynthesis. Conventional methods to enhance grape coloration, including the use of abscisic acid (ABA), ethephon, foliar nutrient supplementation, and viticultural practices like cluster trimming and girdling, have limitations related to cost, regulatory restrictions, and potential adverse effects on grapes quality. This study proposes the application of tsikoudia, a traditional Greek alcoholic beverage, as a novel, sustainable, and cost-effective alternative to conventional practices. Tsikoudia, applied during the veraison stage, significantly improved the coloration of ‘Crimson Seedless’ and ‘Red Globe’ grapes by enhancing anthocyanin accumulation and altering color parameters. Specifically, lightness (L*), chroma (C*), and hue angle (h), measured using the CIE-Lab color system, were reduced, while the Color Index for Red Grapes (CIRG) was increased. Additionally, total anthocyanin content, determined through spectrophotometric analysis, also showed an increase. These changes indicate a more intense red coloration. This research highlights the effectiveness of tsikoudia in improving grape coloration and contributes to the development of more sustainable viticultural practices. Full article
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Figure 1
<p>Effect of treatments on the coloration of ‘Crimson Seedless’ grapes, expressed as CIE-Lab color parameters lightness (<span class="html-italic">L</span>*), chroma (<span class="html-italic">C</span>*), hue angle (<span class="html-italic">h</span>), and Color Index for Red Grapes (CIRG). The different letters for each year (in italics for the 2023) indicate significant differences according to Duncan’s multiple range test (<span class="html-italic">p</span> ≤ 0.05) among the treatments. Error bars represent the standard errors of the means.</p>
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<p>Effect of treatments on the coloration of ‘Red Globe’ grapes, expressed as CIE-Lab color parameters lightness (<span class="html-italic">L</span>*), chroma (<span class="html-italic">C</span>*), hue angle (<span class="html-italic">h</span>), and Color Index for Red Grapes (CIRG). The different letters for each year (in italics for the 2023) indicate significant differences according to Duncan’s multiple range test (<span class="html-italic">p</span> ≤ 0.05) among the treatments. Error bars represent the standard errors of the means.</p>
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<p>Effect of treatments on the total anthocyanin content in ‘Crimson Seedless’ and ‘Red Globe’ grape skins. The different letters for each year (in italics for the 2023) indicate significant differences according to Duncan’s multiple range test (<span class="html-italic">p</span> ≤ 0.05) among the treatments. Error bars represent the standard errors of the means.</p>
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17 pages, 6508 KiB  
Article
RNA-Seq Analysis and Candidate Gene Mining of Gossypium hirsutum Stressed by Verticillium dahliae Cultured at Different Temperatures
by Ni Yang, Zhaolong Gong, Yajun Liang, Shiwei Geng, Fenglei Sun, Xueyuan Li, Shuaishuai Qian, Chengxia Lai, Mayila Yusuyin, Junduo Wang and Juyun Zheng
Plants 2024, 13(19), 2688; https://doi.org/10.3390/plants13192688 - 25 Sep 2024
Abstract
The occurrence and spread of Verticillium dahliae (V. dahliae) in cotton depends on the combined effects of pathogens, host plants, and the environment, among which temperature is one of the most important environmental factors. Studying how temperature impacts the occurrence of [...] Read more.
The occurrence and spread of Verticillium dahliae (V. dahliae) in cotton depends on the combined effects of pathogens, host plants, and the environment, among which temperature is one of the most important environmental factors. Studying how temperature impacts the occurrence of V. dahliae in cotton and the mechanisms governing host defense responses is crucial for disease prevention and control. Understanding the dual effects of temperature on both pathogens and hosts can provide valuable insights for developing effective strategies to manage this destructive fungal infection in cotton. This study was based on the deciduous V. dahliae Vd-3. Through cultivation at different temperatures, Vd-3 formed the most microsclerotia and had the largest colony diameter at 25 °C. Endospore toxins were extracted, and 48 h was determined to be the best pathogenic time point for endotoxins to infect cotton leaves through a chlorophyll fluorescence imaging system and phenotypic evaluation. Transcriptome sequencing was performed on cotton leaves infected with Vd-3 endotoxins for 48 h at different culture temperatures. A total of 34,955 differentially expressed genes (DEGs) were identified between each temperature and CK (no pathogen inoculation), including 17,422 common DEGs. The results of the enrichment analysis revealed that all the DEGs were involved mainly in photosynthesis and sugar metabolism. Among the 34,955 DEGs, genes in the biosynthesis and signal transduction pathways of jasmonic acid (JA), salicylic acid (SA), and ethylene (ET) were identified, and their expression patterns were determined. A total of 5652 unique DEGs were clustered into six clusters using the k-means clustering algorithm, and the functions and main transcription factors (TFs) of each cluster were subsequently annotated. In addition, we constructed a gene regulatory network via weighted correlation network analysis (WGCNA) and identified twelve key genes related to cotton defense against V. dahliae at different temperatures, including four genes encoding transcription factors. These findings provide a theoretical foundation for investigating temperature regulation in V. dahliae infecting cotton and introduce novel genetic resources for enhancing resistance to this disease in cotton plants. Full article
(This article belongs to the Section Plant Genetics, Genomics and Biotechnology)
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<p>(<b>a</b>) Phenotype and colony diameter of Vd-3 colonies after 14 days of culture at different temperatures, bar = 1 cm. Different letters indicate the significance level of difference in colony diameter at different temperatures (<span class="html-italic">p</span> &lt; 0.05). (<b>b</b>) Phenotype and chlorophyll fluorescence imaging of cotton leaves infected with the spore toxin protein at different times under normal conditions; bar = 1 cm. (<b>c</b>) Phenotype and chlorophyll fluorescence imaging of cotton leaves infected with spore toxin protein at different temperatures for 48 h; bar = 1 cm.</p>
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<p>(<b>a</b>) Correlation analysis of RNA-seq data from cotton leaves infected with the spore toxin protein at different temperatures. (<b>b</b>) PCA of RNA-seq data from cotton leaves infected with the spore toxin protein at different temperatures.</p>
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<p>(<b>a</b>) Numbers of upregulated and downregulated DEGs at different temperatures. (<b>b</b>) Numbers of common and unique DEGs at different temperatures. (<b>c</b>) GO enrichment analysis of DEGs. (<b>d</b>) KEGG enrichment analysis of DEGs.</p>
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<p>Line graph of the cluster analysis of DEGs. The green numbers represent the numbers of DEGs and TFs in each cluster.</p>
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<p>(<b>a</b>) Line graph of the cluster analysis of unique DEGs. (<b>b</b>) Heatmap of TF proportions in each cluster. (<b>c</b>) Heatmap of the KEGG enrichment analysis results for each cluster.</p>
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<p>(<b>a</b>) Calorimetry of JA biosynthesis and signaling-related DEGs. (<b>b</b>) Calorimetry of SA biosynthesis and signaling-related DEGs. (<b>c</b>) Calorimetry of ET biosynthesis and signaling-related DEGs.</p>
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<p>(<b>a</b>) WGCNA module hierarchical clustering tree diagram; different modules are represented by different colors. (<b>b</b>) Correlation and significance heatmaps between samples and modules. (<b>c</b>) Red module gene interaction network diagram. (<b>d</b>) Turquoise module gene interaction network diagram. (<b>e</b>) Yellow module gene interaction network diagram. (<b>f</b>) Black module gene interaction network diagram.</p>
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<p>Analysis of the expression patterns of candidate genes under different temperature conditions (error bars represent the means ± SEs of three replicates, * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01).</p>
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9 pages, 1844 KiB  
Article
An Improved Sampling and Baiting Method for Phytophthora tropicalis and P. heveae Detection in Macadamia integrifolia
by Christopher M. Ference and Lisa M. Keith
Plants 2024, 13(19), 2687; https://doi.org/10.3390/plants13192687 - 25 Sep 2024
Abstract
Macadamia nuts are, economically, the second most important crop in the state of Hawai’i. A recent decline in yield and acreage has been attributed to insect damage and diseases such as Macadamia Quick Decline (MQD) caused by Phytophthora tropicalis and P. heveae. [...] Read more.
