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Volume 13
Issue 17
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Plants, Volume 13, Issue 17 (September-1 2024) – 195 articles

Cover Story (view full-size image): Understanding the factors driving seedling growth and mortality is essential to predicting successional trajectories of secondary forests. We investigated the relationships of 15 seedling traits (above-ground and below-ground organ-level traits and biomass allocation traits) with growth and mortality for 26 tree species across a successional gradient in Costa Rica. Site-specific conditions influenced how traits mediated seedling growth and mortality. Allocation traits displayed the greatest significant intraspecific differences and were stronger predictors of seedling performance than organ-level traits. Our results highlight the importance of biomass allocation in early tree life stages and that how traits affect vital rates can differ depending on environmental conditions. View this paper
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14 pages, 4266 KiB  
Article
Assessment of the Restoration Potential of Forest Vegetation Coverage in the Alxa Desert Region of China
by Yanlin Pan, Dongmeng Zhou, Jianhua Si and Bing Jia
Plants 2024, 13(17), 2536; https://doi.org/10.3390/plants13172536 - 9 Sep 2024
Viewed by 724
Abstract
To scientifically evaluate the sustainability of tree planting and afforestation in the Alxa Desert region, this study, grounded in the principles of water balance within the natural water cycle, employed multi-source remote sensing products and ground-based measurements to construct a quantitative response relationship [...] Read more.
To scientifically evaluate the sustainability of tree planting and afforestation in the Alxa Desert region, this study, grounded in the principles of water balance within the natural water cycle, employed multi-source remote sensing products and ground-based measurements to construct a quantitative response relationship model. This model links evapotranspiration (ET) with meteorological variables and the Enhanced Vegetation Index (EVI). Furthermore, the study estimated the recovery thresholds and potential of forest and grassland vegetation coverage in the Alxa Desert region under various precipitation scenarios. The findings reveal that ET exhibited an increasing trend in 84.17% of the Alxa Desert region, with a significant increase observed in 61.53% of the area, indicating positive outcomes from the implementation of the Three-North Shelterbelt Forest Program. Notably, however, ET in the southeastern plain region demonstrated a decreasing trend, which is strongly associated with human activities. The response relationship model demonstrated that linear relationship areas constituted 47.52%, while nonlinear relationship areas accounted for 45.51% of the total. The overall model exhibited an R2 value of 0.69, indicating a high level of predictive accuracy. Analysis of forest and grassland coverage revealed that, under wet year scenarios, the vegetation coverage showed a significant trend of recovery, with an average recovery threshold of (75.4 ± 12.5)% and an average recovery potential of (8.5 ± 3.6)%. It is noteworthy that the vegetation coverage in 31.25% of the area had already surpassed the recovery threshold. The outcomes of this study provide a theoretical foundation for the formulation of more scientifically rigorous ecological restoration strategies in the future. Full article
(This article belongs to the Section Plant Ecology)
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<p>Location of the study area in the Alxa Legue desert, China.</p> Full article ">Figure 2
<p>Validation Results of MODIS and PML_V2 ET Products Based on Station Observation Data.</p> Full article ">Figure 3
<p>2000–2020 Spatiotemporal Distribution of ET and EVI in the Alxa Desert Region.</p> Full article ">Figure 4
<p>Scatter Plot of EVI and ET Relationship in the Study Area.</p> Full article ">Figure 5
<p>Response relationship between ET and EVI.</p> Full article ">Figure 6
<p>Recovery Threshold and Potential of Forest and Grass Vegetation Coverage, (<b>A</b>) FVC<sub>thr</sub> in Wet Years; (<b>B</b>) FVC<sub>thr</sub> in Normal Years; (<b>C</b>) FVC<sub>thr</sub> in Dry Years; (<b>D</b>) FVC<sub>pot</sub> in Wet Years; (<b>E</b>) FVC<sub>pot</sub> in Normal Years; (<b>F</b>) FVC<sub>pot</sub> in Dry Years. FVC<sub>thr</sub>: Restoration threshold of fractional vegetation cover; FVC<sub>pot</sub>: Restoration potential of fractional vegetation cover.</p> Full article ">
16 pages, 3627 KiB  
Article
Phylogeography and Population Variation in Prunus discoidea (Prunus subg. Cerasus) in China
by Xiangzhen Chen, Shucheng Gao, Hong Yang, Wenyi Fu, Siyu Qian, Xianrong Wang and Xiangui Yi
Plants 2024, 13(17), 2535; https://doi.org/10.3390/plants13172535 - 9 Sep 2024
Viewed by 871
Abstract
Prunus discoidea is a unique cherry blossom germplasm resource native to China. It is widely distributed across the provinces of Anhui, Zhejiang, Jiangxi, Jiangsu, and Henan, with significant variation. We employed phylogeographic analysis to reveal the evolutionary history of P. discoidea to better [...] Read more.
Prunus discoidea is a unique cherry blossom germplasm resource native to China. It is widely distributed across the provinces of Anhui, Zhejiang, Jiangxi, Jiangsu, and Henan, with significant variation. We employed phylogeographic analysis to reveal the evolutionary history of P. discoidea to better understand its genetic diversity and structure. This study provides more accurate molecular insights for the effective conservation and utilization of this germplasm resource. We conducted a phylogeographic analysis of 348 individual plants from 13 natural populations using three fragments (rpoB, rps16, and trnD–E) of chloroplast DNA (cpDNA) and one fragment (ITS) of ribosomal DNA. The results revealed that P. discoidea demonstrates a significant level of genetic diversity (Hd = 0.782; Rd = 0.478). Gene flow among populations was limited, and the variation within populations was the main source of genetic diversity in P. discoidea (among populations: 34.26%, within populations: 65.74%). Regarding genetic differences among populations, Nst (0.401) showed greater differences than Gst (0.308; p < 0.05), demonstrating that there was a significant geographical structure of lineage. One lineage was the central region of Anhui and the western region of Hubei. The other lineage was the Jiangsu region and the Zhejiang region. P. discoidea diverged from Prunus campanulata approximately 1.5 million years ago, during the Pleistocene epoch. This study provides a scientific theoretical basis for the conservation and utilization of germplasm resources of P. discoidea. Full article
(This article belongs to the Special Issue Origin and Evolution of the East Asian Flora (EAF))
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<p>(<b>A</b>) Geographical distribution map of the haplotypes of <span class="html-italic">P. discoidea</span> based on cpDNA sequences. (<b>B</b>) Network diagram of the TCS haplotypes of <span class="html-italic">P. discoidea</span> based on cpDNA sequences. (<b>C</b>) Geographical distribution map of the ribotypes of <span class="html-italic">P. discoidea</span> based on nrDNA sequences. (<b>D</b>) Network diagram of TCS ribotypes of <span class="html-italic">P. discoidea</span> based on nrDNA sequences.</p> Full article ">Figure 2
<p>(<b>A</b>) ML tree of <span class="html-italic">P. discoidea</span> haplotypes based on cpDNA sequences. (<b>B</b>) BI tree of <span class="html-italic">P. discoidea</span> haplotypes based on cpDNA sequences.</p> Full article ">Figure 3
<p>(<b>A</b>) ML tree of <span class="html-italic">P. discoidea</span> ribotypes based on nrDNA sequences. (<b>B</b>) BI tree of <span class="html-italic">P. discoidea</span> ribotypes based on nrDNA sequences.</p> Full article ">Figure 4
<p>Phylogenetic tree of Rosaceae based on chloroplast DNA (<span class="html-italic">rps</span>16) and four fossil dates.</p> Full article ">Figure 5
<p>Sampling points and population distribution points of <span class="html-italic">P. discoidea</span>. Note: Green represents the sampling points, and red represents the population distribution points.</p> Full article ">
16 pages, 10793 KiB  
Article
A Novel Method for the Enhancement of Sunflower Growth from Animal Bones and Chicken Feathers
by Ume Laila, Mishkat ul Huda, Isha Shakoor, Aisha Nazir, Muhammad Shafiq, Firdaus e Bareen, Kamran Shaukat and Talha Mahboob Alam
Plants 2024, 13(17), 2534; https://doi.org/10.3390/plants13172534 - 9 Sep 2024
Viewed by 854
Abstract
The present study aimed at converting meat industry waste, particularly waste bones and chicken feathers, into biochar to recycle valuable nutrients present in it, which ultimately become part of the municipal waste. The bone biochar (BB) and feathers biochar (FB) were prepared at [...] Read more.
The present study aimed at converting meat industry waste, particularly waste bones and chicken feathers, into biochar to recycle valuable nutrients present in it, which ultimately become part of the municipal waste. The bone biochar (BB) and feathers biochar (FB) were prepared at 550 °C, and their potential was evaluated as an organic amendment for the growth of sunflower. The ash content (AC) and fixed carbon (FC) improved significantly in prepared biochars as compared to raw feedstock. Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDX) analyses signaled the occurrence of various functional groups viz. amide group and hydroxyapatite, porosity, and multiple nutrients. Application of BB and FB in potted soil alone as well as in composites (1:1, 1:2, 2:1) at 1%, 3%, and 5% (w/w) and synthetic fertilizer significantly increased soil pH, electrical conductivity (ECe), organic matter (OM) and water holding capacity (WHC), while reducing the bulk density (BD). The growth of plants grown in soil treated with a 2:1 composite of feathers and bone biochar at 5% application rate showed significantly greater differences in plant height, total chlorophyll content, and plant dry weight than the control but was comparable to growth with chemical fertilizer, rendering it a potential alternative to chemical-based synthetic fertilizer. Full article
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<p>The FTIR spectra of (<b>a</b>) bone char and (<b>b</b>) feather biochar.</p> Full article ">Figure 2
<p>Scanning electron micrographs of bone biochar (BB) at (<b>a</b>) 130×, (<b>b</b>) 250×, (<b>c</b>) 500× and (<b>d</b>) 1000×.</p> Full article ">Figure 3
<p>Scanning electron micrographs of feather biochar (FB) at (<b>a</b>) 130×, (<b>b</b>) 250×, (<b>c</b>) 500× and (<b>d</b>) 1000×. Arrows are indicating the pores.</p> Full article ">Figure 4
<p>EDX spectra of bone biochar (<b>a</b>) and feather biochar (<b>b</b>).</p> Full article ">Figure 5
<p>Variations in growth measurements. (<b>a</b>) Seed yield, (<b>b</b>) plant height, (<b>c</b>) SPAD values, and (<b>d</b>) plant dry biomass of sunflowers cultivated under different application levels of bone char (BB), feather-derived biochar (FB) and biochar composites (M1, M2, M3) along with control and commercial fertilizer. The values represented with similar letters are statistically not different, with reference to Duncan’s multiple range test (<span class="html-italic">p</span> = 0.05). The bar represents the standard error.</p> Full article ">Figure 5 Cont.
<p>Variations in growth measurements. (<b>a</b>) Seed yield, (<b>b</b>) plant height, (<b>c</b>) SPAD values, and (<b>d</b>) plant dry biomass of sunflowers cultivated under different application levels of bone char (BB), feather-derived biochar (FB) and biochar composites (M1, M2, M3) along with control and commercial fertilizer. The values represented with similar letters are statistically not different, with reference to Duncan’s multiple range test (<span class="html-italic">p</span> = 0.05). The bar represents the standard error.</p> Full article ">
15 pages, 5525 KiB  
Article
Dentatacid A: An Unprecedented 2, 3-Seco-arbor-2, 3-dioic Triterpenoid from the Invasive Plant Euphorbia dentata, with Cytotoxicity Effect on Colon Cancer
by Chen-Sen Xu, Yuan-Ling Shao, Qing Li, Yu Zhang, Hong-Wei Wu, Hao-Lin Yu, Yun-Yun Su, Jing Zhang, Chao Wang and Zhi-Xin Liao
Plants 2024, 13(17), 2533; https://doi.org/10.3390/plants13172533 - 9 Sep 2024
Cited by 1 | Viewed by 1048
Abstract
Euphorbia dentata Michx. is an invasive plant species in China, known for its toxicity and potential to reduce crop yields, posing numerous threats. To gain a deeper understanding of this invasive plant, phytochemical methods were employed to isolate 13 terpenoids (1– [...] Read more.
Euphorbia dentata Michx. is an invasive plant species in China, known for its toxicity and potential to reduce crop yields, posing numerous threats. To gain a deeper understanding of this invasive plant, phytochemical methods were employed to isolate 13 terpenoids (1–11, 19, 20) and 7 sterols (12–18) from the ethanol extract of E. dentata, identifying one new compound and 19 known compounds. Within spectroscopic methods such as NMR, HR-ESI-MS, and ECD, the structures and absolute configurations of these compounds were established. Among them, dentatacid A (11) possesses an unprecedented 2, 3-seco-arbor-2, 3-dioic skeleton within the potential biosynthetic pathway proposed. Dentatacid A also exhibited excellent anti-proliferative activity against the HT-29 (human colorectal adenocarcinoma) cell line, with an IC50 value of 2.64 ± 0.78 μM, which was further confirmed through network pharmacology and molecular docking. This study significantly expands the chemical diversity of E. dentata and offers new insights into the resource utilization and management of this invasive plant from the perspective of natural product discovery. Full article
(This article belongs to the Section Phytochemistry)
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<p><span class="html-italic">Euphorbia dentata</span> Michx. in Yunnan Province, People’s Republic of China.</p> Full article ">Figure 2
<p>Structures of compounds <b>1</b>–<b>20</b>.</p> Full article ">Figure 3
<p>The structure (<b>A</b>), the key HMBC, and <sup>1</sup>H–<sup>1</sup>H COSY correlations (<b>B</b>) of <b>11</b>.</p> Full article ">Figure 4
<p>Key NOESY correlations of <b>11</b>.</p> Full article ">Figure 5
<p>Calculated and experimental ECD spectra for <b>11</b>.</p> Full article ">Figure 6
<p>Plausible biogenetic pathways for dentatacid A (<b>1</b>).</p> Full article ">Figure 7
<p>Target prediction and enrichment analysis results of triterpenoids from <span class="html-italic">E. dentata</span> and colon cancer. (<b>A</b>) The flowchart about the targets of triterpenoids and the target prediction of colon cancer. (<b>B</b>) The Venn diagram of triterpenoids—colon cancer targets. (<b>C</b>) The PPI network of the intersection target. (<b>D</b>) The GO enrichment analysis of the intersection targets. The green, orange, and purple columns respectively represent the intersecting targets related to Biological Process (BP), Cellular Components (CC), and Molecular Functions (MF). (<b>E</b>) The KEGG pathway enrichment analysis of common targets. The bubble size represents the number of intersecting targets enriched in a signaling pathway while the color of the bubble represents the p-value.</p> Full article ">Figure 8
<p>Molecular docking analysis of <b>11</b> with SRC protein (PDB ID: 8JN8, as shown in (<b>A</b>)) and PIK3CA protein (PDB ID: 7R9V, as shown in (<b>B</b>)).</p> Full article ">
16 pages, 6851 KiB  
Article
Genome-Wide Characterization of IQD Family Proteins in Apple and Functional Analysis of the Microtubule-Regulating Abilities of MdIQD17 and MdIQD28 under Cold Stress
by Yu Zhang, Shengjie Wang, Chaochao Zhang, Meng Qi, Luoqi Liu, Lipeng Yang and Na Lian
Plants 2024, 13(17), 2532; https://doi.org/10.3390/plants13172532 - 9 Sep 2024
Viewed by 669
Abstract
Microtubules undergo dynamic remodeling in response to diverse abiotic stress in plants. The plant-specific IQ67 DOMAIN (IQD) family proteins serve as microtubule-associated proteins, playing multifaceted roles in plant development and response to abiotic stress. However, the biological function of IQD genes in apple [...] Read more.