Macadamia nuts are, economically, the second most important crop in the state of Hawai’i. A recent decline in yield and acreage has been attributed to insect damage and diseases such as Macadamia Quick Decline (MQD) caused by Phytophthora tropicalis and P. heveae. To develop an improved methodology for the diagnosis and treatment of MQD, investigations were undertaken to better understand the pathosystem of the disease. These investigations included sampling from multiple locations from sectioned trees utilizing two methods of tissue collection and isolations using two baiting techniques. The collection of tissue from the cambium and phloem of trees after scraping away the bark and in locations of recent or current sap exudation using a narrow diameter steel awl proved to be an efficient means for the molecular detection of the MQD pathogens from infected trees exhibiting MQD symptoms. In addition, a more efficient and cost-effective baiting method using apple puree was developed. Full article
(This article belongs to the Special Issue Novel Methods for Detection and Control Strategies of Phytopathogens)
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<p>(<b>A</b>) MQD-symptomatic ‘bleeding’ = dried exuded sap on main trunk; (<b>B</b>) drill sampling from wood; up-pointing arrows indicate drill holes from healthy looking sapwood, right-pointing arrows indicate drill holes from boundary sapwood, and down-pointing arrows indicate drill holes from MQD-symptomatic dried stained sapwood; (<b>C</b>) shallow and deep sampling; down-pointing arrows indicate drill holes from vascular cambium and phloem, and left-pointing arrows indicate drill holes from stained sapwood; (<b>D</b>) unhealed branch wound with insect bore holes.</p>
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<p>Locations on the island of Hawai’i of sampled trees exhibiting MQD symptoms. Blue asterisks = <span class="html-italic">P. tropicalis</span>. Red asterisks = <span class="html-italic">P. heveae</span>. Yellow asterisks = both species of <span class="html-italic">Phytophthora</span>.</p>
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<p>Comparing zoospore concentrations of <span class="html-italic">Phytophthora heveae</span> with their cycle threshold (Ct) following qPCR. The formula for the best-fit slope is y = (−1.28 × ln(x)) + 35.049 which is represented by the dotted black line, with an R<sup>2</sup> confidence value of 0.9946, where 1 = perfect confidence.</p>
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14 pages, 1186 KiB  
Review
The Role of Phosphate-Solubilizing Microbial Interactions in Phosphorus Activation and Utilization in Plant–Soil Systems: A Review
by Ying Zhu, Yijing Xing, Yue Li, Jingyi Jia, Yeqing Ying and Wenhui Shi
Plants 2024, 13(19), 2686; https://doi.org/10.3390/plants13192686 - 25 Sep 2024
Abstract
To address the issue of phosphorus limitation in agricultural and forestry production and to identify green and economical alternatives to chemical phosphorus fertilizers, this paper reviews the utilization of phosphorus in plant–soil systems and explores the considerable potential for exploiting endogenous phosphorus resources. [...] Read more.
To address the issue of phosphorus limitation in agricultural and forestry production and to identify green and economical alternatives to chemical phosphorus fertilizers, this paper reviews the utilization of phosphorus in plant–soil systems and explores the considerable potential for exploiting endogenous phosphorus resources. The application of phosphate-solubilizing microorganisms (PSMs) is emphasized for their role in phosphorus activation and plant growth promotion. A focus is placed on microbial interactions as an entry point to regulate the functional rhizosphere microbiome, introducing the concept of synthetic communities. This approach aims to deepen the understanding of PSM interactions across plant root, soil, and microbial interfaces, providing a theoretical foundation for the development and application of biological regulation technologies to enhance phosphorus utilization efficiency. Full article
(This article belongs to the Special Issue Nutrient Management on Soil Microbiome Dynamics and Plant Health)
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<p>Phosphorus limitation in production and common solutions.</p>
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<p>Potential for the development and utilization of phosphate-solubilizing microorganisms. Phosphate-solubilizing microorganisms (PSMs) within the rhizosphere microbiome have the potential to enhance soil phosphorus availability. These microorganisms can secrete phytases to mineralize organic phosphorus (Po) and produce organic acids, hydrogen ions, and extracellular polysaccharides to solubilize insoluble inorganic phosphorus compounds (such as Ca-P, Al-P, Fe-P, and O-P). This activity increases the concentration of available phosphorus in the soil. Additionally, PSMs can promote plant growth by secreting substances such as indole-3-acetic acid (IAA), 1-aminocyclopropane-1-carboxylate (ACC) deaminase, siderophores, and antibiotics.</p>
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17 pages, 3076 KiB  
Article
Transcriptome Profiling Identifies Plant Hormone Signaling Pathway-Related Genes and Transcription Factors in the Drought and Re-Watering Response of Ginkgo biloba
by Meiling Ming, Juan Zhang, Jiamin Zhang, Jing Tang, Fangfang Fu and Fuliang Cao
Plants 2024, 13(19), 2685; https://doi.org/10.3390/plants13192685 - 25 Sep 2024
Abstract
Ginkgo biloba, usually referred to as a “living fossil,” is widely planted in many countries because of its medicinal value and beautiful appearance. Owing to ginkgo’s high resistance to drought stress, ginkgo seedlings can even survive withholding water for several days without [...] Read more.
Ginkgo biloba, usually referred to as a “living fossil,” is widely planted in many countries because of its medicinal value and beautiful appearance. Owing to ginkgo’s high resistance to drought stress, ginkgo seedlings can even survive withholding water for several days without exhibiting leaf wilting and desiccation. To assess the physiological and transcriptomic mechanisms involved in the drought stress and re-watering responses of Ginkgo biloba, ginkgo seedlings were subjected to drought treatment for 15 d (D_15 d) and 22 d (D_22 d) until they had severely wilted, followed by re-watering for 3 d (D_Re3 d) to restore normal growth. Variations in physiological characteristics (relative water content, malondialdehyde (MDA) content, stomatal aperture, and antioxidant enzyme activity) during drought and re-watering were assessed. In total, 1692, 2031, and 1038 differentially expressed genes (DEGs) were upregulated, while 1691, 2820, and 1910 were downregulated in D_15 d, D_22 d, and D_Re3 d, respectively, relative to the control. Three pathways, namely, plant hormone signal transduction, plant–pathogen interaction, and the plant MAPK signaling pathway, were enriched during drought stress and re-watering. The DEGs involved in plant hormone signal transduction pathways (those of IAA, CTK, GA, ABA, ETH, BR, SA, and JA) and the major differentially expressed transcription factors (TFs; MYB, bHLH, AP2/ERF, NAC, WRKY, and bZIP) were identified. Quantitative real-time PCR revealed six TFs as positive or negative regulators of drought stress response. These phenotype-related physiological characteristics, DEGs, pathways, and TFs provide valuable insights into the drought stress and re-watering responses in G. biloba. Full article
(This article belongs to the Section Plant Response to Abiotic Stress and Climate Change)
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<p>Phenotypes, physiological, and biochemical characteristics of <span class="html-italic">G. biloba</span> under drought and re-watering treatments. (<b>A</b>) Phenotypes of <span class="html-italic">G. biloba</span> drought for 0 d, 15 d, 22 d and re-watering 3 d, compared with the control group (CK) with regular irrigation. (<b>B</b>) Relative water content. (<b>C</b>) Malondialdehyde (MDA) content. (<b>D</b>) Stomatal aperture. (<b>E</b>) Catalase (CAT) activity. (<b>F</b>) Superoxide dismutase (SOD) activity. (<b>G</b>) Peroxidase (POD) activity. All data are presented as the mean ± SDs (n ≥ 3 biological replicates). Lowercase letters above bars indicate significant differences as determined by one-way ANOVA test followed by Tukey’s multiple comparisons test (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Identification and analysis of differentially expressed genes (DEGs) in <span class="html-italic">G. biloba</span> leaves during drought and re-watering treatments. (<b>A</b>) Principal component analysis of 21 samples based on transcripts per kilobase of exon model per million mapped reads (TPM). (<b>B</b>) Hierarchical clustering analysis of 21 samples based on Manhattan distance. (<b>C</b>) Number of DEGs in the drought and re-watering treatments. (<b>D</b>,<b>E</b>) Venn diagram of DEGs in the drought and re-watering treatments. (<b>F</b>–<b>H</b>) Volcano plots of DEGs at D_15 d vs. CK_15 d, D_22 d vs. CK_22 d, and D_Re3 d vs. CK_Re3 d. Red and blue dots indicate significantly upregulated and downregulated genes, respectively. The labeled genes indicate the most significantly differentially expressed genes.</p>
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<p>Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs in the leaves of <span class="html-italic">G. biloba</span> under drought and re-watering treatments. (<b>A</b>) The top 10 GO enrichment items for each group. (<b>B</b>) The top 10 KEGG enrichment items for each group. D: drought; CK: control with regular irrigation.</p>
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<p>DEGs involved in plant hormone signal transduction during drought and re-watering treatments in <span class="html-italic">G. biloba</span>. (<b>A</b>) Schematic representation of the plant hormone signal transduction pathway. DEGs encoding key enzymes are shaded, and their expression is presented in the heatmap. The small, yellow five-pointed star indicates genes belonging to TFs. (<b>B</b>–<b>H</b>) Heatmap of DEGs encoding the auxin (IAA), cytokinin (CTK), gibberellin (GA), abscisic acid (ABA), ethylene (ETH), salicylic acid (SA), and brassinosteroid (BR) signal transduction pathways.</p>
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<p>The major differentially expressed transcription factor families involved in the drought and re-watering treatments in <span class="html-italic">G. biloba</span>. (<b>A</b>) MYB. (<b>B</b>) AP2/ERF. (<b>C</b>) bHLH. (<b>D</b>) bZIP. (<b>E</b>) WRKY. (<b>F</b>) NAC. Genes labeled with red and blue rectangles indicate upregulation and downregulation, respectively.</p>
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<p>The relative expression levels of key differentially expressed transcription factors based on qRT-PCR analysis. (<b>A</b>) Upregulated MYB. (<b>B</b>) Downregulated MYB. (<b>C</b>) Downregulated WRKY. (<b>D</b>) Downregulated bZIP. (<b>E</b>) Downregulated NAC. (<b>F</b>) Downregulated AP2/ERF.</p>
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22 pages, 11500 KiB  
Article
Overexpression of Auxin/Indole-3-Acetic Acid Gene TrIAA27 Enhances Biomass, Drought, and Salt Tolerance in Arabidopsis thaliana
by Muhammad Zafar Iqbal, Yuzhou Liang, Muhammad Anwar, Akash Fatima, Muhammad Jawad Hassan, Asif Ali, Qilin Tang and Yan Peng
Plants 2024, 13(19), 2684; https://doi.org/10.3390/plants13192684 - 25 Sep 2024
Abstract
White clover (Trifolium repens L.) is an important forage and aesthetic plant species, but it is susceptible to drought and heat stress. The phytohormone auxin regulates several aspects of plant development and alleviates the effects of drought stress in plants, including white [...] Read more.