Microtubules undergo dynamic remodeling in response to diverse abiotic stress in plants. The plant-specific IQ67 DOMAIN (IQD) family proteins serve as microtubule-associated proteins, playing multifaceted roles in plant development and response to abiotic stress. However, the biological function of IQD genes in apple remains unclear. In this study, we conducted a comprehensive analysis of the Malus domestica genome, identifying 42 IQD genes distributed across 17 chromosomes and categorized them into four subgroups. Promoter analysis revealed the presence of stress-responsive elements. Subsequent expression analysis highlighted the significant upregulation of MdIQD17 and MdIQD28 in response to cold treatments, prompting their selection for further functional investigation. Subcellular localization studies confirmed the association of MdIQD17 and MdIQD28 with microtubules. Crucially, confocal microscopy and quantification revealed diminished microtubule depolymerization in cells transiently overexpressing MdIQD17 and MdIQD28 compared to wild-type cells during cold conditions. In conclusion, this study provides a comprehensive analysis of IQD genes in apple, elucidating their molecular mechanism in response to cold stress. Full article
(This article belongs to the Special Issue Advances in Plant Anatomy and Cell Biology)
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<p>Chromosome location and synteny analysis of <span class="html-italic">MdIQDs</span>. A total of 42 <span class="html-italic">MdIQDs</span> were obtained in the apple genome. (<b>A</b>) Chromosome distribution analysis. (<b>B</b>) Synteny analysis. Duplicated <span class="html-italic">MdIQD</span> gene pairs were connected using the black lines.</p> Full article ">Figure 2
<p>The phylogenetic tree and collinear relationship analysis between different genomes. (<b>A</b>) Phylogenetic analysis of the MdIQDs family. MdIQD family members highlighted by light blue round dot and other species distinguished by different colors and shapes. (<b>B</b>) Yellow line, pink line and purple line represent collinear gene pairs of <span class="html-italic">Malus domestica</span> and <span class="html-italic">Arabidopsis thaliana</span>, <span class="html-italic">Malus domestica</span> and <span class="html-italic">Solanum tuberosum</span>, <span class="html-italic">Malus domestica</span> and <span class="html-italic">Populus trichocarpa</span>, respectively.</p> Full article ">Figure 3
<p>Phylogenetic relationship, gene structures, and conserved domains of MdIQDs. (<b>A</b>) The phylogenetic tree and gene structure of <span class="html-italic">MdIQD</span> genes. Green and yellow boxes represent UTR and CDS, respectively. (<b>B</b>) Motif compositions of MdIQD proteins. Different motifs highlighted with different colors. (<b>C</b>) Domain organization of MdIQD proteins.</p> Full article ">Figure 4
<p>Intrinsically disordered regions prediction of 42 MdIQD proteins. The Predictor of Natural Disordered Regions (PONDR) website (<a href="http://www.pondr.com/" target="_blank">http://www.pondr.com/</a>) was utilized to project the intrinsically disordered regions (IDRs). A score exceeding 0.5 signifies a high degree of disorder within the sequence. The red part of the figure represents the disordered region of the protein.</p> Full article ">Figure 5
<p><span class="html-italic">Cis</span>-acting elements analysis of <span class="html-italic">MdIQD</span> promoters. (<b>A</b>) Distribution of <span class="html-italic">cis</span>-acting elements in <span class="html-italic">MdIQD</span> promoters. (<b>B</b>) Statistics of <span class="html-italic">cis</span>-acting elements in each promoter region. The heatmap demonstrated the number of <span class="html-italic">cis</span>-elements with the higher number in red color and lower number with orange color.</p> Full article ">Figure 6
<p>Expression profiles of <span class="html-italic">MdIQD17</span> and <span class="html-italic">MdIQD28</span> in different tissues under cold treatment. Three-month-old apple seedlings were treated at 0 °C for various durations, followed by the collection of leaves, stems, and roots for total RNA extraction and RT-qPCR analysis. (<b>A</b>) Relative expression of <span class="html-italic">MdIQD17</span> and <span class="html-italic">MdIQD28</span> in the leaves. (<b>B</b>) Relative expression of <span class="html-italic">MdIQD17</span> and <span class="html-italic">MdIQD28</span> in the stems. (<b>C</b>) Relative expression of <span class="html-italic">MdIQD17</span> and <span class="html-italic">MdIQD28</span> in the roots. <span class="html-italic">MdUBQ</span> was used as a reference gene. Error bars represent standard error of the mean (n = 3). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p> Full article ">Figure 7
<p>Subcellular localization of MdIQD17 and MdIQD28 proteins. (<b>A</b>) MdIQD17-GFP and MdIQD28-GFP transiently expressed in tobacco cells, respectively. The filamentous pattern of MdIQD17-GFP and MdIQD28-GFP were disrupted when the cells were treated with 10 μM oryzalin for 10 min, 20 min, and 30 min. (<b>B</b>) The graph shows the number of microtubules. ** <span class="html-italic">p</span> &lt; 0.01. (<b>C</b>) Analysis of colocalization of transiently expressed MdIQD17-GFP and MdIQD28-GFP with MBD-mCherry.</p> Full article ">Figure 8
<p>MdIQD17 and MdIQD28 enhanced microtubule stability in response to cold stress. (<b>A</b>) Relative expression level of <span class="html-italic">MdIQD17</span> and <span class="html-italic">MdIQD28</span> in tobacco leaves. EV, Empty Vector control. (<b>B</b>) Cortical microtubules in tobacco leaf cells from wild-type (WT), MdIQD17-OE, and MdIQD28-OE under cold treatment (0 °C, 2 h). Bar = 10 μm. (<b>C</b>) Quantification of cortical microtubules. ** <span class="html-italic">p</span> &lt; 0.01 by <span class="html-italic">t</span>-test comparing the number of cortical microtubules in MdIQD17-OE and MdIQD28-OE cells with that in WT cells under the same conditions. (<b>D</b>) Electrolyte leakage from leaves of the tobacco leaves under cold treatment (0 °C, 2 h). ** <span class="html-italic">p</span> &lt; 0.01.</p> Full article ">
11 pages, 1169 KiB  
Article
Generic Workflow of a Highly Effective and Easy Anther Culture Method for Both Japonica and Indica Rice
by Guimei Guo, Shisen Liu, Shuwei Zhang, Linian Yang, Yingjie Zong, Nigel G. Halford, Ting He, Runhong Gao, Zhenzhu Guo, Longhua Zhou, Chenghong Liu, Shujun Wu and Zhiwei Chen
Plants 2024, 13(17), 2531; https://doi.org/10.3390/plants13172531 - 9 Sep 2024
Viewed by 982
Abstract
As one of the most important staple crops in the world, rice plays a pivotal role in world food security. The creation of doubled haploids based on anther culture is an important technology for rice breeding. However, at present, rice anther culture technology [...] Read more.
As one of the most important staple crops in the world, rice plays a pivotal role in world food security. The creation of doubled haploids based on anther culture is an important technology for rice breeding. However, at present, rice anther culture technology still faces many problems, such as genotype dependency, especially genotypes of indica rice. In this study, fifteen rice genotypes, including twelve japonica rice genotypes and three indica rice genotypes, were randomly selected and used to study anther culture by using a modified M8 medium. The results showed that the total callus induction rates of these different rice genotypes ranged from 0.81 to 13.95%, with an average of 6.64%, while the callus induction rates calculated for the top ten highest callus inductions for each rice genotype ranged from 2.75 to 17.00%, with an average of 10.56%. There were varying gaps between the total callus induction rates and the callus induction rates in these different rice genotypes. The fact that the gaps for some rice genotypes were relatively large indicated that standard tiller or anther collection was not applicable to all rice genotypes and that there was still a lot of room for improvement in the callus induction rate of some rice genotypes through optimization of the sampling method. The plantlet regeneration rates ranged from 12.55 to 456.54%, with an average of 200.10%. Although there were many albinos from anther culture for some rice genotypes, these would still meet the requirement if the rice genotypes had higher callus induction rates or regeneration rates. The percentages of seed setting of regenerated green seedlings ranged from 14% to 84%, with an average of 48.73%. Genetic diversity analysis showed that the genetic background of these different rice genotypes was representative, and the phylogenetic tree and Principal Component Analysis (PCA) divided them into indica and japonica types. Therefore, in this study, an anther culture method suitable for both indica and japonica rice genotypes was established, which could improve doubled haploid breeding in rice. Full article
(This article belongs to the Collection Advances in Plant Breeding)
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<p>Callus induction rate of different rice genotypes. Different letters mean significant differences in callus induction rate between different rice genotypes.</p> Full article ">Figure 2
<p>Ploidy identification of regenerated plants in the field at grain-filling stage. (<b>A</b>) Their growth in fields. (<b>B</b>) The comparison of diploid and haploid rice panicles. “D” means diploid and “H” means haploid.</p> Full article ">Figure 3
<p>Phylogenetic tree and principal component analysis (PCA) of fifteen rice genotypes based on 49,676 SNP markers. (<b>A</b>) The phylogenetic tree constructed using the neighbor-joining method; (<b>B</b>) The PCA plot.</p> Full article ">Figure 4
<p>The workflow of anther culture in rice.</p> Full article ">
12 pages, 1064 KiB  
Article
Identification of Insertion and Deletion (InDel) Markers for Chickpea (Cicer arietinum L.) Based on Double-Digest Restriction Site-Associated DNA Sequencing
by Duygu Sari
Plants 2024, 13(17), 2530; https://doi.org/10.3390/plants13172530 - 9 Sep 2024
Viewed by 642
Abstract
Enhancing the marker repository and the development of breeder-friendly markers in chickpeas is important in relation to chickpea genomics-assisted breeding applications. Insertion–deletion (InDel) markers are widely distributed across genomes and easily observed with specifically designed primers, leading to less time, cost, and labor [...] Read more.
Enhancing the marker repository and the development of breeder-friendly markers in chickpeas is important in relation to chickpea genomics-assisted breeding applications. Insertion–deletion (InDel) markers are widely distributed across genomes and easily observed with specifically designed primers, leading to less time, cost, and labor requirements. In light of this, the present study focused on the identification and development of InDel markers through the use of double-digest restriction site-associated DNA sequencing (ddRADSeq) data from 20 chickpea accessions. Bioinformatic analysis identified 20,700 InDel sites, including 15,031 (72.61%) deletions and 5669 (27.39%) insertions, among the chickpea accessions. The InDel markers ranged from 1 to 25 bp in length, while single-nucleotide-length InDel markers were found to represent the majority of the InDel sites and account for 79% of the total InDel markers. However, we focused on InDel markers wherein the length was greater than a single nucleotide to avoid any read or alignment errors. Among all of the InDel markers, 96.1% were less than 10 bp, 3.6% were between 10 and 20 bp, and 0.3% were more than 20 bp in length. We examined the InDel markers that were 10 bp and longer for the development of InDel markers based on a consideration of the genomic distribution and low-cost genotyping with agarose gels. A total of 29 InDel regions were selected, and primers were successfully designed to evaluate their efficiency. Annotation analysis of the InDel markers revealed them to be found with the highest frequency in the intergenic regions (82.76%), followed by the introns (6.90%), coding sequences (6.90%), and exons (3.45%). Genetic diversity analysis demonstrated that the polymorphic information content of the markers varied from 0.09 to 0.37, with an average of 0.20. Taken together, these results showed the efficiency of InDel marker development for chickpea genetic and genomic studies using the ddRADSeq method. The identified markers might prove valuable for chickpea breeders. Full article
(This article belongs to the Special Issue Genetic Diversity of Germplasm Resources in Cereals and Legumes)
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<p>Total number of reads and GC content (%) per accession.</p> Full article ">Figure 2
<p>Principal coordinate analysis (PCoA) of the 20 chickpea accessions genotyped with 21 InDel markers.</p> Full article ">Figure 3
<p>Phylogenetic tree constructed through the neighbor-joining method using InDel markers and 20 chickpea accessions.</p> Full article ">
13 pages, 2315 KiB  
Article
Lobelia Lakes’ Vegetation and Its Photosynthesis Pathways Concerning Water Parameters and the Stable Carbon Isotopic Composition of Plants’ Organic Matter
by Eugeniusz Pronin, Krzysztof Banaś, Rafał Chmara, Rafał Ronowski, Marek Merdalski, Anne-Lise Santoni and Olivier Mathieu
Plants 2024, 13(17), 2529; https://doi.org/10.3390/plants13172529 - 9 Sep 2024
Cited by 2 | Viewed by 695
Abstract
Most of the aquatic vegetation produces organic substances via the C3 photosynthetic pathway (mosses, isoetids—Lobelia dortmanna L., Luronium natans (L.) Raf., and vascular plants) or Crassulacean acid metabolism (CAM, e.g., Littorella uniflora (L.) Asch. and Isoëtes lacustris L.) or by their ability to use HCO3 [...] Read more.
Most of the aquatic vegetation produces organic substances via the C3 photosynthetic pathway (mosses, isoetids—Lobelia dortmanna L., Luronium natans (L.) Raf., and vascular plants) or Crassulacean acid metabolism (CAM, e.g., Littorella uniflora (L.) Asch. and Isoëtes lacustris L.) or by their ability to use HCO3− via carbon concentration mechanisms (CCMs—some elodeids and charophytes). Differentiating these predominant photosynthetic pathways in aquatic vegetation based on their organic matter (OM) carbon stable isotopes (δ13CORG) is a complex task, in contrast to terrestrial plants. This study investigates the OM deposition, characterized by δ13CORG values in 10 macrophyte species with different photosynthetic pathways (C3, CAM, and CCM) collected from 14 softwater Lobelia lakes in northern Poland. The higher δ13CORG values distinguish the CCM group, indicating their use of 13C-enriched HCO3¯ in photosynthesis. CAM species show slightly higher δ13CORG values than C3, particularly in lower pH lakes. Principal component analysis of isotopic and environmental data did not yield clear distinctions by the groups, but still, they significantly differ in light of analyzed parameters and isotopic signals (PRMANOVA = 5.08, p < 0.01; K-W H = 27.01, p < 0.001). The first two PCA dimensions showed that the water pH and Ca2+ concentration positively influenced δ13C values. The influence of light conditions on δ13CORG values revealed by third PCA components seems to also be important. In summary, northern Polish Lobelia lakes serve as a key differentiation point between vegetation employing CCMs and those relying on C3/CAM photosynthesis without HCO3− utilization, providing insights into transitions in plant communities within these ecosystems. Full article
(This article belongs to the Special Issue Physiology and Ecology of Aquatic Plants)
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<p>Comparison of δ<sup>13</sup>C values of plants’ OM investigated from a species group concerning their photosynthesis pathways and carbon acquisition mechanism (CCM). The lowercase letters above boxplots, if they differ, indicate the statistical significance of the Dunn post hoc (<span class="html-italic">p</span> &lt; 0.05) after the Kruskal–Wallis test.</p> Full article ">Figure 2
<p>Spearman rank correlations heat map of (<b>A</b>) ambient waters variables and (<b>B</b>) sediment water variables. OM <sub>SEDIMENTS</sub>—% of the OM in the sediments, TN—total nitrogen and TP—total phosphorus, DOC—dissolved organic carbon, PAR—photosynthetic active radiation, and PVI—percentage volume infested by plants. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001.</p> Full article ">Figure 3
<p>PCA analysis: (<b>A</b>)—ambient water variables and the δ<sup>13</sup>C of plants and other measured parameters (<span class="html-italic">n</span> = 85) and first and second dimensions, (<b>B</b>)—above sediment water variables, and δ<sup>13</sup>C of plants and other measured parameters (<span class="html-italic">n</span> = 85) and first and second dimensions, (<b>C</b>)—ambient water variables and the δ<sup>13</sup>C of plants and other measured parameters (<span class="html-italic">n</span> = 85) and first and third dimensions, and (<b>D</b>)—above sediment water variables, and δ<sup>13</sup>C of plants and other measured parameters (<span class="html-italic">n</span> = 85) and first and third dimensions. TN—total nitrogen and TP—total phosphorus, DOC—dissolved organic carbon, PAR—photosynthetic active radiation, PVI—percentage volume infested by plants, and OM<sub>SEDIMENTS</sub>—% of the OM in the sediments. The biggest circles indicated the centroids of each group.</p> Full article ">Figure 4
<p>Localization of the investigated lakes. The numbers correspond to the lake’s order provided in <a href="#plants-13-02529-t001" class="html-table">Table 1</a>.</p> Full article ">
18 pages, 3014 KiB  
Article
Zinc Enhances Cadmium Accumulation in Shoots of Hyperaccumulator Solanum nigrum by Improving ATP-Dependent Transport and Alleviating Toxicity
by Jia Zheng, Yukang Yue, Yuting Zhu, Yufeng Wang, Wenwen Zheng, Linfeng Hu, Dianyun Hou, Fayuan Wang, Liming Yang and Hongxiao Zhang
Plants 2024, 13(17), 2528; https://doi.org/10.3390/plants13172528 - 9 Sep 2024
Viewed by 1080
Abstract
Solanum nigrum is a cadmium (Cd) and zinc (Zn) accumulator with potential for phytoextraction of soil contaminated with heavy metals. However, how Zn affects Cd accumulation in S. nigrum remains unclear. In this study, S. nigrum seedlings were treated with 100 μmol·L−1 [...] Read more.
Solanum nigrum is a cadmium (Cd) and zinc (Zn) accumulator with potential for phytoextraction of soil contaminated with heavy metals. However, how Zn affects Cd accumulation in S. nigrum remains unclear. In this study, S. nigrum seedlings were treated with 100 μmol·L−1 Zn (Zn100), 100 μmol·L−1 Cd (Cd100), and the Zn and Cd combination (Zn100+Cd100) for 10 days under hydroponic culture. Compared with Cd100, the Cd content in stems, leaves, and xylem saps was 1.8, 1.6, and 1.3 times more than that in Zn100+Cd100, respectively. In addition, the production of reactive oxygen species in leaves was significantly upregulated in Cd100 compared with the control, and it was downregulated in Zn100. Comparative analyses of transcriptomes and proteomes were conducted with S. nigrum leaves. Differentially expressed genes (DEGs) were involved in Cd uptake, transport, and sequestration, and the upregulation of some transporter genes of Zn transporters (ZIPs), a natural resistance associated macrophage protein (Nramp1), a metal–nicotianamine transporter (YSL2), ATP-binding cassette transporters (ABCs), oligopeptide transporters (OPTs), and metallothionein (MTs) and glutathione S-transferase (GSTs) genes was higher in Zn100+Cd100 than in Cd100. In addition, differentially expressed proteins (DEPs) involved in electron transport chain, ATP, and chlorophyll biosynthesis, such as malate dehydrogenases (MDHs), ATPases, and chlorophyll a/b binding proteins, were mostly upregulated in Zn100. The results indicate that Zn supplement increases Cd accumulation and tolerance in S. nigrum by upregulating ATP-dependent Cd transport and sequestration pathways. Full article
(This article belongs to the Special Issue Physiological Response and Molecular Mechanisms of Plants to Heavy Metal/Loid Toxicity)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Zn (<b>a</b>,<b>b</b>) and Cd (<b>c</b>,<b>d</b>) content in stems, leaves, xylem, and phloem saps of <span class="html-italic">S. nigrum</span>. Plants were exposed to a complete Hoagland solution (CK) or with 100 μmol·L<sup>−1</sup> Zn (Zn100), 100 μmol·L<sup>−1</sup> Cd (Cd100) and 100 μmol·L<sup>−1</sup> Zn+100 μmol·L<sup>−1</sup> Cd (Zn100+Cd100) for 10 days. Values are means ± SE (<span class="html-italic">n</span> = 3) of three different experiments. Means denoted by different letters refer to the significant differences (<span class="html-italic">p</span> &lt; 0.05, Duncan’s test).</p> Full article ">Figure 2
<p>Production of O<sub>2</sub><sup>−</sup> (<b>a</b>,<b>b</b>) and H<sub>2</sub>O<sub>2</sub> (<b>c</b>,<b>d</b>) in leaves of <span class="html-italic">S. nigrum</span> under Zn and Cd treatment. Histochemical location of O<sub>2</sub><sup>−</sup> by NBT staining (<b>a</b>) and H<sub>2</sub>O<sub>2</sub> by DAB staining (<b>c</b>), with bar = 1 cm; O<sub>2</sub><sup>−</sup> producing rate (<b>b</b>) and H<sub>2</sub>O<sub>2</sub> content (<b>d</b>) in leaves of <span class="html-italic">S. nigrum</span>. Samples from the second youngest leaf of plants, which were exposed to a complete Hoagland solution (CK) or with 100 μmol·L<sup>−1</sup> Zn (Zn100), 100 μmol·L<sup>−1</sup> Cd (Cd100), and 100 μmol·L<sup>−1</sup> Zn+100 μmol·L<sup>−1</sup> Cd (Zn100+Cd100) for 10 days. Staining experiments were repeated at least three times, with similar results. Values are means ± SE (<span class="html-italic">n</span> = 3) of three different experiments. Means denoted by different letters refer to the significant differences (<span class="html-italic">p</span> &lt; 0.05, Duncan’s test).</p> Full article ">Figure 3
<p>The numbers of differentially expressed genes (<b>a</b>,<b>b</b>) and differentially expressed proteins (<b>c</b>,<b>d</b>) in leaves of <span class="html-italic">S. nigrum</span> by transcriptome and proteome. Plants were exposed to a complete Hoagland solution (CK) or with 100 μmol·L<sup>−1</sup> Zn (Zn), 100 μmol·L<sup>−1</sup> Cd (Cd), and 100 μmol·L<sup>−1</sup> Zn +100 μmol·L<sup>−1</sup> Cd (ZnCd) for 10 days. Rising green arrow shows increase, and falling red arrow shows decrease in significant differential expression between sample set (Zn vs. CK, Cd vs. CK, and ZnCd vs. CK).</p> Full article ">Figure 4
<p>Identification and gene expression levels of significantly differentially expressed transporters in leaves of <span class="html-italic">S. nigrum</span> by transcriptome. Proportions of the identified transporters (<b>a</b>). Gene expression level of metal transporters (<b>b</b>); ABC transporters (<b>c</b>); peptide transporters (<b>d</b>); nitrate, phosphate, and boron transporters (<b>e</b>); and sulfate and amino acid transporters (<b>f</b>). The boxed transporter genes were then verified by qRT-PCR. Plant was exposed to a complete Hoagland solution (CK) or with 100 μmol·L<sup>−1</sup> Zn (Zn), 100 μmol·L<sup>−1</sup> Cd (Cd), and 100 μmol·L<sup>−1</sup> Zn+100 μmol·L<sup>−1</sup> Cd (ZnCd) for 10 days. Expression levels of transporters shown use Log<sub>2</sub> (fold change) between sample sets (Zn vs. CK, Cd vs. CK, and ZnCd vs. CK). <span class="html-italic">ABC</span> (<span class="html-italic">A</span>, <span class="html-italic">B</span>, <span class="html-italic">C</span>, <span class="html-italic">F</span>, <span class="html-italic">G</span>, <span class="html-italic">I</span>): ATP-biding cassette transporter six subfamilies; <span class="html-italic">Sultr</span>, sulfate transporter; <span class="html-italic">AAT</span>, amino acid transporter; <span class="html-italic">ZIP</span>, zinc transporter; <span class="html-italic">COP</span>, copper transporter; <span class="html-italic">Nramp</span>, natural resistance associated macrophage protein; <span class="html-italic">YSL</span>, metal–nicotianamine transporter; <span class="html-italic">VIT</span>, vacuolar iron transporter; <span class="html-italic">MGT</span>, magnesium transporter; <span class="html-italic">PTR</span>, peptide transporter; <span class="html-italic">OPT</span>, oligopeptide transporter; <span class="html-italic">NRT</span>, nitrate transporter; <span class="html-italic">PNT</span>, peptide/nitrate transporter; <span class="html-italic">BOR</span>, boron transporter; <span class="html-italic">KT</span>, potassium transporter; <span class="html-italic">PHT</span>, phosphate transporter; <span class="html-italic">SWEET</span>, bidirectional sugar transporter.</p> Full article ">Figure 5
<p>Relative gene expression level of transporters in leaves of <span class="html-italic">S. nigrum</span> by qRT-PCR. Plant was exposed to a complete Hoagland solution (CK) or with 100 μmol·L<sup>−1</sup> Zn (Zn100), 100 μmol·L<sup>−1</sup> Cd (Cd100), and 100 μmol·L<sup>−1</sup> Zn+100 μmol·L<sup>−1</sup> Cd (Zn100+Cd100) for 10 days. Relative expression level of genes denoted by different letters refer to the significant differences (<span class="html-italic">p</span> &lt; 0.05, Duncan’s test).</p> Full article ">Figure 6
<p>Expression levels of DEGs and DEPs involved in glutathione (<b>a</b>,<b>b</b>) and malate (<b>c</b>,<b>d</b>) metabolism in leaves of <span class="html-italic">S. nigrum</span> by transcriptome and proteome. The boxes with the same color are the same genes. Plant was exposed to a complete Hoagland solution (CK) or with 100 μmol·L<sup>−1</sup> Zn (Zn), 100 μmol·L<sup>−1</sup> Cd (Cd), and 100 μmol·L<sup>−1</sup> Zn+100 μmol·L<sup>−1</sup> Cd (ZnCd) for 10 days. Expression level of gene by transcriptome was shown using Log<sub>2</sub> (fold change) between sample sets (Zn vs. CK, Cd vs. CK, and ZnCd vs. CK). Expression level of protein by proteome was shown using a fold change (<span class="html-italic">p</span> &lt; 0.05, Student’s <span class="html-italic">t</span>-test) between sample sets (Zn/CK, Cd/CK, and ZnCd/CK). GRK, cysteine-rich receptor-like protein kinase; CysS, cysteine synthase; GPX, glutathione peroxidase; GR, glutathione reductase; GST, glutathione S-transferase; CysP, cysteine proteinase precursor; Lgl, lactoylglutathione lyase; MDH, malate dehydrogenase; DTC, dicarboxylate/tricarboxylate transporter; CS, ATP-citrate synthase.</p> Full article ">Figure 7
<p>Expression levels of DEPs involved in chlorophyll (<b>a</b>) and ATP metabolism (<b>b</b>), chlorophyll content (<b>c</b>), and cytochemical characteristics (<b>d</b>) in leaves of <span class="html-italic">S. nigrum</span>. Plant was exposed to a complete Hoagland solution (CK) or with 100 μmol·L<sup>−1</sup> Zn (Zn), 100 μmol·L<sup>−1</sup> Cd (Cd), and 100 μmol·L<sup>−1</sup> Zn+100 μmol·L<sup>−1</sup> Cd (ZnCd) for 10 days. Expression level of protein was shown using a fold change (<span class="html-italic">p</span> &lt; 0.05, Student’s <span class="html-italic">t</span>-test) between sample sets (Zn/CK, Cd/CK, and ZnCd/CK). Chlorophyll (Chl) contents denoted by different letters refer to the significant differences (<span class="html-italic">p</span> &lt; 0.05, Duncan’s test). Paraffin-section experiments were repeated at least three times with similar results; bar, 20 μm. psbA, photosystem I P700 chlorophyll apoprotein; psbC, photosystem II CP43 chlorophyll apoprotein; RCCR, red chlorophyll catabolite reductase; POR, protochlorophyllide reductase; CAB, chlorophyll <span class="html-italic">a</span>/<span class="html-italic">b</span> binding protein; H<sup>+</sup>-ATPase, plasma membrane H<sup>+</sup>-ATPase; Zmp, ATP-dependent zinc metalloprotease; PFK, ATP-dependent 6-phosphofructokinase; ANT, ADP/ATP translocator; V-ATPase, vacuolar-type ATPase; ClpP, ATP-dependent Clp protease; ASP, ATP sulfurylase; ADRH, ATP-dependent RNA helicase.</p> Full article ">Figure 8
<p>Molecular mechanism involved in transport and accumulation of Cd in leaves of <span class="html-italic">S. nigrum</span> exposed to Zn and Cd. Magenta and green pellets indicate Cd and Zn, respectively; and the genes or proteins in red font represent those upregulated by Cd or Zn in leaves of <span class="html-italic">S. nigrum</span>. Cd or Zn enters into leaf cells by plasma membrane transporters of <span class="html-italic">Nramp1</span>, <span class="html-italic">YSLs</span>, <span class="html-italic">ZIPs</span>, etc.; <span class="html-italic">MTs</span> and <span class="html-italic">GSTs</span> in cells are induced for antioxidant protection or chelation with excess metal ions; and then Cd-GSH complexes are transported to vacuoles for sequestration, or to cell walls for xylem transport by ABCs and OPTs. In addition, Zn promoted electron transport chain (ETC) activities and ATP biosynthesis via increased expression levels of MDHs and ATPases.</p> Full article ">
18 pages, 3778 KiB  
Article
The Physiological Mechanism of Exogenous Melatonin on Improving Seed Germination and the Seedling Growth of Red Clover (Trifolium pretense L.) under Salt Stress
by Rui Liu, Ting Wang, Jiajie Wang, Di Yan, Yijia Lian, Zhengzong Lu, Yue Hong, Xue Yuan, Ye Wang and Runzhi Li
Plants 2024, 13(17), 2527; https://doi.org/10.3390/plants13172527 - 8 Sep 2024
Viewed by 1228
Abstract
Salt stress can affect various physiological processes in plants, ultimately hindering their growth and development. Melatonin (MT) can effectively resist multiple abiotic stresses, improving plant stress resistance. To analyze the mechanism of exogenous MT to enhance salt tolerance in red clover, we conducted [...] Read more.
Salt stress can affect various physiological processes in plants, ultimately hindering their growth and development. Melatonin (MT) can effectively resist multiple abiotic stresses, improving plant stress resistance. To analyze the mechanism of exogenous MT to enhance salt tolerance in red clover, we conducted a comprehensive study to examine the influence of exogenous MT on various parameters, including seed germination indices, seedling morphological traits, and physiological and photosynthetic indicators, using four distinct red clover varieties (H1, H2, H3, and H4). This investigation was performed under various salt stress conditions with differing pH values, specifically utilizing NaCl, Na2SO4, NaHCO3, and Na2CO3 as the salt stressors. The results showed that MT solution immersion significantly improved the germination indicators of red clover seeds under salt stress. The foliar spraying of 50 μM and 25 μM MT solution significantly increased SOD activity (21–127%), POD activity, soluble sugar content, proline content (22–117%), chlorophyll content (2–66%), and the net photosynthetic rate. It reduced the MDA content (14–55%) and intercellular CO2 concentration of red clover seedlings under salt stress. Gray correlation analysis and the Mantel test further verified that MT is a key factor in enhancing seed germination and seedling growth of red clover under salt stress; the most significant improvement was observed for NaHCO3 stress. MT is demonstrated to improve the salt tolerance of red clover through a variety of mechanisms, including an increase in antioxidant enzyme activity, osmoregulation ability, and cell membrane stability. Additionally, it improves photosynthetic efficiency and plant architecture, promoting energy production, growth, and optimal resource allocation. These mechanisms function synergistically, enabling red clover to sustain normal growth and development under salt stress. Full article
(This article belongs to the Special Issue Mitigation Strategies and Tolerance of Plants to Abiotic Stresses—2nd Edition)
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<p>Effects of exogenous MT on plant height of red clover seedlings under different salt stresses. The error bar in the figure represents the standard deviation (SD, n = 3). Different letters indicate significant differences by Tukey’s test (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 2
<p>The effect of exogenous MT on osmotic substances of red clover seedlings under different types of salt stress: (<b>A</b>) changes in soluble sugar content under different treatments; (<b>B</b>) changes in proline content under different treatments; (<b>C</b>) changes in MDA content under different treatments. The error bar in the figure represents the standard deviation (SD, n = 3). Different letters indicate significant differences by Tukey’s test (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 2 Cont.