White clover (Trifolium repens L.) is an important forage and aesthetic plant species, but it is susceptible to drought and heat stress. The phytohormone auxin regulates several aspects of plant development and alleviates the effects of drought stress in plants, including white clover, by involving auxin/indole acetic acid (Aux/IAA) family genes. However, Aux/IAA genes and the underlying mechanism of auxin-mediated drought response remain elusive in white clover. To extend our understanding of the multiple functions of Aux/IAAs, the current study described the characterization of a member of the Aux/IAA family TrIAA27 of white clover. TrIAA27 protein had conserved the Aux/IAA family domain and shared high sequence similarity with the IAA27 gene of a closely related species and Arabidopsis. Expression of TrIAA27 was upregulated in response to heavy metal, drought, salt, NO, Ca2+, H2O2, Spm, ABA, and IAA treatments, while downregulated under cold stress in the roots and leaves of white clover. TrIAA27 protein was localized in the nucleus. Constitutive overexpression of TrIAA27 in Arabidopsis thaliana led to enhanced hypocotyl length, root length, plant height, leaf length and width, and fresh and dry weights under optimal and stress conditions. There was Improved photosynthesis activity, chlorophyll content, survival rate, relative water content, endogenous catalase (CAT), and peroxidase (POD) concentration with a significantly lower electrolyte leakage percentage, malondialdehyde (MDA) content, and hydrogen peroxide (H2O2) concentration in overexpression lines compared to wild-type Arabidopsis under drought and salt stress conditions. Exposure to stress conditions resulted in relatively weaker roots and above-ground plant growth inhibition, enhanced endogenous levels of major antioxidant enzymes, which correlated well with lower lipid peroxidation, lower levels of reactive oxygen species, and reduced cell death in overexpression lines. The data of the current study demonstrated that TrIAA27 is involved in positively regulating plant growth and development and could be considered a potential target gene for further use, including the breeding of white clover for higher biomass with improved root architecture and tolerance to abiotic stress. Full article
(This article belongs to the Special Issue Abiotic Stress Responses in Plants)
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<p>The gene sequence characteristics and phylogenetic analysis of the <span class="html-italic">Tr-IAA27</span> gene of white clover. (<b>a</b>) Amplified band of <span class="html-italic">Tr-IAA27</span> from white clover cDNA; (<b>b</b>) homology of nucleotides bases of <span class="html-italic">Tr-IAA27</span> with closely related species; (<b>c</b>) comparison of amino acid sequences of <span class="html-italic">Tr-IAA27</span> with other related <span class="html-italic">IAA27</span> proteins, highly homolog amino acid residues are shaded; (<b>d</b>) phylogenetic relationship of <span class="html-italic">Tr-IAA27</span> proteins with same protein from closely related species and <span class="html-italic">Arabidopsis thaliana</span>; triangles in branches represent bootstrap values, and the phylogenetic tree analysis was based on minimum evolution using Mega (version 11).</p>
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<p>Relative expression of <span class="html-italic">Tr-IAA27</span> in white clover in response to different external stimuli. (<b>a</b>–<b>k</b>) Show relative expression of <span class="html-italic">TrIAA27</span> gene in response to treatments of heavy metal (600 mM CdSO<sub>4</sub>), cold (4 °C), heat (35 °C), drought (15% PEG6000 <span class="html-italic">w</span>/<span class="html-italic">v</span>), salt (200 mM NaCl), NO (25 μM), Ca<sup>2+</sup> (5 mM), H<sub>2</sub>O<sub>2</sub> (10 mM), Spm (20 mM), ABA (100 μM), and IAA (1 mM), respectively. (<b>l</b>) Expression of <span class="html-italic">TrIAA27</span> in wild-type <span class="html-italic">Arabidopsis</span> and 5 overexpression lines (OE1-5) of <span class="html-italic">Arabidopsis</span>. Different alphabet letters over bars show statistically significant differences (ANOVA, <span class="html-italic">p</span> &lt; 0.05), and the error bar shows standard error (SE).</p>
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<p>Subcellular localization of <span class="html-italic">TrIAA27</span> in tobacco leaves. The TrIAA27::GFP vector and an empty vector were transferred into tobacco leaves for transient expression and observed under the fluorescent microscope. The green color shows signals of the <span class="html-italic">TrIAA27</span> protein.</p>
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<p>Phenotypic comparison of growth performance indicators between wild-type (WT) and <span class="html-italic">TrIAA27</span> overexpression <span class="html-italic">Arabidopsis</span> lines (OE3 and OE5). (<b>a</b>–<b>c</b>) Display hook angles of WT, OE3, and OE5, respectively. (<b>d</b>,<b>i</b>) Show a comparison of hypocotyl length between WT, OE3, and OE5. (<b>e</b>,<b>j</b>,<b>k</b>) Roots growth comparison of WT, OE3, and OE5 grown on ½ MS medium supplemented with 3% sucrose and 0.7% agar. (<b>f</b>,<b>m</b>,<b>n</b>) Leaf length and width differences among WT, OE3, and OE5. (<b>g</b>,<b>h</b>,<b>i</b>,<b>l</b>,<b>o</b>,<b>p</b>) Growth performance, hypocotyl length, plant height, fresh weight, and dry weight of WT, OE3, and OE5, respectively. Different alphabet letters over bars show statistically significant differences (ANOVA, <span class="html-italic">p</span> &lt; 0.05) among genotypes, and the error bar shows standard error (SE). WT, OE3, and OE5 represent wild types, overexpression lines 3 and 5, respectively.</p>
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<p>Growth performance of wild-type (WT) and <span class="html-italic">TrIAA27</span> overexpression lines (OE3 and OE5) under drought stress conditions. (<b>a</b>) Phenotypical comparison of WT, OE3, and OE5 grown on ½ MS media containing 3% sucrose and 0.7% agar supplemented with 0 mM mannitol, 100 mM mannitol, or 200 mM mannitol on 5th and 10th d. (<b>b</b>) Phenotypes of WT, OE3, and OE5 grown in soil after irrigation withholding at 15 d. (<b>c</b>–<b>j</b>) Display statistical comparisons of secondary roots no., roots length, fresh weights, dry weights, photochemical efficiency (Fm/Fv ratio), performance index on an absorption basis (PI), relative water content %, and electrolyte leakage %, respectively. Different alphabet letters over bars show statistically significant differences (ANOVA, <span class="html-italic">p</span> &lt; 0.05) among genotypes; N.S represents statistically non-significant differences. The error bar shows standard error (SE). WT, OE3, and OE5 represent wild types, overexpression line 3 and overexpression line 5, respectively.</p>
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<p>Growth performance comparison of wild-type (WT) and <span class="html-italic">TrIAA27</span> overexpression (OE) <span class="html-italic">Arabidopsis</span> lines under salt stress conditions. (<b>a</b>) Performance of WT, OE3, and OE5 on ½ MS media supplemented with 150 mM NaCl at day 10. (<b>b</b>) Performance of WT, OE3, and OE5 in soil irrigated with saline water. (<b>c</b>–<b>i</b>) Statistical comparisons of photochemical efficiency Fm/Fv, performance index on an absorption basis, relative water content %, electrolyte leakage %, chlorophyll contents, survival rates, and root length, respectively. Different alphabet letters over bars show statistically significant differences (ANOVA, <span class="html-italic">p</span> &lt; 0.05) among genotypes, and the error bar shows standard error (SE). WT, OE3, and OE5 represent wild types, overexpression line 3, and overexpression line 5, respectively.</p>
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<p>The comparative analysis of endogenous oxidants and antioxidant levels in the wild-type and overexpression <span class="html-italic">TrIAA27 Arabidopsis</span> under drought and salt stress conditions. (<b>a</b>) Relative malondialdehyde (MDA) contents, (<b>b</b>) relative hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) concentration, (<b>c</b>) endogenous catalase (CAT), and (<b>d</b>) the peroxidase (POD) concentration in the leaves of wild-type and <span class="html-italic">TrIAA27</span>-overexpressing <span class="html-italic">Arabidopsis</span>. Different alphabet letters over bars show statistically significant differences (ANOVA, <span class="html-italic">p</span> &lt; 0.05) among genotypes, and the error bar shows standard error (SE). WT, OE3, and OE5 represent wild types, overexpression line 3, and overexpression line 5, respectively.</p>
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15 pages, 4124 KiB  
Article
Effect of Environmental and Anthropic Conditions on the Development of Solanum peruvianum: A Case of the Coastal Lomas, Lima-Peru
by Vladimir Camel, July Quispe-Huañahue, Edwin Felix, Zulema Ninanya-Parra, Yngrid Mendoza, Sebastian Peralta-Yalta, Freddy Pillpa and Rita Cabello-Torres
Plants 2024, 13(19), 2683; https://doi.org/10.3390/plants13192683 - 25 Sep 2024
Abstract
Land degradation and the effects of climate change are increasing arid lands, accelerating desertification, and leading to the loss of ecosystem services worldwide. This research focused on evaluating how human impact and environmental factors affect the development of Solanum peruvianum in its natural [...] Read more.