<p>The effect of exogenous MT on osmotic substances of red clover seedlings under different types of salt stress: (<b>A</b>) changes in soluble sugar content under different treatments; (<b>B</b>) changes in proline content under different treatments; (<b>C</b>) changes in MDA content under different treatments. The error bar in the figure represents the standard deviation (SD, n = 3). Different letters indicate significant differences by Tukey’s test (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 3
<p>The effect of exogenous MT on antioxidant enzyme activity of red clover seedlings under different types of salt stresses: (<b>A</b>) changes in SOD activity under different treatments; (<b>B</b>) changes in POD activity under different treatments. The error bar in the figure represents the standard deviation (SD, n = 3). Different letters indicate significant differences by Tukey’s test (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 4
<p>The effect of exogenous MT on photosynthetic characteristics of red clover seedlings under different types of salt stress: (<b>A</b>) changes in chlorophyll content under different treatments; (<b>B</b>) changes in intercellular CO<sub>2</sub> concentration under different treatments. The error bar in the figure represents the standard deviation (SD, n = 3). Different letters indicate significant differences by Tukey’s test (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 5
<p>A comprehensive evaluation of MT effects on red clover seedlings under salt stress: (<b>A</b>) gray correlation coefficients of 20 indicators under four types of salt stress in red clover; (<b>B</b>) Mantel test correlation heatmap for intragroup and intergroup correlation analyses. Purple lines indicate a significant positive correlation, and green lines indicate a significant negative correlation.</p> Full article ">Figure 6
<p>The response mechanism of exogenous MT in regulating seed germination and seedling growth in red clover under salt stress. Red arrows indicate increases, and green arrows indicate decreases after MT treatment.</p> Full article ">
16 pages, 326 KiB  
Review
Microplastic and Nanoplastic in Crops: Possible Adverse Effects to Crop Production and Contaminant Transfer in the Food Chain
by Bhakti Jadhav and Agnieszka Medyńska-Juraszek
Plants 2024, 13(17), 2526; https://doi.org/10.3390/plants13172526 - 8 Sep 2024
Viewed by 2255
Abstract
With the increasing amounts of microplastic (MP) deposited in soil from various agricultural activities, crop plants can become an important source of MP in food products. The last three years of studies gave enough evidence showing that plastic in the form of nanoparticles [...] Read more.
With the increasing amounts of microplastic (MP) deposited in soil from various agricultural activities, crop plants can become an important source of MP in food products. The last three years of studies gave enough evidence showing that plastic in the form of nanoparticles (<100 nm) can be taken up by the root system and transferred to aboveground plant parts. Furthermore, the presence of microplastic in soil affects plant growth disturbing metabolic processes in plants, thus reducing yields and crop quality. Some of the adverse effects of microplastic on plants have been already described in the meta-analysis; however, this review provides a comprehensive overview of the latest findings about possible adverse effects and risks related to wide microplastic occurrence in soil on crop production safety, including topics related to changes of pesticides behavior and plant pathogen spreading under the presence MP and possibly threaten to human health. Full article
(This article belongs to the Special Issue Advances in Soil Fertility Management for Sustainable Crop Production)
14 pages, 2244 KiB  
Article
Soybean Yield Simulation and Sustainability Assessment Based on the DSSAT-CROPGRO-Soybean Model
by Lei Zhang, Zhenxi Cao, Yang Gao, Weixiong Huang, Zhuanyun Si, Yuanhang Guo, Hongbo Wang and Xingpeng Wang
Plants 2024, 13(17), 2525; https://doi.org/10.3390/plants13172525 - 8 Sep 2024
Viewed by 1092
Abstract
In order to ensure national grain and oil security, it is imperative to expand the soybean planting area in the Xinjiang region. However, the scarcity of water resources in southern Xinjiang, the relatively backward soybean planting technology, and the lack of a supporting [...] Read more.
In order to ensure national grain and oil security, it is imperative to expand the soybean planting area in the Xinjiang region. However, the scarcity of water resources in southern Xinjiang, the relatively backward soybean planting technology, and the lack of a supporting irrigation system have negatively impacted soybean planting and yield. In 2022 and 2023, we conducted an experiment which included three irrigation amounts of 27 mm, 36 mm, and 45 mm and analyzed the changes in dry mass and yield. Additionally, we simulated the potential yield using the corrected DSSAT-CROPGRO-Soybean model and biomass based on the meteorological data from 1994 to 2023. The results demonstrated that the model was capable of accurately predicting soybean emergence (the relative root mean square error (nRMSE) = 0, the absolute relative error (ARE) = 0), flowering (nRMSE = 0, ARE = 2.78%), maturity (nRMSE = 0, ARE = 3.21%). The model demonstrated high levels of accuracy in predicting soybean biomass (R2 = 0.98, nRMSE = 20.50%, ARE = 20.63%), 0–80 cm soil water storage (R2 = 0.64, nRMSE = 7.78%, ARE = 3.24%), and yield (R2 = 0.81, nRMSE = 10.83%, ARE = 8.79%). The biomass of soybean plants increases with the increase in irrigation amount. The highest biomass of 63 mm is 9379.19 kg·hm−2. When the irrigation yield is 36–45 mm (p < 0.05), the maximum yield can reach 4984.73 kg·hm−2; the maximum efficiency of soybean irrigation water was 33–36 mm. In light of the impact of soybean yield and irrigation water use efficiency, the optimal irrigation amount for soybean cultivation in southern Xinjiang is estimated to be between 36 and 42 mm. The simulation results provide a theoretical foundation for soybean cultivation in southern Xinjiang. Full article
(This article belongs to the Special Issue Strategies to Improve Water-Use Efficiency in Plant Production)
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<p>Simulated vs. measured biomass in 2022 and 2023. Note: (<b>a</b>,<b>c</b>,<b>e</b>) refer to the years 2022. (<b>b</b>,<b>d</b>,<b>f</b>) refer to the years 2023.</p> Full article ">Figure 2
<p>Simulated values of average soybean yield, average biomass, and irrigation water use efficiency from 1994 to 2023. (<b>a</b>) soybean above biomass. (<b>b</b>) soybean yield and irrigation water use efficiency. Note: T1~T14 represent the irrigation amounts of 24 to 63 mm, respectively. The lowercase letters indicate the difference in significance among treatments at the 0.05 level. The short line represents the standard deviation.</p> Full article ">Figure 3
<p>Soybean yield simulation, yield coefficients of variation, and sustainability indices from 1994 to 2023. Note: T1~T14 represent the irrigation amounts of 24 to 63 mm, respectively.</p> Full article ">Figure 4
<p>Study area.</p> Full article ">Figure 5
<p>Soybean planting pattern map.</p> Full article ">
19 pages, 1945 KiB  
Article
Aminoethoxyvinylglicine and 1-Methylcyclopropene: Effects on Preharvest Drop, Fruit Maturity, Quality, and Associated Gene Expression of ‘Honeycrisp’ Apples in the US Mid-Atlantic
by Emily Johnson and Macarena Farcuh
Plants 2024, 13(17), 2524; https://doi.org/10.3390/plants13172524 - 8 Sep 2024
Viewed by 766
Abstract
Preharvest fruit drop is one of the main challenges in apple production as it can lead to extensive crop losses in commercially important cultivars including ‘Honeycrisp’. Plant growth regulators, such as aminoethoxyvinylglicine (AVG) and 1-methylcyclopropene (1-MCP), which hinder ethylene biosynthesis and perception, respectively, [...] Read more.
Preharvest fruit drop is one of the main challenges in apple production as it can lead to extensive crop losses in commercially important cultivars including ‘Honeycrisp’. Plant growth regulators, such as aminoethoxyvinylglicine (AVG) and 1-methylcyclopropene (1-MCP), which hinder ethylene biosynthesis and perception, respectively, can control preharvest fruit drop, but an assessment of their effects in ‘Honeycrisp’ fruit grown under US mid-Atlantic conditions is lacking. In this study, we evaluated the effects of AVG (130 mg a.i. L−1) and 1-MCP (150 mg a.i. L−1) on preharvest fruit drop, ethylene production, fruit physicochemical parameters, skin color, and transcript accumulation of ethylene and anthocyanin-related genes in ‘Honeycrisp’ apples throughout on-the-tree ripening. We showed that both AVG and 1-MCP significantly minimized preharvest fruit drop with respect to the control fruit. Additionally, AVG was the most effective in decreasing ethylene production, downregulating ethylene biosynthesis and perception-related gene expression, and delaying fruit maturity. Nevertheless, AVG negatively impacted apple red skin color and exhibited the lowest expression of anthocyanin-biosynthesis-related genes, only allowing apples to reach the minimum required 50% blush at the last ripening stage. Conversely, 1-MCP-treated fruit displayed an intermediate behavior between AVG-treated and control fruit, decreasing ethylene production rates and the associated gene expression as well as delaying fruit maturity when compared to the control fruit. Remarkably, 1-MCP treatment did not sacrifice red skin color development or anthocyanin-biosynthesis-related gene expression, thus exhibiting > 50% blush one week earlier than AVG. Full article
(This article belongs to the Special Issue Horticultural Plant Cultivation and Fruit Quality Enhancement)
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<p>AVG and 1-MCP effects on ‘Honeycrisp’ preharvest fruit drop in Aspers, PA. Fruit drop was measured 1 week before commercial harvest (1WBCH), at commercial harvest (CH), 1 week after CH (CH + 1W), and 2 weeks after CH (CH + 2W). Values are means ± standard error. Different letters indicate significant differences (<span class="html-italic">p</span> ≤ 0.05) according to Tukey’s HSD test.</p> Full article ">Figure 2
<p>AVG and 1-MCP impacts on the relative expression of ethylene-biosynthesis-related genes of ‘Honeycrisp’ apples throughout ripening on-the-tree in Aspers, PA. (<b>A</b>) <span class="html-italic">MdACS1</span>, (<b>B</b>) <span class="html-italic">MdACO1</span>. Apples were assessed at commercial harvest (CH), 1 week after CH (CH + 1W), and 2 weeks after commercial harvest (CH + 2W). Values are means ± standard error. Different letters indicate significant differences (<span class="html-italic">p</span> ≤ 0.05) according to Tukey’s HSD test. 1-aminocyclopropane-carboxylase synthase (ACS), 1-aminocyclopropane-carboxylase oxidase (ACO).</p> Full article ">Figure 3
<p>AVG and 1-MCP impacts on the relative expression of ethylene-perception-related genes of ‘Honeycrisp’ apples throughout ripening on-the-tree in Aspers, PA. (<b>A</b>) <span class="html-italic">MdERS1</span>, (<b>B</b>) <span class="html-italic">MdERS2</span>, (<b>C</b>) <span class="html-italic">MdETR1</span>, (<b>D</b>) <span class="html-italic">MdETR2</span>, (<b>E</b>) <span class="html-italic">MdETR5</span>, (<b>F</b>) <span class="html-italic">MdCTR1</span>. Apples were assessed at commercial harvest (CH), 1 week after CH (CH + 1W), and 2 weeks after commercial harvest (CH + 2W). Values are means ± standard error. Different letters indicate significant differences (<span class="html-italic">p</span> ≤ 0.05) according to Tukey’s HSD test. Ethylene-response sensor (ERS), ethylene receptor-type (ETR), constitutive triple response (CTR).</p> Full article ">Figure 4
<p>AVG and 1-MCP impacts on the relative expression of anthocyanin-biosynthesis-related genes of ‘Honeycrisp’ apples throughout ripening on-the-tree in Aspers, PA. (<b>A</b>) <span class="html-italic">MdPAL</span>, (<b>B</b>) <span class="html-italic">MdCHS</span>, (<b>C</b>) <span class="html-italic">MdCHI</span>, (<b>D</b>) <span class="html-italic">MdF3H</span>, (<b>E</b>) <span class="html-italic">MdDFR</span>, (<b>F</b>) <span class="html-italic">MdLOX</span>, (<b>G</b>) <span class="html-italic">MdUFGT</span>. Apples were assessed at commercial harvest (CH), 1 week after CH (CH + 1W), and 2 weeks after commercial harvest (CH + 2W). Values are means ± standard error. Different letters indicate significant differences (<span class="html-italic">p</span> ≤ 0.05) according to Tukey’s HSD test. Phenylalanine ammonia-lyase (PAL), chalcone synthase (CHS), chalcone isomerase (CHI), flavanone 3-hydroxylase (F3H), dihydroflavonol 4-reductase (DFR), leucoanthocyanidin dioxygenase (LDOX), UDP glucose-flavonoid 3-O-glucosyltransferase (UFGT).</p> Full article ">Figure 5
<p>Principal component analysis of data acquired from fruit drop, ethylene production, physicochemical properties, skin color, expression of ethylene-biosynthesis-related and perception-related genes, as well as anthocyanin-biosynthesis-related genes of ‘Honeycrisp’ apples subjected to AVG and 1-MCP treatments and evaluated throughout ripening on-the-tree. Numbers correspond to the different treatments and evaluation periods/ripening stages that were examined (1 (AVG_CH), 2 (AVG_CH + 1W), 3 (AVG_CH + 2W), 4 (1-MCP_CH), 5 (1-MCP_CH + 1W), 6 (1-MCP_CH + 2W), 7 (Control_CH), 8 (Control_CH + 1W), 9 (Control_CH + 2W)). Starch pattern index (SPI), soluble solids content (SSC), titratable acidity (TA), index of absorbance difference (I<sub>AD</sub>). Codes for genes are defined in <a href="#plants-13-02524-f002" class="html-fig">Figure 2</a>, <a href="#plants-13-02524-f003" class="html-fig">Figure 3</a> and <a href="#plants-13-02524-f004" class="html-fig">Figure 4</a>.</p> Full article ">
20 pages, 1765 KiB  
Review
Advances in Plant Auxin Biology: Synthesis, Metabolism, Signaling, Interaction with Other Hormones, and Roles under Abiotic Stress
by Jianshuang Gao, Shunyao Zhuang and Weiwei Zhang
Plants 2024, 13(17), 2523; https://doi.org/10.3390/plants13172523 - 8 Sep 2024
Viewed by 4891
Abstract
Auxin is a key hormone that regulates plant growth and development, including plant shape and sensitivity to environmental changes. Auxin is biosynthesized and metabolized via many parallel pathways, and it is sensed and transduced by both normal and atypical pathways. The production, catabolism, [...] Read more.
Auxin is a key hormone that regulates plant growth and development, including plant shape and sensitivity to environmental changes. Auxin is biosynthesized and metabolized via many parallel pathways, and it is sensed and transduced by both normal and atypical pathways. The production, catabolism, and signal transduction pathways of auxin primarily govern its role in plant growth and development, and in the response to stress. Recent research has discovered that auxin not only responds to intrinsic developmental signals, but also mediates various environmental signals (e.g., drought, heavy metals, and temperature stresses) and interacts with hormones such as cytokinin, abscisic acid, gibberellin, and ethylene, all of which are involved in the regulation of plant growth and development, as well as the maintenance of homeostatic equilibrium in plant cells. In this review, we discuss the latest research on auxin types, biosynthesis and metabolism, polar transport, signaling pathways, and interactions with other hormones. We also summarize the important role of auxin in plants under abiotic stresses. These discussions provide new perspectives to understand the molecular mechanisms of auxin’s functions in plant development. Full article
(This article belongs to the Special Issue Advances in Plant Auxin Biology)
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<p>Schematic presentation of auxin function in plants.</p> Full article ">Figure 2
<p>Examples of endogenous auxins (<b>A</b>) and some synthetic auxins (<b>B</b>) are presented. (<b>A</b>) IAA: indole-acetic acid; IBA: indole-3-butyric acid; 4-Cl-IAA: 4-chloroindole-3-acetic acid; and PAA: phenyl-acetic acid. (<b>B</b>) 1-NAA: 1-Naphthalene-acetic acid; 2,4-D: 2,4-dichlorophenoxyacetic acid; 2,4,5-T: 2,4,5-trichlorophenoxy-acetic acid; dicamba: 3,6-dichloro-2-methoxybenzoic acid, and picloram: 4-Amino-3,5,6-trichloropicolinic acid [<a href="#B48-plants-13-02523" class="html-bibr">48</a>].</p> Full article ">Figure 3
<p>A model of the tryptophan (Trp)-dependent and Trp-independent indole acetic acid (IAA) biosynthetic pathways. IGP, indole-3-glycerol phosphate; INS, indole synthase gene; TAA1, tryptophan aminotransferase; TARs, TAA1-associated proteins; IPyA, indole-3-pyruvate; YUC, YUCCA; IAOx, indole-3-acetaldoxime; IAM, indole-3-acetamide; CYP79B2 and CYP79B3 in the cellular phosphorus P450 (CYP) mono-oxygenase family.</p> Full article ">Figure 4
<p>The auxin signaling transduction pathway in plants. Under low auxin concentration conditions, the auxin transduction repressor auxin/indole-acetic acid protein (Aux/IAA) forms a heterodimer with the auxin response factor (ARF), which inhibits the transcriptional activity of ARF, resulting in the suppression of auxin response gene expression. Under high auxin concentration, the auxin receptor transport inhibitor response 1 (TIR1) binds to Aux/IAA, ubiquitinates and degrades AUX/IAA by the action of the 26S proteasome, and ARF is released, activating the expression of auxin-responsive genes.</p> Full article ">
16 pages, 7340 KiB  
Article
Characteristics of Phenotypic Variation of Malus Pollen at Infrageneric Scale
by Junjun Fan, Yun Wang, Zhenping Hao, Ye Peng, Jingze Ma, Wangxiang Zhang, Mingming Zhao and Xueming Zai
Plants 2024, 13(17), 2522; https://doi.org/10.3390/plants13172522 - 8 Sep 2024
Viewed by 679
Abstract
Pollen carries extensive genetic information, which may provide clues regarding the kinship of Malus, whose genetic relationships are complex. In this study, the phenotypic variation of pollen from 107 Malus taxa was investigated using combined methods of intraspecific/interspecific uniformity testing, cluster analysis, and [...] Read more.
Pollen carries extensive genetic information, which may provide clues regarding the kinship of Malus, whose genetic relationships are complex. In this study, the phenotypic variation of pollen from 107 Malus taxa was investigated using combined methods of intraspecific/interspecific uniformity testing, cluster analysis, and Pearson correlation analysis. The family aggregation distributions in Malus sections, species, and cultivars were analyzed to infer their pedigree relationships. The results showed that (1) compared with pollen size and morphology, aberrant pollen rate and ornamentation were highly interspecifically differentiated, but ornamentation was also intraspecifically unstable, especially perforation densities (c.v.¯ > 15%). (2) The pollen alteration direction from the original to the evolutionary population of Malus was large to small, with elliptic to rectangular morphologies, large and compact to small and sparse ridges, and low to high perforation densities. However, there was no significant change in pollen size. (3) The 107 studied taxa were divided into four groups. Malus species were relatively clustered in the same section, while homologous cultivars showed evidence of family aggregation distribution characteristics (92.60% of cultivars were clustered with their parents). (4) M. baccata and M. pumilar var. neidzwetzkyana were high-frequency parents, participating in 38.7% and 20.7% of cross-breeding, respectively. Overall, this study provides a reference for identifying Malus’ pedigree relationship. Full article
(This article belongs to the Section Horticultural Science and Ornamental Plants)
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<p>The box plots and coefficient of variation of pollen morphology traits for <span class="html-italic">Malus</span> taxa. (<b>a</b>) The box plot of the percentage of aberrant pollen grains. (<b>b</b>) The box plot of the relative pollen size. Indicators contain the length of the polar axis (P), equatorial diameter (E), and area of equatorial view with two colpi (S). (<b>c</b>) In the box plot of pollen morphology, indicators include P/E, P/E′, and E′/E. E′ indicates the diameter at the equatorial plane halfway between the equator and pole. (<b>d</b>) The box plot of pollen ornamentation. The indicators measured were the ridge width (RW), furrow width (FW), and perforation density (PD). The interquartile range (IQR) is the box plot (box body) showing the middle 50% of observation values and can be calculated by subtracting the lower quartile (Q1) from the upper quartile (Q3). An outlier (★) is an observation value numerically distant from the rest of the data, 1.5 times the interquartile range, less than Q1 and greater than Q3. The min (—) and max (—) values (excluding outliers) in the box plot are assigned the values of Q1 − 1.5 × IQR and Q3 + 1.5 × IQR, respectively. The mean values are presented in small squares (■) inside the box bodies. Datasets ● of <span class="html-italic">Malus</span> taxa are shown on the right of each box plot, and their distributions are fitted with a line. (<b>e</b>) The intraspecific uniformity of pollen morphology traits using 15% as the criteria. If <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi>c</mi> <mo>.</mo> <mi>v</mi> <mo>.</mo> </mrow> <mo stretchy="true">¯</mo> </mover> </mrow> </semantics></math> ≤ 15%, then the trait has met the uniformity requirements. (<b>f</b>) The interspecific distinctness of pollen morphology traits using 15% as the criteria. If <span class="html-italic">c.v</span>. ≥ 15%, the differentiation degree of this trait is considered to be high among all the taxa. The one-way ANOVA (Tukey’s method) was performed to obtain a more accurate expression. * indicates significant differences (<span class="html-italic">p</span> &lt; 0.05) in pollen morphology traits between <span class="html-italic">Malus</span> taxa.</p> Full article ">Figure 2
<p>The cluster analysis of the pollen’s phenotypic traits in 107 <span class="html-italic">Malus</span> taxa. The scientific names of the species are noted in different colors. The same color font indicates they are in the same section [<a href="#B17-plants-13-02522" class="html-bibr">17</a>]. The red font represents the species belonging to Sect. <span class="html-italic">Docyniopsis</span>. The green font represents the species belonging to Sect. <span class="html-italic">Chloromeles</span>. The blue font represents the species belonging to Sect. <span class="html-italic">Sorbomalus</span>. The purple font represents the species belonging to Sect. <span class="html-italic">Baccatus</span>. The brown font represents the species belonging to Sect. <span class="html-italic">Malus</span>. A fully filled circle indicates that these cultivars are grouped with their parents, and a half-filled circle indicates a cultivar that is not grouped with its parent. Parental traceability information for each cultivar is available in the study by Zhou et al. [<a href="#B20-plants-13-02522" class="html-bibr">20</a>].</p> Full article ">Figure 3
<p>The distribution of <span class="html-italic">Malus</span> sections in each cluster group and their Pearson correlation with pollen phenotypic traits. (<b>a</b>) The distribution of <span class="html-italic">Malus</span> species of <span class="html-italic">Malus</span> sections in each cluster group. (<b>b</b>) Pearson correlation analysis between <span class="html-italic">Malus</span> classification units and pollen phenotypic traits. According to the order from original to evolved in the classic taxonomy system, the five sections of 23 <span class="html-italic">Malus</span> species were assigned the following values: Sect. <span class="html-italic">Docyniopsis</span> (1) → Sect. <span class="html-italic">Chloromeles</span> (2) → Sect. <span class="html-italic">Sorbomalus</span> (3) → Sect. <span class="html-italic">Baccatus</span> (4) → Sect. <span class="html-italic">Malus</span> (5). S vs. C represents species and cultivars populations, valued to species (1) and cultivars (2) in Pearson correlation analysis. The circle marked with ‘*’ indicates that the correlation reached a significant level (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 4
<p>The frequency statistics and correlation analysis of the <span class="html-italic">Malus</span> species involved in hybridization. (<b>a</b>) The frequency of hybridization and family aggregation of the 18 species based on the literature and this study (12 bolded species). (<b>b</b>) The correlation of the hybridization frequency between the species in this study and the literature.</p> Full article ">Figure 5
<p>A schematic diagram illustrating the directional variation at the microscopic level of pollen using <span class="html-italic">M. ioensis</span> and its naturally pollinated progeny as a case.</p> Full article ">Figure 6
<p>The pollen morphology of <span class="html-italic">Malus</span> ‘Amey’ obtained via SEM observation. (<b>a</b>) The population; (<b>b</b>) The polar view; (<b>c</b>) Two colpi in the equatorial view; (<b>d</b>) One colpi in the equatorial view; (<b>e</b>) Ornamentation in the polar view; (<b>f</b>) Ornamentation in the equatorial view. The indicators contain the abnormal pollen rate (AP), the length of the polar axis (P), equatorial diameter (E), the diameter at the equatorial plane halfway between the equator and pole (E′), the ridge width (RW), the furrow width (FW), and the perforation density (PD). The area of the equatorial view with two colpi (S) serves as a relative metric computed by Image J [<a href="#B44-plants-13-02522" class="html-bibr">44</a>] to estimate the region encompassed within the equatorial plane.</p> Full article ">
11 pages, 1784 KiB  
Article
GL5.2, a Quantitative Trait Locus for Rice Grain Shape, Encodes a RING-Type E3 Ubiquitin Ligase
by Hui Zhang, De-Run Huang, Yi Shen, Xiao-Jun Niu, Ye-Yang Fan, Zhen-Hua Zhang, Jie-Yun Zhuang and Yu-Jun Zhu
Plants 2024, 13(17), 2521; https://doi.org/10.3390/plants13172521 - 8 Sep 2024
Cited by 1 | Viewed by 721
Abstract
Grain weight and grain shape are important traits that determine rice grain yield and quality. Mining more quantitative trait loci (QTLs) that control grain weight and shape will help to further improve the molecular regulatory network of rice grain development and provide gene [...] Read more.
Grain weight and grain shape are important traits that determine rice grain yield and quality. Mining more quantitative trait loci (QTLs) that control grain weight and shape will help to further improve the molecular regulatory network of rice grain development and provide gene resources for high-yield and high-quality rice varieties. In the present study, a QTL for grain length (GL) and grain width (GW), qGL5.2, was firstly fine-mapped into a 21.4 kb region using two sets of near-isogenic lines (NILs) derived from the indica rice cross Teqing (TQ) and IRBB52. In the NIL populations, the GL and ratio of grain length to grain width (RLW) of the IRBB52 homozygous lines increased by 0.16–0.20% and 0.27–0.39% compared with the TQ homozygous lines, but GW decreased by 0.19–0.75%. Then, by analyzing the grain weight and grain shape of the knock-out mutant, it was determined that the annotation gene Os05g0551000 encoded a RING-type E3 ubiquitin ligase, which was the cause gene of qGL5.2. The results show that GL and RLW increased by 2.44–5.48% and 4.19–10.70%, but GW decreased by 1.69–4.70% compared with the recipient. Based on the parental sequence analysis and haplotype analysis, one InDel variation located at −1489 in the promoter region was likely to be the functional site of qGL5.2. In addition, we also found that the Hap 5 (IRBB52-type) increased significantly in grain length and grain weight compared with other haplotypes, indicating that the Hap 5 can potentially be used in rice breeding to improve grain yield and quality. Full article
(This article belongs to the Section Plant Molecular Biology)
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<p>Development of the rice populations used in this study. NIL, near-isogenic line.</p> Full article ">Figure 2
<p>Distributions of 1000 grain weight, grain length, grain width, and ratio of grain length to grain width in two near-isogenic line populations. (<b>A</b>–<b>D</b>) FW1; (<b>E</b>–<b>H</b>) FW2. TGW, 1000 grain weight; GL, grain length; GW, grain width; RLW, ratio of grain length to grain width; NIL-TQ and NIL-IRBB52 are near-isogenic lines having Teqing and IRBB52 homozygous genotypes in segregating region, respectively.</p> Full article ">Figure 3
<p>Sequence and phenotypic variation between the knock-out mutants and the transgenic-negative. (<b>A</b>) Variations of the DNA sequences in the target region. The protospacer adjacent motif site is shown in blue. Insertion is indicated by lowercase letter in red. Deletion is indicated by a hyphen in red; (<b>B</b>) Grain length of the CK and knock-out mutants; (<b>C</b>) Grain width of the CK and knock-out mutants. CK, transgenic-negative.</p> Full article ">Figure 4
<p>Phenotypic differences among different groups of <span class="html-italic">GL5.2</span>. (<b>A</b>–<b>D</b>), Phenotypic differences among six haplotypes of <span class="html-italic">GL5.2</span>. Values are given as the mean ± SD (n = 135 for Hap1, n = 116 for Hap2, n = 103 for Hap3, n = 36 for Hap4, n = 12 for Hap5, n = 11 for Hap6). Values with different letters are significantly different at <span class="html-italic">p</span> &lt; 0.05 based on Duncan’s multiple range test; (<b>E</b>–<b>H</b>), Phenotypic differences between IRBB52-type and TQ-type of <span class="html-italic">GL5.2</span>. Values are given as the mean ± SD (n = 441 for IRBB52-type; n = 27 for Teqing-type). **, <span class="html-italic">p</span> &lt; 0.01; ***, <span class="html-italic">p</span> &lt; 0.001; ns: not significant.</p> Full article ">
20 pages, 1812 KiB  
Article
An Extended Application of the Fast Multi-Locus Ridge Regression Algorithm in Genome-Wide Association Studies of Categorical Phenotypes
by Jin Zhang, Bolin Shen, Ziyang Zhou, Mingzhi Cai, Xinyi Wu, Le Han and Yangjun Wen
Plants 2024, 13(17), 2520; https://doi.org/10.3390/plants13172520 - 7 Sep 2024
Viewed by 937
Abstract
Categorical (either binary or ordinal) quantitative traits are widely observed to measure count and resistance in plants. Unlike continuous traits, categorical traits often provide less detailed insights into genetic variation and possess a more complex underlying genetic architecture, which presents additional challenges for [...] Read more.
Categorical (either binary or ordinal) quantitative traits are widely observed to measure count and resistance in plants. Unlike continuous traits, categorical traits often provide less detailed insights into genetic variation and possess a more complex underlying genetic architecture, which presents additional challenges for their genome-wide association studies. Meanwhile, methods designed for binary or continuous phenotypes are commonly used to inappropriately analyze ordinal traits, which leads to the loss of original phenotype information and the detection power of quantitative trait nucleotides (QTN). To address these issues, fast multi-locus ridge regression (FastRR), which was originally designed for continuous traits, is used to directly analyze binary or ordinal traits in this study. FastRR includes three stages of continuous transformation, variable reduction, and parameter estimation, and it can computationally handle categorical phenotype data instead of link functions introduced or methods inappropriately used. A series of simulation studies demonstrate that, compared with four other continuous or binary or ordinal approaches, including logistic regression, FarmCPU, FaST-LMM, and POLMM, the FastRR method outperforms in the detection of small-effect QTN, accuracy of estimated effect, and computation speed. We applied FastRR to 14 binary or ordinal phenotypes in the Arabidopsis real dataset and identified 479 significant loci and 76 known genes, at least seven times as many as detected by other algorithms. These findings underscore the potential of FastRR as a very useful tool for genome-wide association studies and novel gene mining of binary and ordinal traits. Full article
(This article belongs to the Special Issue Genome-Wide Association Study Accelerates Deciphering of Crop Complex Agronomic Traits)
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<p>A flow chart of the FastRR method.</p> Full article ">Figure 2
<p>The phenotypic distribution of fourteen binary or ordinal traits in the <span class="html-italic">Arabidopsis</span> real dataset. (<b>A</b>–<b>J</b>) for ten binary traits (avrPphB, avrRpm1, avrRpt2, avrB, Anthocyanin 10, 16, and 22, Leaf roll 10, 16, and 22); (<b>K</b>–<b>N</b>) for four ordinal traits (Leaf serr 10, 16, 22, and Silique 22).</p> Full article ">Figure 3
<p>The statistical power for QTN detected by five methods in the first simulation experiment under (<b>A</b>) a normal distribution with 5 hierarchical levels, (<b>B</b>) a uniform distribution with 5 hierarchical levels, and (<b>C</b>) a binomial distribution with 2 hierarchical levels.</p> Full article ">Figure 4
<p>ROC curves for the five methods of the first simulation experiment. From top to bottom, each row represents 2 (<b>A</b>–<b>C</b>), 5 (<b>D</b>–<b>F</b>), and 10 (<b>G</b>–<b>I</b>) times the polygenic background, respectively.</p> Full article ">Figure 5
<p>The average computing time using five methods in three simulation experiments. From top to bottom, each row represents the first (<b>A</b>–<b>C</b>), second (<b>D</b>–<b>F</b>), and third (<b>G</b>–<b>I</b>) simulation experiment, respectively.</p> Full article ">Figure 6
<p>A heatmap of known genes identified by five methods for fourteen binary or ordinal traits in the <span class="html-italic">Arabidopsis</span> real dataset.</p> Full article ">
16 pages, 3740 KiB  
Article
Quantification of Airborne Particulate Matter and Trace Element Deposition on Hedera helix and Senecio cineraria Leaves
by Anabel Saran, Mariano Javier Mendez, Diego Gabriel Much, Valeria Imperato, Sofie Thijs, Jaco Vangronsveld and Luciano Jose Merini
Plants 2024, 13(17), 2519; https://doi.org/10.3390/plants13172519 - 7 Sep 2024
Viewed by 896
Abstract
In both developed and developing countries, atmospheric pollution with particulate matter (PM) remains an important issue. Despite the health effects of poor air quality, studies on air pollution are often limited by the high costs of continuous monitoring and the need for extensive [...] Read more.