Land degradation and the effects of climate change are increasing arid lands, accelerating desertification, and leading to the loss of ecosystem services worldwide. This research focused on evaluating how human impact and environmental factors affect the development of Solanum peruvianum in its natural habitat of coastal lomas. The study was carried out in the coastal lomas of Mangomarca-Peru, where phenotypic and ecological data on the plants were collected. Information was also gathered on human impacts on the nutritional characteristics of the soils. Then, five types of organic amendments were used to improve the physical and chemical characteristics of the degraded soil, and the development and photosynthetic activity of S. peruvianum were evaluated. As a result, under the study conditions, it was found that S. peruvianum was established approximately 33.74 cm from the rocks, in a range of 300 to 650 m asl. The maximum height of the plants was 90 cm, with a stem diameter at ground level of 2 cm. S. peruvianum produced fruits between January and July, with a seed germination rate of 36% in 25 days. On the other hand, the anthropogenic impact on the soil reduced 58% of organic material (OM), 71% of nitrogen, 40% of P2O5, and 13% of K2O and increased the concentration of magnesium oxide, calcium oxide, pH, and electric conductivity (EC). The organic amendments bokashi, compost, and biochar, when mixed with the degraded soil, increased the pH, OM, N, P, and EC; however, the plants died after 25 days. On the other hand, the application of the Premix5 substrate for 100 days favored the growth of 52.84 cm and 38.29 cm in the preserved soil and 23.21 cm in the black soil mixed with blond peat, and it should be noted that the substrates presented an acid pH and EC > 0.1. Regarding photosynthetic phenotyping, S. peruvianum plants grown in their natural habitat and in Premix5 showed a higher proton flux (vH+), linear electron flow (LEF), and maximum quantum yield (Fv’/Fm’). On the contrary, they showed a lower NPQt value than plants grown in preserved and black soil mixed with blond peat. Full article
(This article belongs to the Section Plant Response to Abiotic Stress and Climate Change)
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<p>Average coefficients of the model that influence the parameters of the structure of <span class="html-italic">Solanum peruvianum</span>: (<b>a</b>) total height, (<b>b</b>) diameter ground level, (<b>c</b>) number of stems, and (<b>d</b>) natural regeneration. Error bars represent 95% confidence intervals. Black boxes with asterisks indicate significant effects on structure parameters (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Comparisons of soil characteristics in preserved and degraded coastal Lomas: (<b>a</b>) organic material (%), (<b>b</b>) nitrogen (%), (<b>c</b>) diphosphorus oxide (P<sub>2</sub>O<sub>5</sub>), (<b>d</b>) potassium oxide (K<sub>2</sub>O %), (<b>e</b>) magnesium oxide (MgO %), (<b>f</b>) calcium oxide, known as quicklime (CaO %), (<b>g</b>) pH, and (<b>h</b>) electric conductivity (C.E. dS m<sup>−1</sup>). Different letters are significantly different at <span class="html-italic">p</span> ≤ 0.05, as per Tukey’s test after GLMM. Error bars represent the 95% confidence intervals.</p>
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<p>Morphological evaluation of <span class="html-italic">Solanum peruvianum</span>. (<b>a</b>) Correlation between diameter at ground level and total height; (<b>b</b>) Relationship between fruit diameter and number of seeds. (<b>c</b>) Correlation between fruit weight and number of seeds. The grey band indicates the 95% confidence limit.</p>
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<p>Statistical differences in the growth of <span class="html-italic">S. peruvianum</span> under different organic substrates.</p>
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<p>Photosynthetic performance of <span class="html-italic">S. peruvianum</span>: (<b>a</b>) PSII; (<b>b</b>) relative chlorophyll; (<b>c</b>) total non-photochemical quenching corrected for Fm (NPQt); (<b>d</b>) proton conductivity of the chloroplast ATP synthase (vH+); (<b>e</b>) leaf thickness; (<b>f</b>) maximum quantum efficiency (Fv’/Fm’); (<b>g</b>) fraction of photosystem II open center (qL); (<b>h</b>) linear electron flow (LEF). Different letters significantly differ at <span class="html-italic">p</span> ≤ 0.05 with Tukey’s test after GLMM. The grey band indicates photosynthetic measurements in the natural habitat of <span class="html-italic">S. peruvianum</span>, in the Mangomarca Lomas.</p>
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<p><span class="html-italic">S. peruvianum</span> plants and location of coastal lomas ecosystems in Peru. (<b>a</b>) Location of Peru in South America. (<b>b</b>) On the map of Peru, green dots indicate the position of other coastal lomas, and red dot indicates the position of the Mangomarca Lomas. (<b>c</b>) Map of the coastal lomas of Mangomarca. The polygon indicates the region where the <span class="html-italic">S. peruvianum</span> plants are distributed.</p>
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17 pages, 2373 KiB  
Article
Comparative Analysis of Volatile Compounds and Biochemical Activity of Curcuma xanthorrhiza Roxb. Essential Oil Extracted from Distinct Shaded Plants
by Waras Nurcholis, Rahmadansah Rahmadansah, Puji Astuti, Bambang Pontjo Priosoeryanto, Rini Arianti and Endre Kristóf
Plants 2024, 13(19), 2682; https://doi.org/10.3390/plants13192682 - 25 Sep 2024
Abstract
The application of shade during plants’ growth significantly alters the biochemical compounds of the essential oil (EO). We aimed to analyze the effect of shade on the volatile compounds and biochemical activities of EO extracted from Curcuma xanthorrhiza Roxb. (C. xanthorrhiza) [...] Read more.