In both developed and developing countries, atmospheric pollution with particulate matter (PM) remains an important issue. Despite the health effects of poor air quality, studies on air pollution are often limited by the high costs of continuous monitoring and the need for extensive sampling. Furthermore, these particles are often enriched with potentially toxic trace elements and organic pollutants. This study evaluates both the composition of atmospheric dust accumulated during a certain timespan on Hedera helix and Senecio cineraria leaves and the potential for their use as bio-monitors. The test plants were positioned near automatic air quality monitoring stations at four different sites with respectively high, moderate and low traffic intensity. The gravimetric deposition of PM10 and PM2.5 on leaves was compared with data recorded by the monitoring stations and related to the weather conditions reported by Argentina’s National Meteorological Service. To determine the presence of trace elements enriching the PM deposited on leaves, two analytical techniques were applied: XRF (not destructive) and ICP (destructive). The results indicated that only in the unpaved street location (site 2) did PM10 and PM2.5 concentrations (90 µg m−3 and 9 µg m−3) in the air exceed more than five times WHO guidelines (15 µg m−3 and 5 µg m−3). However, several trace elements were found to be enriching PM deposited on leaves from all sites. Predominantly, increased concentrations of Cd, Cu, Ti, Mn, Zn and Fe were found, which were associated with construction, traffic and unpaved street sources. Furthermore, based on its capability to sequester above 2800 µg cm−2 of PM10, 2450 µg cm−2 of PM2.5 and trace elements, Senecio cineraria can be taken into consideration for adoption as a bio-monitor or even for PM mitigation. Full article
(This article belongs to the Section Plant Ecology)
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<p>Measurements from an automatic weather station of the National Weather Service of Argentina at Santa Rosa Aero station. Humidity (orange line), wind speed (grey bars), temperature (red line) and precipitation (blue bars) recorded between 15 September 2021 and 15 March 2022.</p> Full article ">Figure 2
<p>Average concentrations of PM 10 and PM 2.5 recorded monthly at each site (n = 5). The pink line represents the WHO recommended annual limit (PM10 = 15 µg m<sup>−3</sup>; PM2.5 = 5 µg m<sup>−3</sup>).</p> Full article ">Figure 3
<p>Spearman correlation matrix, pairwise relationships between meteorological variables and PM concentrations recorded by monitors located at the four sites. Circle sizes dynamically adjust based on the magnitude of correlation, and the color gradient indicates the strength and direction of correlations, from negative (red) to positive (blue).</p> Full article ">Figure 4
<p>Means (n = 5) of XRF spectra of <span class="html-italic">Hedera helix</span> (H) leaves collected from (<b>a</b>) site 1, (<b>b</b>) site 2, (<b>c</b>) site 3 and (<b>d</b>) site 4. Leaves were analyzed before (0 m) and after 3 and 6 months of exposure (3 m and 6 m). The KeV of the peaks shows which elements are present, and the height of a peak indicates the abundance of that element.</p> Full article ">Figure 5
<p>Means (n = 5) of XRF spectra of <span class="html-italic">Senecio cineraria</span> (C) leaves originating from (<b>a</b>) site 1, (<b>b</b>) site 2, (<b>c</b>) site 3 and (<b>d</b>) site 4. Leaves were analyzed before (0 m) and after 3 and 6 months of exposure (3 m and 6 m). The KeV of the peaks shows which elements are present, and the height of a peak indicates the abundance of that element.</p> Full article ">Figure 6
<p>Score plot of the first two PCs obtained by PCA illustrating sample distributions based on (<b>A</b>) site and plant species (‘C’ for <span class="html-italic">Senecio cineraria</span> and ‘H’ for <span class="html-italic">Hedera helix</span>) and (<b>B</b>) exposure time in months. (<b>C</b>) Loading plot highlighting elements with the main influence on the sample distribution.</p> Full article ">Figure 7
<p>Pie charts of average leaf surface elemental concentration measured by ICP and XRF for <span class="html-italic">Hedera</span> helix and <span class="html-italic">Senecio cineraria</span> plants after 6 months of exposure at sites 1, 2, 3 and 4. Cd and Cu (left side) were only detected by ICP. Al and Si (right side) were only detected using XRF.</p> Full article ">Figure 8
<p>Site locations, Santa Rosa City, La Pampa province, Argentina. (<b>a</b>) Site 1, an urban area with high intensity of car traffic; (<b>b</b>) Site 2, a suburban area with moderate car traffic and unpaved streets; (<b>c</b>) Site 3, a residential area with moderate car traffic; and (<b>d</b>) Site 4, a rural area with low intensity of car traffic, based on Google Maps Traffic.</p> Full article ">
22 pages, 2364 KiB  
Article
Water and Fertilizer Management Is an Important Way to Synergistically Enhance the Yield, Rice Quality and Lodging Resistance of Hybrid Rice
by Haijun Zhu, Lingli Nie, Xiaoe He, Xuehua Wang, Pan Long and Hongyi Chen
Plants 2024, 13(17), 2518; https://doi.org/10.3390/plants13172518 - 7 Sep 2024
Viewed by 845
Abstract
This study comprehensively investigated the synergistic effects and underlying mechanisms of optimized water and fertilizer management on the yield, quality, and lodging resistance of hybrid rice (Oryza sativa), through a two-year field experiment. Two hybrid rice varieties, Xinxiangliangyou 1751 (XXLY1751) and [...] Read more.
This study comprehensively investigated the synergistic effects and underlying mechanisms of optimized water and fertilizer management on the yield, quality, and lodging resistance of hybrid rice (Oryza sativa), through a two-year field experiment. Two hybrid rice varieties, Xinxiangliangyou 1751 (XXLY1751) and Yueliangyou Meixiang Xinzhan (YLYMXXZ), were subjected to three irrigation methods (W1: wet irrigation, W2: flooding irrigation, W3: shallow-wet-dry irrigation) and four nitrogen fertilizer treatments (F1 to F4 with application rates of 0, 180, 225, and 270 kg ha−1, respectively). Our results revealed that the W1F3 treatment significantly enhanced photosynthetic efficiency and non-structural carbohydrate (NSC) accumulation, laying a robust foundation for high yield and quality. NSC accumulation not only supported rice growth but also directly influenced starch and protein synthesis, ensuring smooth grain filling and significantly improving yield and quality. Moreover, NSC strengthened stem fullness and thickness, converting them into structural carbohydrates such as cellulose and lignin, which substantially increased stem mechanical strength and lodging resistance. Statistical analysis demonstrated that water and fertilizer treatments had significant main and interactive effects on photosynthetic rate, dry matter accumulation, yield, quality parameters, NSC, cellulose, lignin, and stem bending resistance. This study reveals the intricate relationship between water and fertilizer management and NSC dynamics, providing valuable theoretical and practical insights for high-yield and high-quality cultivation of hybrid rice, significantly contributing to the sustainable development of modern agriculture. Full article
(This article belongs to the Special Issue Strategies to Improve Water-Use Efficiency in Plant Production)
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<p>(<b>a</b>,<b>b</b>) represent the tillering stages in 2022 and 2023, respectively; (<b>c</b>,<b>d</b>) represent the booting stages in 2022 and 2023, respectively; (<b>e</b>,<b>f</b>) represent the full heading stages in 2022 and 2023, respectively; (<b>g</b>,<b>h</b>) represent the grain filling stages in 2022 and 2023, respectively. Different lowercase letters on the error bars denote statistical differences (at the 0.05 level) among treatments of various varieties based on the LSD test. Significant differences within the same treatment are denoted by ns (<span class="html-italic">p</span> &gt; 0.05), * (0.01 &lt; <span class="html-italic">p</span> ≤ 0.05), and ** (<span class="html-italic">p</span> ≤ 0.01).</p> Full article ">Figure 2
<p>TDW of XXLY1751 (<b>a</b>) and YLYMXXZ (<b>b</b>) in 2022, and XXLY1751 (<b>c</b>) and YLYMXXZ (<b>d</b>) in 2023.</p> Full article ">Figure 3
<p>Figures (<b>a</b>,<b>b</b>) represent the yields of Xinxiangliangyou 1751 and Yueliangyou Meixiangxinzhan in 2022 and 2023, respectively. Different lowercase letters on the error bars indicate statistical differences (at a significance level of 0.05) between treatments of various cultivars in the LSD test. Significant differences within the same treatment are denoted by * (0.01 &lt; <span class="html-italic">p</span> ≤ 0.05), and ** (<span class="html-italic">p</span> ≤ 0.01).</p> Full article ">Figure 4
<p>Head rice rate of XXLY1751 and YLYMXXZ in 2022 (<b>a</b>) and 2023 (<b>b</b>), and chalky grain rate of XXLY1751and YLYMXXZ in 2022 (<b>c</b>) and in 2023 (<b>d</b>). Different lowercase letters denote statistical differences between treatments of each season according to the LSD test (0.05). Significant differences within the same treatment are denoted by ns (<span class="html-italic">p</span> &gt; 0.05), * (0.01 &lt; <span class="html-italic">p</span> ≤ 0.05), and ** (<span class="html-italic">p</span> ≤ 0.01).</p> Full article ">Figure 5
<p>Protein content of XXLY1751 and YLYMXXZ in 2022 (<b>a</b>) and 2023 (<b>b</b>), and amylose content of XXLY1751and YLYMXXZ in 2022 (<b>c</b>) and 2023 (<b>d</b>). Different lowercase letters denote statistical differences between treatments of each season according to the LSD test (0.05). Significant differences within the same treatment are denoted by ns (<span class="html-italic">p</span> &gt; 0.05), and ** (<span class="html-italic">p</span> ≤ 0.01).</p> Full article ">Figure 6
<p>Plant height at center of gravity and panicle length of XXLY1751 (<b>a</b>) and YLYMXXZ (<b>b</b>) in 2022, and XXLY1751 (<b>c</b>) and YLYMXXZ (<b>d</b>) in 2023.</p> Full article ">Figure 7
<p>(<b>a</b>,<b>b</b>) represent the full heading stages in 2022 and 2023, respectively; (<b>c</b>,<b>d</b>) represent the grain filling stages in 2022 and 2023, respectively; (<b>e</b>,<b>f</b>) represent the mature stage in 2022 and 2023, respectively. Different lowercase letters on the error bars denote statistical differences (at the 0.05 level) among treatments of various varieties based on the LSD test. Significant differences within the same treatment are denoted by ns (<span class="html-italic">p</span> &gt; 0.05), * (0.01 &lt; <span class="html-italic">p</span> ≤ 0.05), and ** (<span class="html-italic">p</span> ≤ 0.01).</p> Full article ">Figure 8
<p>Lignin and cellulose of XXLY1751 (<b>a</b>) and YLYMXXZ (<b>b</b>) in 2022, and XXLY1751 (<b>c</b>) and YLYMXXZ (<b>d</b>) in 2023. Different lowercase letters denote statistical differences between treatments of each season according to an LSD test (0.05).</p> Full article ">Figure 9
<p>SPS enzyme activity of XXLY1751 and YLYMXXZ in 2022 (<b>a</b>) and in 2023 (<b>b</b>), α-amylase activity of XXLY1751 and YLYMXXZ in 2022 (<b>c</b>) and in 2023 (<b>d</b>),and β-amylase activity of XXLY1751 and YLYMXXZ in 2022 (<b>e</b>) and in 2023 (<b>f</b>). Different lowercase letters denote statistical differences between treatments of each season according to an LSD test (0.05). Significant differences within the same treatment are denoted by * (0.01 &lt; <span class="html-italic">p</span> ≤ 0.05), and ** (<span class="html-italic">p</span> ≤ 0.01).</p> Full article ">Figure 10
<p>Synergistic regulation of yield, quality, and lodging resistance by water and fertilizer management.</p> Full article ">Figure 11
<p>Water pipes and water meters in the community.</p> Full article ">
16 pages, 10976 KiB  
Article
Multiomics Analysis of the Mechanism by Which Gibberellin Alleviates S-Metolachlor Toxicity in Rice Seedlings
by Cong Wang, Haona Yang, Zhixuan Liu, Lianyang Bai, Lifeng Wang and Shangfeng Zhou
Plants 2024, 13(17), 2517; https://doi.org/10.3390/plants13172517 - 7 Sep 2024
Viewed by 653
Abstract
S-metolachlor is a selective pre-emergence herbicide used in dryland. However, it is challenging to employ in paddy fields due to its phytotoxic effects on rice. As a common phytohormone, Gibberellin-3 (GA3) is inferred to have the ability to alleviate herbicide phytotoxicity. [...] Read more.
S-metolachlor is a selective pre-emergence herbicide used in dryland. However, it is challenging to employ in paddy fields due to its phytotoxic effects on rice. As a common phytohormone, Gibberellin-3 (GA3) is inferred to have the ability to alleviate herbicide phytotoxicity. This study first quantitatively verified the phytotoxicity of s-metolachlor to rice and then demonstrated the mitigative effect of GA3 on these adverse reactions. Furthermore, a transcriptome of rice seedlings subjected to different treatments was constructed to assemble the reference genes, followed by comparative metabolomics and proteomics analyses. Metabolomics revealed an enrichment of flavonoid metabolites in the group of adding GA3, and these flavonoids can eliminate ROS in plants. Proteomics analysis indicated that differential proteins were enriched in the phenylpropanoid biosynthesis pathway responsible for the synthesis of flavonoids and that the functions of most differential proteins are associated with peroxidase. The proteome, combined with the transcriptome, revealed that the expressions of proteins and genes was related to the POD activity in the group of adding GA3. It was speculated that the elimination of ROS is key to alleviating the stress of s-metolachlor on rice growth. It was inferred that the mechanism of GA3 in alleviating the phytotoxicity of the substance s-metolachlor is by increasing the activity of the POD and influencing the growth of rice seedlings through the restoration of flavonoid synthesis. In this study, we screened GA3 as a safener to alleviate the phytotoxicity of s-metolachlor on rice. On this basis, the mechanism of alleviating phytotoxicity was studied. The application range of s-metolachlor might be expanded, providing a new supplementary method for weed control and herbicide resistance management. Full article
(This article belongs to the Section Plant Molecular Biology)
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<p>(<b>A</b>,<b>B</b>) The rice with different treatments after 7d. CK-M4 are different treatments to rice. (<b>C</b>) Shoot length, root length in different treatments. CK: blank control. M2: adding s-metolachlor. M3: adding s-metolachlor and GA<sub>3</sub>. M4: adding GA<sub>3</sub>. Standard error and significant difference are calculated by IBM SPSS Statistics 20.0. abc response 5% significant level.</p> Full article ">Figure 2
<p>Enzymatic activity of rice. X-axis: Different Groups. Y-axis: (<b>A</b>) H<sub>2</sub>O<sub>2</sub> content of rice. (<b>B</b>) relative peroxidase (POD) activity. (<b>C</b>) relative superoxidase dismutase (SOD) activity. (<b>D</b>) relative glutathione s-transferase (GST) activity. CK: Blank control. M2: Adding s-metolachlor. M3: Adding s-metolachlor and GA<sub>3</sub>. M4: Adding GA<sub>3</sub>. Standard error and significant difference are calculated by IBM SPSS Statistics 20.0. abcd response 5% significant.</p> Full article ">Figure 3
<p>The metabolites of different treatments. (<b>A</b>) The number of different metabolites. (<b>B</b>–<b>D</b>) Metabolite numbers in different treatments. (<b>B</b>) CKvsM2 (The control group was CK) (<b>C</b>) CKvsM3 (The control group was CK) (<b>D</b>) M2vsM3 (The control group was M2). The horizontal coordinate represents the type of metabolite, and the vertical coordinate represents the amount of that type of metabolite in this group. (<b>E</b>) Bubble chart of KEGG pathway enrichment in M2vsM3.</p> Full article ">Figure 4
<p>DEGs of M2vsM3. (<b>A</b>) The number of DEGs in different groups. (<b>B</b>) Venn diagram of DEGs (<b>C</b>) The number of DEGs in different pathways. DEGs are selected by annotated in pathway over 10. The control group was M2.</p> Full article ">Figure 5
<p>Histogram of differential proteins. (<b>A</b>) The number of different proteins. (<b>B</b>) GO function classify of proteins. (<b>C</b>) KEGG pathway enrichment of proteins. (<b>D</b>) Protein–protein interaction network. Red bubble stands for the core protein of this network. The control group was M2.</p> Full article ">Figure 6
<p>Phenylpropanoid and flavonoid biosynthesis pathway. The orange block shows differential metabolites in pathways. Red and blue indicate proteins, green and red indicate metabolites, respectively. <span class="html-italic">PTAL</span>, phenylalanine/tyrosine ammonia-lyase; <span class="html-italic">4CL</span>, 4-coumarate--CoA ligase; <span class="html-italic">CAD</span>, cinnamyl-alcohol dehydrogenase; <span class="html-italic">F5H</span>, ferulate-5-hydroxylase; <span class="html-italic">COMT</span>, caffeic acid 3-O-methyltransferase; <span class="html-italic">E1.11.1.7</span>, peroxidase; <span class="html-italic">F3H</span>, naringenin 3-dioxygenase; <span class="html-italic">E5.5.1.6</span>, chalcone isomerase; <span class="html-italic">PGT1</span>, phlorizin synthase.</p> Full article ">Figure 7
<p>qRT-PCR of differential expression genes. (<b>A</b>) <span class="html-italic">Os04g061140</span>; (<b>B</b>) <span class="html-italic">Os06g0522300</span>; (<b>C</b>) <span class="html-italic">Os07g0677500</span>; (<b>D</b>) <span class="html-italic">Os09g0262000</span>; (<b>E</b>) <span class="html-italic">Os10g0109600</span>; (<b>F</b>) <span class="html-italic">Os12g0199500</span>. The horizontal axis represents the control group and the treatment group. The vertical axis represents the expression levels of differential genes. Statistical analysis of variance (ANOVA) with SPSS Version 16.0. The significance level was set at <span class="html-italic">p</span> &lt; 0.05. abc response 5% significant.</p> Full article ">
18 pages, 1588 KiB  
Article
Assisted Phytoremediation between Biochar and Crotalaria pumila to Phytostabilize Heavy Metals in Mine Tailings
by Marcos Rosas-Ramírez, Efraín Tovar-Sánchez, Alexis Rodríguez-Solís, Karen Flores-Trujillo, María Luisa Castrejón-Godínez and Patricia Mussali-Galante
Plants 2024, 13(17), 2516; https://doi.org/10.3390/plants13172516 - 7 Sep 2024
Viewed by 706
Abstract
The increasing demand for mineral resources has generated mine tailings with heavy metals (HM) that negatively impact human and ecosystem health. Therefore, it is necessary to implement strategies that promote the immobilization or elimination of HM, like phytoremediation. However, the toxic effect of [...] Read more.
The increasing demand for mineral resources has generated mine tailings with heavy metals (HM) that negatively impact human and ecosystem health. Therefore, it is necessary to implement strategies that promote the immobilization or elimination of HM, like phytoremediation. However, the toxic effect of metals may affect plant establishment, growth, and fitness, reducing phytoremediation efficiency. Therefore, adding organic amendments to mine tailings, such as biochar, can favor the establishment of plants, reducing the bioavailability of HM and its subsequent incorporation into the food chain. Here, we evaluated HM bioaccumulation, biomass, morphological characters, chlorophyll content, and genotoxic damage in the herbaceous Crotalaria pumila to assess its potential for phytostabilization of HM in mine tailings. The study was carried out for 100 days on plants developed under greenhouse conditions under two treatments (tailing substrate and 75% tailing/25% coconut fiber biochar substrate); every 25 days, 12 plants were selected per treatment. C. pumila registered the following bioaccumulation patterns: Pb > Zn > Cu > Cd in root and in leaf tissues. Furthermore, the results showed that individuals that grew on mine tailing substrate bioaccumulated many times more metals (Zn: 2.1, Cu: 1.8, Cd: 5.0, Pb: 3.0) and showed higher genetic damage levels (1.5 times higher) compared to individuals grown on mine tailing substrate with biochar. In contrast, individuals grown on mine tailing substrate with biochar documented higher chlorophyll a and b content (1.1 times more, for both), as well as higher biomass (1.5 times more). Therefore, adding coconut fiber biochar to mine tailing has a positive effect on the establishment and development of C. pumila individuals with the potential to phytoextract and phytostabilize HM from polluted soils. Our results suggest that the binomial hyperaccumulator plant in combination with this particular biochar is an excellent system to phytostabilize soils contaminated with HM. Full article
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<p>Heavy metal concentration (average ± standard error), two-way ANOVA to determine the effect of treatment, time, and interaction (treatment × time) in root of <span class="html-italic">C. pumila</span> growing under greenhouse conditions. Regression analysis between exposure time and heavy metal concentration in root. The asterisks denote significant differences between treatments by exposure time with <span class="html-italic">p</span> &lt; 0.05 (Tukey). ANOVA test: *** = <span class="html-italic">p</span> &lt; 0.001, * = <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 2
<p>Heavy metal concentration (average ± standard error), two-way ANOVA to determine the effect of treatment, time, and interaction (treatment × time) in leaves of <span class="html-italic">C. pumila</span> growing under greenhouse conditions. Regression analysis between exposure time and heavy metal concentration in root. The asterisks denote significant differences between treatments by exposure time with <span class="html-italic">p</span> &lt; 0.05 (Tukey). ANOVA test: *** = <span class="html-italic">p</span> &lt; 0.001, * = <span class="html-italic">p</span> &lt; 0.05. n.s. = not significant differences.</p> Full article ">Figure 3
<p>Average (±standard error) biomass of <span class="html-italic">C. pumila</span> roots and leaves growing in greenhouse conditions on mine tailing substrate and mine-tailing/substrate. Two-way ANOVA to evaluate the effect of time (100 days) and treatment on root and leaves biomass characters, and simple regression analysis to evaluate the relationship between exposure time to the substrate and biomass characters. The asterisks denote significant differences between treatments by exposure time with <span class="html-italic">p</span> &lt; 0.05 (Tukey). ANOVA test: *** = <span class="html-italic">p</span> &lt; 0.001, * = <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 4
<p>Average ± standard error of chlorophyll <span class="html-italic">a</span> and <span class="html-italic">b</span> from leaves from <span class="html-italic">C. pumila</span> growing in greenhouse conditions on mine tailing substrate and mine-tailing/substrate. Two-way ANOVA to evaluate the effect of time (100 days) and treatment on chlorophyll content from leaves, and simple regressions analysis to evaluate the relationship between exposure time to the substrate and chlorophyll content. The asterisks denote significant differences between treatments by exposure time with <span class="html-italic">p</span> &lt; 0.05 (Tukey). ANOVA test: *** = <span class="html-italic">p</span> &lt; 0.001, * = <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 5
<p>Average ± standard deviation of genetic damage (single strand breaks) in foliar tissue from <span class="html-italic">C. pumila</span> growing in greenhouse conditions on mine tailing substrate and mine tailing/substrate. Two-way ANOVA to evaluate the effect of time (100 days) and treatment on genetic damage, and simple regression analysis to evaluate the relationship between exposure time to the substrate and genetic damage. The asterisks denote significant differences between treatments by exposure time with <span class="html-italic">p</span> &lt; 0.05 (Tukey). ANOVA test: *** = <span class="html-italic">p</span> &lt; 0.001, ** = <span class="html-italic">p</span> &lt; 0.01, * = <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">
12 pages, 1599 KiB  
Communication
Antibacterial and Antioxidant Activities of Hydroalcoholic and Phenolic Extracts from Ternstroemia dentisepala and T. lineata Leaves
by Alexis Uriel Soto Díaz, María Luisa Villarreal, Marcelo Victorio-De los Santos and Alexandre Toshirrico Cardoso-Taketa
Plants 2024, 13(17), 2515; https://doi.org/10.3390/plants13172515 - 7 Sep 2024
Viewed by 783
Abstract
Traditional Mexican medicine commonly uses infusions of Ternstroemia spp. to treat insomnia, injuries, and infections. The antibacterial activities of Ternstroemia dentisepala and Ternstroemia lineata were evaluated for the first time against a panel of Gram-positive and Gram-negative bacteria that have implications for human health, [...] Read more.
Traditional Mexican medicine commonly uses infusions of Ternstroemia spp. to treat insomnia, injuries, and infections. The antibacterial activities of Ternstroemia dentisepala and Ternstroemia lineata were evaluated for the first time against a panel of Gram-positive and Gram-negative bacteria that have implications for human health, including Enterococcus faecalis, Streptococcus agalactiae, Streptococcus pyogenes, Salmonella typhi, Pseudomonas aeruginosa, and Vibrio parahaemolyticus. Furthermore, the scavenging potential of the hydroalcoholic (HAEs) and total phenolic extracts (TPEs) from the leaves of both plants by a 2,2’-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) assay (ABTS•+) was determined. Also, the total phenolic contents of the HAEs using the Folin–Ciocalteu reagent were assayed. T. dentisepala HAE and TPE were active against all bacterial strains tested, with a minimum inhibitory concentration between 1.0 and 6.0 mg/mL, with the last one being the most active. However, the T. lineata extracts only demonstrated effectiveness against S. typhi and P. aeruginosa. The TPEs from T. dentisepala and T. lineata improved the activity by approximately 30% in all bacteria tested in comparison with the HAEs. The T. dentisepala HAE had a higher total phenolic content than the T. lineata extract, which was consistent with its ABTS•+-scavenging activity. The two HAEs had different chemical profiles, mostly because of the types and amounts of phenolic compounds they contained. These profiles were obtained using thin-layer chromatography (TLC), high-performance liquid chromatography (HPLC), and proton nuclear magnetic resonance (1H NMR) experiments. Full article
(This article belongs to the Special Issue Biological Activities of Plant Extracts 2023)
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<p>HPLC analysis of the hydroalcoholic extracts from the leaves of <span class="html-italic">T. lineata</span> and <span class="html-italic">T. dentisepala</span>. Chromatographic conditions: C-18 column and a mobile phase of acetonitrile and water (85:15) with a flow of 1 mL/min and detection at 254 nm.</p> Full article ">Figure 2
<p>Thin-layer chromatographic profiles of the hydroalcoholic extracts of five individuals of <span class="html-italic">T. dentisepala</span> and <span class="html-italic">T. lineata</span>. The numbers 1, 2, 3, 4, and 5 represent extracts from different individuals of the same species. (<b>A</b>) UV light at 365 nm; (<b>B</b>) UV light at 254 nm; (<b>C</b>) plate developed with vanillin/H<sub>2</sub>SO<sub>4</sub> and 120 °C; (<b>D</b>) plate developed with vanillin/H<sub>2</sub>SO<sub>4</sub> and observed with UV light at 365 nm. The elution system was composed of a mobile phase with chloroform and methanol (4:1) in silica gel.</p> Full article ">Figure 3
<p><sup>1</sup>H NMR of the hydroalcoholic extracts from the leaves of <span class="html-italic">T. dentisepala</span>, in green color, and <span class="html-italic">T. lineata</span>, in brown color, at 600 MHz in a CD<sub>3</sub>OD solvent.</p> Full article ">
19 pages, 5052 KiB  
Article
Genome-Wide Analysis of the Nramp Gene Family in Kenaf (Hibiscus cannabinus): Identification, Expression Analysis, and Response to Cadmium Stress
by Qin Liu, Shaocui Li, Guanghui Du and Xia An
Plants 2024, 13(17), 2514; https://doi.org/10.3390/plants13172514 - 7 Sep 2024
Viewed by 792
Abstract
Kenaf (Hibiscus cannabinu) is a grass bast fiber crop that has the ability to tolerate and accumulate heavy metals, and it has been considered as a potential heavy metal accumulator and remediation plant. Nramp is a natural resistance-related macrophage, which plays [...] Read more.
Kenaf (Hibiscus cannabinu) is a grass bast fiber crop that has the ability to tolerate and accumulate heavy metals, and it has been considered as a potential heavy metal accumulator and remediation plant. Nramp is a natural resistance-related macrophage, which plays an important role in the transport of divalent metal ions, plant growth and development, and abiotic stress. In this study, the Nramp gene family of kenaf was analyzed at the whole genome level. A total of 15 HcNramp genes were identified. They are distributed unevenly on chromosomes. Phylogenetic analysis classified 15 HcNramp proteins into 3 different subfamilies. All proteins share specific motif 4 and motif 6, and the genes belonging to the same subfamily are similar in structure and motif. The promoters are rich in hormone response, meristem expression, and environmental stress response elements. Under different treatments, the expression levels of HcNramp genes vary in different tissues, and most of them are expressed in roots first. These findings can provide a basis for understanding the potential role of the Nramp gene family in kenaf in response to cadmium (Cd) stress, and are of great significance for screening related Cd tolerance genes in kenaf. Full article
(This article belongs to the Special Issue Crop Functional Genomics and Biological Breeding)
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<p>Phylogenetic analysis of 29 Nramp proteins in kenaf, <span class="html-italic">A. thaliana</span>, and tomato. Fifteen proteins of kenaf are labeled in black font, seven proteins of <span class="html-italic">A. thaliana</span> are in blue font, and seven proteins of tomato are in green font. The maximum likelihood tree was created using MEGAX (bootstrap value = 1000) and the bootstrap value of each branch is displayed.</p> Full article ">Figure 2
<p>Conservation motif and gene structure analysis of <span class="html-italic">HcNramp</span> genes according to the phylogenetic relationship. (<b>a</b>) The phylogenetic relationship of HcNramps. (<b>b</b>) Conserved motifs of HcNramps. Different colors represent different motifs. (<b>c</b>) Exon and intron structures of the <span class="html-italic">HcNramp</span> genes in kenaf. The grey lines indicate introns, the green boxes represent exons, and the orange boxes indicate untranslated regions. (<b>d</b>) The amino acid composition of each motif.</p> Full article ">Figure 3
<p>Identification of the cis-acting elements in the promoter of <span class="html-italic">HcNramp genes</span>.</p> Full article ">Figure 4
<p>Chromosome location of <span class="html-italic">HcNramp</span> genes. Chromosome numbers are shown to the left of the chromosomes. <span class="html-italic">Nramp</span> genes are labeled to the right of the chromosomes. Scale bar on the left indicates the chromosome lengths (Mb).</p> Full article ">Figure 5
<p>Intraspecific collinearity analysis of the kenaf <span class="html-italic">Nramp</span> gene family. The green lines in the figure are common genes, and the gray lines represent collinear blocks of the plant genome.</p> Full article ">Figure 6
<p>Interspecific collinearity analysis of the kenaf <span class="html-italic">Nramp</span> gene family. (<b>a</b>) Interspecific collinearity analysis of kenaf and <span class="html-italic">A. thaliana</span>. (<b>b</b>) Interspecific collinearity analysis of kenaf and tomato. The red lines in the figure are the common genes between kenaf, <span class="html-italic">A. thaliana,</span> and tomato, and the gray lines represent the collinear blocks of the plant genome.</p> Full article ">Figure 7
<p>Tissue-specific expression profiles of <span class="html-italic">HcNramp</span> genes in kenaf. CK-H-R represents the root samples treated by CK + water spraying, CK-M-R represents the root samples treated by CK + melatonin spraying, Cd-H-R represents the root samples treated by Cd + water spraying, and Cd-M-R represents the root samples treated by Cd + melatonin spraying. L, S, and R represent the leaves, stems, and roots of kenaf samples. Red and blue boxes indicate high and low expression levels of genes, respectively.</p> Full article ">Figure 8
<p>The expression levels of <span class="html-italic">HcNramp</span> genes in kenaf roots under different treatments. Data represent means ± SD of three biological replicates. CK-H-R represents control samples. The expression levels of each gene were normalized against its own expression level in the root tissue in control (CK-H-R) conditions. Error bars indicate mean ± SD and asterisks indicate statistical differences between the treatment samples and the corresponding control samples, the roots (n = 3; * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001 and ns not significant; Student’s <span class="html-italic">t</span>-test).</p> Full article ">Figure 9
<p>The expression levels of <span class="html-italic">HcNramp</span> genes in kenaf stems under different treatments. Data represent means ± SD of three biological replicates. CK-H-S represents control samples. The expression levels of each gene were normalized against its own expression level in the stem tissue in control (CK-H-S) conditions. Error bars indicate mean ± SD and asterisks indicate statistical differences between the treatment samples and the corresponding control samples, the stems (n = 3; * <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, **** <span class="html-italic">p</span> &lt; 0.0001 and ns not significant; Student’s <span class="html-italic">t</span>-test).</p> Full article ">Figure 10
<p>The expression levels of <span class="html-italic">HcNramp</span> genes in kenaf leaves under different treatments. Data represent means ± SD of three biological replicates. CK-H-L represents control samples. The expression levels of each gene were normalized against its own expression level in the leaf tissue in control (CK-H-L) conditions. Error bars indicate mean ± SD and asterisks indicate statistical differences between the treatment samples and the corresponding control samples, the leaves (n = 3; * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001 and ns not significant; Student’s <span class="html-italic">t</span>-test).</p> Full article ">
17 pages, 12208 KiB  
Article
Metabolomics Analysis of Phenolic Composition and Content in Five Pear Cultivars Leaves
by Huijun Jiao, Qiuzhu Guan, Ran Dong, Kun Ran, Hongwei Wang, Xiaochang Dong and Shuwei Wei
Plants 2024, 13(17), 2513; https://doi.org/10.3390/plants13172513 - 7 Sep 2024
Viewed by 920
Abstract
Phenolic compounds are the predominant chemical constituents in the secondary metabolites of plants and are commonly found in pears. In this study, we focused on the analysis of the phenolic composition and antioxidant activity of leaves from five pear cultivars (Cuiguan, Chaohong, Kuerle, [...] Read more.