The application of shade during plants’ growth significantly alters the biochemical compounds of the essential oil (EO). We aimed to analyze the effect of shade on the volatile compounds and biochemical activities of EO extracted from Curcuma xanthorrhiza Roxb. (C. xanthorrhiza) plants. Four shading conditions were applied: no shading (S0), 25% (S25), 50% (S50), and 75% shade (S75). The volatile compounds of EO extracted from each shaded plant were analyzed by gas chromatography–mass spectrometry. The antioxidant, antibacterial, and antiproliferative activities of EO were also investigated. We found that shade application significantly reduced the C. xanthorrhiza EO yield but increased its aroma and bioactive compound concentration. α-curcumene, xanthorrhizol, α-cedrene, epicurzerenone, and germacrone were found in EO extracted from all conditions. However, β-bisabolol, curzerene, curcuphenol, and γ-himachalene were only detected in the EO of S75 plants. The EO of the shaded plants also showed higher antioxidant activity as compared to unshaded ones. In addition, the EO extracted from S75 exerted higher antiproliferative activity on HeLa cells as compared to S0. The EO extracted from S0 and S25 showed higher antibacterial activity against Gram-positive bacteria than kanamycin. Our results suggest that shade applications alter the composition of the extractable volatile compounds in C. xanthorrhiza, which may result in beneficial changes in the biochemical activity of the EO. Full article
(This article belongs to the Special Issue Secondary Metabolites in Plants)
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<p>(<b>a</b>) The workflow of essential oil extraction from <span class="html-italic">C. xanthorrhiza</span> Roxb. rhizomes. (<b>b</b>) Yield (% <span class="html-italic">v</span>/<span class="html-italic">w</span>) of <span class="html-italic">C. xanthorrhiza</span> essential oil. Extraction was performed in 3 independent plants, with statistical analysis by one-way ANOVA followed by Tukey’s post hoc test, *** <span class="html-italic">p</span> &lt; 0.001. S0, no shade; S25, 25% shade; S50, 50% shade; S75, 75% shade.</p>
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<p>Chemical compositions of <span class="html-italic">C. xanthorrhiza</span> Roxb. essential oil. (<b>a</b>) Pie chart displaying the groups of secondary metabolites in <span class="html-italic">C. xanthorrhiza</span> essential oil from 4 shading conditions detected by GC-MS. (<b>b</b>) Heatmap displaying the abundance of 64 compounds detected by GC-MS in <span class="html-italic">C. xanthorrhiza</span> essential oil from each shade condition. Three replicates of EO extraction were pooled and subjected to chemical compound analysis.</p>
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<p>Antioxidant activity of <span class="html-italic">C. xanthorrhiza</span> Roxb. essential oil measured by DPPH and FRAP assays. n = 3, statistical analysis by one-way ANOVA followed by Tukey’s post hoc test, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001. S0, no shade; S25, 25% shade; S50, 50% shade; S75, 75% shade. DPPH, 2,2′-diphenyl-1-picrylhydrazyl; DW, dry weight; FRAP, ferric reducing antioxidant power; TE, Trolox equivalent; DW: dry weight.</p>
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<p>Antiproliferative activity of <span class="html-italic">C. xanthorrhiza</span> Roxb. essential oil (4 µg/mL) measured in vero (<b>left</b>), MCF-7 (<b>middle</b>), or HeLa (<b>right</b>) cells. n = 3, statistical analysis by one-way ANOVA followed by Tukey’s post hoc test, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001. S0, no shade; S25, 25% shade; S50, 50% shade; S75, 75% shade. Doxo: doxorubicin (0.2 µg/mL).</p>
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<p>Antibacterial activity of <span class="html-italic">C. xanthorrhiza</span> Roxb. essential oil (4 µg/mL) tested against <span class="html-italic">S. aureus</span> (<b>left</b>) or <span class="html-italic">E. coli</span> (<b>right</b>). n = 3, statistical analysis by one-way ANOVA followed by Tukey’s post hoc test, */# <span class="html-italic">p</span> &lt; 0.05, ### <span class="html-italic">p</span> &lt; 0.001. * Analysis was performed by using S0 as reference. # Analysis was performed by using KS as reference. S0, no shade; S25, 25% shade; S50, 50% shade; S75, 75% shade. KS: kanamycin sulfate (1 mg/mL).</p>
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16 pages, 1477 KiB  
Article
Stimulation of Arabidopsis thaliana Seed Germination at Suboptimal Temperatures through Biopriming with Biofilm-Forming PGPR Pseudomonas putida KT2440
by Chandana Pandey, Anna Christensen, Martin N. P. B. Jensen, Emilie Rose Rechnagel, Katja Gram and Thomas Roitsch
Plants 2024, 13(19), 2681; https://doi.org/10.3390/plants13192681 - 24 Sep 2024
Abstract
This study investigated the germination response to temperature of seeds of nine Arabidopsis thaliana ecotypes. They are characterized by a similar temperature dependency of seed germination, and 10 °C and 29 °C were found to be suboptimal low and high temperatures for all [...] Read more.
This study investigated the germination response to temperature of seeds of nine Arabidopsis thaliana ecotypes. They are characterized by a similar temperature dependency of seed germination, and 10 °C and 29 °C were found to be suboptimal low and high temperatures for all nine ecotypes, even though they originated from regions with diverse climates. We tested the potential of four PGPR strains from the genera Pseudomonas and Bacillus to stimulate seed germination in the two ecotypes under these suboptimal conditions. Biopriming of seeds with only the biofilm-forming strain Pseudomonas putida KT2440 significantly increased the germination of Cape Verde Islands (Cvi-0) seeds at 10 °C. However, biopriming did not significantly improve the germination of seeds of the widely utilized ecotype Columbia 0 (Col-0) at any of the two tested temperatures. To functionally investigate the role of KT2440’s biofilm formation in the stimulation of seed germination, we used mutants with compromised biofilm-forming abilities. These bacterial mutants had a reduced ability to stimulate the germination of Cvi-0 seeds compared to wild-type KT2440, highlighting the importance of biofilm formation in promoting germination. These findings highlight the potential of PGPR-based biopriming for enhancing seed germination at low temperatures. Full article
(This article belongs to the Special Issue New Horizons in Plant–Microbe Interactions)
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<p>Illustration of the experimental design. (1) Plant Growth-Promoting Rhizobacteria (PGPR) were grown on solid LB plates. (2) Approximately 10 mg of <span class="html-italic">Arabidopsis thaliana</span> seeds were sterilized and stratified at 4 °C for two days. (3) Fresh bacterial colonies were inoculated in liquid LB medium and grown overnight. (4) The bacterial culture was centrifuged and the pellet was resuspended in 10 mM MgCl<sub>2</sub>. The optical density at 600 nm was adjusted to 0.4. (5) <span class="html-italic">A. thaliana</span> seeds were inoculated with the bacterial suspension; for the control treatment, the seeds were inoculated with 10 mM MgCl<sub>2</sub>. (6) The bacterial suspension and <span class="html-italic">A. thaliana</span> seeds were then transferred to a germination box. The box contained an insert surrounded by two filter paper strips to absorb the suspension from the reservoir, with a square filter paper on top where the seeds were placed. (7) The effect of PGPR on <span class="html-italic">A. thaliana</span> seed germination was investigated at suboptimal low and high temperatures. (8) Germination was assessed using a magnifying lens.</p>
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<p>Seed germination percentages at different temperatures for the nine ecotypes. Germination of seeds from nine <span class="html-italic">A. thaliana</span> ecotypes observed at different temperatures (10 °C, 17 °C, 24 °C, and 29 °C) after a 7 dai (days after inoculation). Germination was assessed under in-vitro conditions using germination boxes. The highest point on the curve indicates the most favorable temperature for germination specific to each ecotypes, Col0 (Col-0; 19.6 °C), Cvi (Cvi-0; 19.6 °C), Bur (20.6 °C), C24 (17.7 °C), Bla1 (Bla-1; 17.6 °C), Neo6 (Neo-6; 18.2 °C), N13 (20.2 °C), Dja1 (Dja-1; 18.7 °C), and MS0 (17.8 °C). The dotted line represents the polynomial regression, and the fitted polynomial regression equation is shown in <a href="#app1-plants-13-02681" class="html-app">Supplementary Table S1</a>.</p>
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<p>Effect of PGPR on seed germination at suboptimal temperatures (10 °C and 29 °C) in Col-0 and Cvi-0 ecotypes. Seed germination was assessed at suboptimal temperatures (10 °C and 29 °C) for two <span class="html-italic">A. thaliana</span> ecotypes in the presence of <span class="html-italic">Pseudomonas</span> and <span class="html-italic">Bacillus</span> PGPR. Strain KT2440 (KT) showed increased germination in the Cvi (Cvi-0) ecotype under suboptimal low temperatures (10 °C). Significant differences between each bacterial treatment are depicted using different lowercase letters. Error bars in each figure represent the standard deviation of independent experiments.</p>
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<p>Impact of KT2440 (KT) and its biofilm mutants on seed germination under suboptimal low temperatures (10 °C) in the Cvi-0 ecotype. Seed germination was investigated at a suboptimal low temperature (10 °C) in the presence of biofilm-forming KT2440 and its mutant q/<span class="html-italic">alg</span><sup>−</sup>, with compromised biofilm-forming ability. Significant differences between each bacterial treatment are depicted using lowercase letters. Error bars in each figure represent the standard deviation among the independent experiments.</p>
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25 pages, 12604 KiB  
Article
Decoding the Transcriptomics of Oil Palm Seed Germination
by Padungsak Suksa-Ard, Sunya Nuanlaong, Chettupon Pooljun, Azzreena Mohamad Azzeme and Potjamarn Suraninpong
Plants 2024, 13(19), 2680; https://doi.org/10.3390/plants13192680 - 24 Sep 2024
Abstract
Seed dormancy and germination are critical factors affecting oil palm production efficiency. The typical dormancy-breaking process involves dry heat treatment (38–40 °C for 40–60 days) followed by germination at 30–32 °C. To understand the molecular mechanisms behind this process and improve germination rates [...] Read more.
Seed dormancy and germination are critical factors affecting oil palm production efficiency. The typical dormancy-breaking process involves dry heat treatment (38–40 °C for 40–60 days) followed by germination at 30–32 °C. To understand the molecular mechanisms behind this process and improve germination rates and speed, we conducted transcriptome analysis at three stages: pre-incubation, 45-day incubation at 40 °C, and 14-day germination at 32 °C. Our findings, supported by qRT–PCR and DEGs analysis, identified four key stages: ABA degradation, energy mobilization, starch mobilization, and cell elongation and division. ABA pathway genes (SnRK2, PYR/PYL) were active during dormancy release, while GAE and GPI were upregulated after heat treatment, indicating increased energy metabolism and structural changes. During germination, genes involved in starch/sucrose metabolism (SPS, TPP, SS, MGAM) and cell wall biosynthesis (GAUT1, PE, GAE) supported embryo expansion, with BAM, PGM, GlgB fueling early growth. Auxin (TIR1, AUX/IAA, ARF), brassinosteroid (BRI1, BSK, BIN2, CYCD3), ethylene (ETR, CTR1), and jasmonic acid (JAR1, COI1) pathway genes regulated cell growth and stress response, promoting seedling development. Though gibberellins were not crucial for this oil palm variety, gene expression varied between varieties. This study provides information on oil palm seed germination that could be applied to other oil palm species, particularly in terms of incubation times and chemical treatments. Full article
(This article belongs to the Special Issue Advancements in Plant Genetics and Genome Characterization)
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<p>The distribution of unigenes in the NR database. (<b>a</b>) E-value distribution. (<b>b</b>) Species distribution.</p>
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<p>Gene ontology classification of unigenes at the secondary level.</p>
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<p>KOG function classification of unigenes.</p>
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<p>KEGG function classification of unigenes. (A) Cellular processes. (B) Environmental information processing. (C) Genetic information processing. (D) Metabolism. (E) Organismal systems.</p>
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<p>The differentially expressed gene analysis at each stage of oil palm seed germination. (<b>a</b>–<b>c</b>) Volcano plots. (<b>d</b>) A Venn diagram. (<b>e</b>) Gene cluster analysis was performed for each sample using FPKM values, with high-expression genes marked in red and low-expression genes marked in blue. C_em = seeds before incubation; G_em = seeds after 40 °C incubation for 45 days; H_em = germinated seedlings after 32 °C incubation for 14 days.</p>
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<p>GO terms of genes differentially expressed between oil palm seed germination stages. (<b>a</b>) Enriched GO terms of G_em vs. C_em. (<b>b</b>) Enriched GO terms of G_em vs. H_em. (<b>c</b>) Enriched GO terms of H_em vs. G_em (BP = biological process; CC = cellular component; MF = molecular function; C_em = seeds before incubation; G_em = seeds after 40 °C incubation for 45 days; H_em = germinated seedlings after 32 °C incubation for 14 days).</p>
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<p>KEGG pathway enrichment analysis of the differentially expressed genes between oil palm seed germination stages. (<b>a</b>) DEGs between G_em and C_em. (<b>b</b>) DEGs between G_em and H_em. (<b>c</b>) DEGs between H_em and G_em. (C_em = seeds before incubation; G_em = seeds after 40 °C incubation for 45 days; H_em = germinated seedlings after 32 °C incubation for 14 days).</p>
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<p>The expression of genes associated with plant hormone signal transduction validated via transcriptome analysis and qRT–PCR across four stages of oil palm seed germination. cDNA1 = seeds before incubation; cDNA2 = seeds after 40 °C incubation for 45 days; C. cDNA3 = germinated seedlings after 32 °C incubation for 14 days; cDNA4 = shoots of germinating seeds incubated at 32 °C for 60 days; cDNA5 = roots of germinating seeds incubated at 32 °C for 60 days. * = <span class="html-italic">p</span> &lt; 0.05, statistical significance at the 5% level; ** = <span class="html-italic">p</span> &lt; 0.01, statistical significance at the 1% level; *** = <span class="html-italic">p</span> &lt; 0.001, statistical significance at the 0.1% level; ns = Not significant (<span class="html-italic">p</span> ≥ 0.05); NA = Not Available.</p>
Full article ">Figure 8 Cont.
<p>The expression of genes associated with plant hormone signal transduction validated via transcriptome analysis and qRT–PCR across four stages of oil palm seed germination. cDNA1 = seeds before incubation; cDNA2 = seeds after 40 °C incubation for 45 days; C. cDNA3 = germinated seedlings after 32 °C incubation for 14 days; cDNA4 = shoots of germinating seeds incubated at 32 °C for 60 days; cDNA5 = roots of germinating seeds incubated at 32 °C for 60 days. * = <span class="html-italic">p</span> &lt; 0.05, statistical significance at the 5% level; ** = <span class="html-italic">p</span> &lt; 0.01, statistical significance at the 1% level; *** = <span class="html-italic">p</span> &lt; 0.001, statistical significance at the 0.1% level; ns = Not significant (<span class="html-italic">p</span> ≥ 0.05); NA = Not Available.</p>
Full article ">Figure 8 Cont.
<p>The expression of genes associated with plant hormone signal transduction validated via transcriptome analysis and qRT–PCR across four stages of oil palm seed germination. cDNA1 = seeds before incubation; cDNA2 = seeds after 40 °C incubation for 45 days; C. cDNA3 = germinated seedlings after 32 °C incubation for 14 days; cDNA4 = shoots of germinating seeds incubated at 32 °C for 60 days; cDNA5 = roots of germinating seeds incubated at 32 °C for 60 days. * = <span class="html-italic">p</span> &lt; 0.05, statistical significance at the 5% level; ** = <span class="html-italic">p</span> &lt; 0.01, statistical significance at the 1% level; *** = <span class="html-italic">p</span> &lt; 0.001, statistical significance at the 0.1% level; ns = Not significant (<span class="html-italic">p</span> ≥ 0.05); NA = Not Available.</p>
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<p>The expression of genes associated with starch and sucrose metabolism validated via transcriptome analysis and qRT–PCR across four stages of oil palm seed germination. cDNA1 = seeds before incubation; cDNA2 = seeds after 40 °C incubation for 45 days; C. cDNA3 = germinated seedlings after 32 °C incubation for 14 days; cDNA4 = shoots of germinating seeds incubated at 32 °C for 60 days and cDNA5 = roots of germinating seeds incubated at 32 °C for 60 days. * = <span class="html-italic">p</span> &lt; 0.05, statistical significance at the 5% level; ** = <span class="html-italic">p</span> &lt; 0.01, statistical significance at the 1% level; *** = <span class="html-italic">p</span> &lt; 0.001, statistical significance at the 0.1% level; ns = Not significant (<span class="html-italic">p</span> ≥ 0.05); NA = Not Available.</p>
Full article ">Figure 9 Cont.
<p>The expression of genes associated with starch and sucrose metabolism validated via transcriptome analysis and qRT–PCR across four stages of oil palm seed germination. cDNA1 = seeds before incubation; cDNA2 = seeds after 40 °C incubation for 45 days; C. cDNA3 = germinated seedlings after 32 °C incubation for 14 days; cDNA4 = shoots of germinating seeds incubated at 32 °C for 60 days and cDNA5 = roots of germinating seeds incubated at 32 °C for 60 days. * = <span class="html-italic">p</span> &lt; 0.05, statistical significance at the 5% level; ** = <span class="html-italic">p</span> &lt; 0.01, statistical significance at the 1% level; *** = <span class="html-italic">p</span> &lt; 0.001, statistical significance at the 0.1% level; ns = Not significant (<span class="html-italic">p</span> ≥ 0.05); NA = Not Available.</p>
Full article ">Figure 9 Cont.
<p>The expression of genes associated with starch and sucrose metabolism validated via transcriptome analysis and qRT–PCR across four stages of oil palm seed germination. cDNA1 = seeds before incubation; cDNA2 = seeds after 40 °C incubation for 45 days; C. cDNA3 = germinated seedlings after 32 °C incubation for 14 days; cDNA4 = shoots of germinating seeds incubated at 32 °C for 60 days and cDNA5 = roots of germinating seeds incubated at 32 °C for 60 days. * = <span class="html-italic">p</span> &lt; 0.05, statistical significance at the 5% level; ** = <span class="html-italic">p</span> &lt; 0.01, statistical significance at the 1% level; *** = <span class="html-italic">p</span> &lt; 0.001, statistical significance at the 0.1% level; ns = Not significant (<span class="html-italic">p</span> ≥ 0.05); NA = Not Available.</p>
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<p>Summary of upregulated genes related to plant hormone signal transduction and starch/sucrose metabolism via qRT–PCR and transcriptomic data during oil palm seed germination across three stages: seeds before incubation, seeds incubated at 40 °C for 45 days, and germinated seedlings incubated at 32 °C for 14 days.</p>
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<p>The stages of oil palm seed germination used in this study. (<b>a</b>) C_em = seeds before incubation. (<b>b</b>) G_em = seeds after 40 °C incubation for 45 days. (<b>c</b>) H_em = germinated seedlings after 32 °C incubation for 14 days (red arrows = roots; green arrow = shoot).</p>
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