Phenolic compounds are the predominant chemical constituents in the secondary metabolites of plants and are commonly found in pears. In this study, we focused on the analysis of the phenolic composition and antioxidant activity of leaves from five pear cultivars (Cuiguan, Chaohong, Kuerle, Nanguoli, and Yali) and tea leaves (Fudingdabai as the control) using ultra-performance liquid chromatography coupled with electrospray ionization triple quadrupole mass spectrometry. The results indicated significant differences in the amount and composition of phenolic metabolites between tea and pear leaves, as well as among the five pear varieties. Only approximately one-third of the metabolites exhibited higher levels in pear leaves compared to that in tea leaves. The total phenol content in the Yali cultivar was higher than that in the other pear cultivars. Furthermore, specific phenolic metabolites with high expression were identified in the leaves of different groups. The levels of delphinidin 3-glucoside, aesculin, prunin, cosmosiin, quercetin 3-galactoside, isorhamnetin-3-O-glucoside, nicotiflorin, narcissin, chlorogenic acid, and cryptochlorogenic acid were relatively high among the five pear cultivars. (-)-Gallocatechin gallate, 6-methylcoumarin, aesculetin, hesperidin, kaempferol, and caftaric acid were identified as specific metabolic substances unique to each type of pear leaf. Most of the differential metabolites showed positive correlations and were primarily enriched in the flavonoid biosynthesis, flavone and flavonol biosynthesis, and phenylpropanoid biosynthesis pathways. DPPH (1,1-Diphenyl-2-picrylhydrazyl radical) analysis indicated that the Yali cultivar exhibited the highest antioxidant activity compared to other varieties. This systematic analysis of the differences in phenolic metabolite composition and antioxidant activity between pear and tea leaves provides a theoretical foundation for the development and utilization of pear leaf resources. Full article
(This article belongs to the Special Issue In Vivo and In Vitro Production for Plant Secondary Metabolites: Recent Advances and Biological Applications)
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<p>The density distribution of metabolites and analysis of metabolite cluster expression. (<b>A</b>): Sample metabolite strength box plot. (<b>B</b>): The expression classification of 13 phenolic substances detected. (<b>C</b>): HCA analysis of the expression level of 13 phenolic substances detected. Color scale represents log2 transformed expression content values. Blue indicates low expression and red indicates high expression.</p> Full article ">Figure 2
<p>Hierarchical clustering analysis of metabolite expression level trend of six leaves. Color scale represents log2 transformed expression content values of each metabolite. Blue indicates low expression and red indicates high expression.</p> Full article ">Figure 3
<p>Comparative analysis of differential metabolites in five comparison groups. (<b>A</b>): Statistical analysis of the number of differential metabolites in five comparison groups. The different colors indicate different comparison groups. (<b>B</b>): Comparative analysis of differential metabolites by Venn diagram in five comparison groups. The different colors indicate different comparison groups.</p> Full article ">Figure 4
<p>K-mean cluster analysis of differential metabolites. The black line shows the overall trend of metabolites in each sub class. The other different colored lines represent different sub classes.</p> Full article ">Figure 5
<p>KEGG enrichment pathway analysis of differential metabolites in comparison groups. The greater the rich factor, the greater the degree of enrichment. The color from red to green indicates that the <span class="html-italic">p</span>-value decreases in turn. The larger the dot, the more metabolites are enriched in the pathway.</p> Full article ">Figure 6
<p>The metabolic pathway diagram for the main phenolic metabolites. The content of phenolic metabolites is shown in heat maps based on the abundance in the metabolite profile. Blue indicates low expression and red indicates high expression. The main phenolic metabolites detected in this study are colored in blue and their corresponding metabolic pathways are colored in yellow. The metabolites in black represent that they were not detected.</p> Full article ">Figure 7
<p>Correlation analysis of metabolites in the six groups of leaves. Blue indicates a negative correlation and red indicates a positive correlation.</p> Full article ">Figure 8
<p>Evaluation and correlation analysis of the antioxidant capacity of leaves. (<b>A</b>): Detection of antioxidant capacity of six groups of leaves. The different colors indicate different sample groups. The values presented are the means of three independent replicates. (<b>B</b>): Correlation analysis of antioxidant capacity and metabolites in leaves. The different colors indicate different metabolites.</p> Full article ">
11 pages, 7191 KiB  
Article
The Small Auxin-Up RNA 50 (SAUR50) Gene from Ammopiptanthus nanus Negatively Regulates Drought Tolerance
by Yuanyuan Zhang, Qi Li, Mengyang Jiang, Hui Tian, Muhammad Hayder Bin Khalid, Yingge Wang and Haoqiang Yu
Plants 2024, 13(17), 2512; https://doi.org/10.3390/plants13172512 - 7 Sep 2024
Viewed by 968
Abstract
Drought stress is a primary abiotic stress that causes significant losses to forestry and agricultural production. Therefore, exploring drought-responsive genes and their regulatory mechanism is crucial for plant molecular breeding for forestry and agriculture production safety. Small auxin-up RNA (SAUR) proteins are essential [...] Read more.
Drought stress is a primary abiotic stress that causes significant losses to forestry and agricultural production. Therefore, exploring drought-responsive genes and their regulatory mechanism is crucial for plant molecular breeding for forestry and agriculture production safety. Small auxin-up RNA (SAUR) proteins are essential in plant growth and development but show functional diversity in stress response. In this study, the transcriptome sequencing data of Ammopiptanthus nanus seedlings revealed that the expression of AnSAUR50 was continuously downregulated under drought stress. Hence, the AnSAUR50 gene was cloned and functionally analyzed in drought response. The results showed that the coding sequence of AnSAUR50 was 315 bp in length and encoded 104 amino acids. The AnSAUR50 protein showed high conservation, possessed a SAUR-specific domain, and localized in the nucleus and cell membrane. The heterologous expression of the AnSAUR50 gene enhanced the drought sensitivity of the transgenic Arabidopsis with a lower survival rate, biomass, and higher malondialdehyde content and relative electrolyte leakage. Moreover, transgenic plants showed shorter root lengths and bigger stomatal apertures, resulting in facilitating water loss under drought stress. The study indicates that AnSAUR50 negatively regulates drought tolerance by inhibiting root growth and stomatal closure, which provides insights into the underlying function and regulatory mechanism of SAURs in plant stress response. Full article
(This article belongs to the Topic Plant Responses to Environmental Stress)
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<p>Identification of AnSAUR50. (<b>A</b>) The phylogenetic tree of AnSAUR50 and other SAURs. (<b>B</b>) Multiple alignments of AnSAUR50 and other SAURs, indicating that the SAUR-specific domain is highly conserved. Red, blue and gray backgrounds mean 100%, 75% and 50% conservation, respectively. (<b>C</b>) The predicted structure of AnSAUR50 and CcSAUR50 protein. (<b>D</b>) Expression pattern of <span class="html-italic">AnSAUR50</span> gene under polyethylene glycol (PEG) treatment for simulating drought conditions. The RNA-seq data were retrieved from our previous project (PRJNA684798). Box indicates the SAUR-specific domain. The black dot means AnSAUR50. ** indicates statistical significance at <span class="html-italic">p</span> &lt; 0.01 level.</p> Full article ">Figure 2
<p>Subcellular localization of AnSAUR50. (<b>A</b>) The diagram of expression vector. <span class="html-italic">35S</span> promoter was used to drive <span class="html-italic">AnSAUR50</span>-<span class="html-italic">eGFP</span>, and <span class="html-italic">eGFP</span> gene. <span class="html-italic">OCS</span>, the 3’-flanking region of octopine synthase gene, was used as terminator. (<b>B</b>) The photos of eGFP fluorescence signal in the cells transformed by <span class="html-italic">35S</span>-<span class="html-italic">AnSAUR50</span>-<span class="html-italic">eGFP</span> and <span class="html-italic">35S</span>-<span class="html-italic">eGFP</span>. Scar bar was 50 μm.</p> Full article ">Figure 3
<p>Identification of transgenic lines. (<b>A</b>) The diagram of expression vectors. The <span class="html-italic">35S</span> and <span class="html-italic">OCS</span> sequences were used to drive and terminate <span class="html-italic">AnSAUR50</span> gene. <span class="html-italic">MASp</span> and <span class="html-italic">MASt</span> mean promoter and terminator of mannopine synthase. <span class="html-italic">BlpR</span>, encoding phosphinothricin acetyltransferase confers resistance to bialophos or phosphinothricin. (<b>B</b>) Phenotype of basta screening for transgenic plants. (<b>C</b>) PCR detection of transgenic lines. M, DNA marker DL2000. L1 to L6 were T<sub>1</sub> transgenic lines overexpressing <span class="html-italic">AnSAUR50</span>. (<b>D</b>) RT-PCR analysis. The <span class="html-italic">AtUBQ5</span> was used as a reference gene. WT, wild type. L1 and L3 were homozygous lines expressing <span class="html-italic">AnSAUR50</span> gene.</p> Full article ">Figure 4
<p>Overexpression of <span class="html-italic">AnSAUR50</span> enhanced drought sensitivity of transgenic <span class="html-italic">Arabidopsis</span>. (<b>A</b>) Phenotype of <span class="html-italic">A. thaliana</span> transformed by <span class="html-italic">AnSAUR50</span> under drought stress. (<b>B</b>) Survival rate of each line after drought stress. (<b>C</b>) Biomass of every line after drought stress. (<b>D</b>) Malondialdehyde (MDA) content. (<b>E</b>). Relative electrolyte leakage (REL). WT, wild type. L1 and L3 were homozygous lines expressing the <span class="html-italic">AnSAUR50</span> gene. ** indicates statistical significance at <span class="html-italic">p</span> &lt; 0.01 level.</p> Full article ">Figure 5
<p>Overexpression of <span class="html-italic">AnSAUR50</span> resulted in root growth retardation of transgenic <span class="html-italic">Arabidopsis</span> under simulated drought conditions. (<b>A</b>) Root phenotype. (<b>B</b>) The statistics of root length. WT, wild type. L1 and L3 were homozygous lines expressing <span class="html-italic">AnSAUR50</span> gene. ** indicates statistical significance at <span class="html-italic">p</span> &lt; 0.01 level.</p> Full article ">Figure 6
<p>Overexpression of <span class="html-italic">AnSAUR50</span> altered stomatal aperture of transgenic <span class="html-italic">Arabidopsis</span>. (<b>A</b>) Phenotype of stoma of each line. The leaf samples of every line were excised for 90 min of dehydration and used to detect stomatal aperture. Scar bar = 20 µm. (<b>B</b>) The statistical data of stomatal aperture. (<b>C</b>) Water loss rate of detached leaves. WT, wild type. L1 and L3 were homozygous lines expressing <span class="html-italic">AnSAUR50</span> gene. * and ** indicate statistical significance at <span class="html-italic">p</span> &lt; 0.05 and <span class="html-italic">p</span> &lt; 0.01 level.</p> Full article ">
15 pages, 3145 KiB  
Review
L-Band Synthetic Aperture Radar and Its Application for Forest Parameter Estimation, 1972 to 2024: A Review
by Zilin Ye, Jiangping Long, Tingchen Zhang, Bingbing Lin and Hui Lin
Plants 2024, 13(17), 2511; https://doi.org/10.3390/plants13172511 - 7 Sep 2024
Cited by 1 | Viewed by 1405
Abstract
Optical remote sensing can effectively capture 2-dimensional (2D) forest information, such as woodland area and percentage forest cover. However, accurately estimating forest vertical-structure relevant parameters such as height using optical images remains challenging, which leads to low accuracy of estimating forest stocks like [...] Read more.
Optical remote sensing can effectively capture 2-dimensional (2D) forest information, such as woodland area and percentage forest cover. However, accurately estimating forest vertical-structure relevant parameters such as height using optical images remains challenging, which leads to low accuracy of estimating forest stocks like biomass and carbon stocks. Thus, accurately obtaining vertical structure information of forests has become a significant bottleneck in the application of optical remote sensing to forestry. Microwave remote sensing such as synthetic aperture radar (SAR) and polarimetric SAR provides the capability to penetrate forest canopies with the L-band signal, and is particularly adept at capturing the vertical structure information of forests, which is an alternative ideal remote-sensing data source to overcome the aforementioned limitation. This paper utilizes the Citexs data analysis platform, along with the CNKI and PubMed databases, to investigate the advancements of applying L-band SAR technology to forest canopy penetration and structure-parameter estimation, and provides a comprehensive review based on 58 relevant articles from 1978 to 2024 in the PubMed database. The metrics, including annual publication numbers, countries/regions from which the publications come, institutions, and first authors, with the visualization of results, were utilized to identify development trends. The paper summarizes the state of the art and effectiveness of L-band SAR in addressing the estimation of forest height, moisture, and forest stocks, and also examines the penetration depth of the L-band in forests and highlights key influencing factors. This review identifies existing limitations and suggests research directions in the future and the potential of using L-band SAR technology for forest parameter estimation. Full article
(This article belongs to the Special Issue The Application of Spectral Techniques in Agriculture and Forestry)
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<p>SCI published papers from 1978 to 2024 dealing with L-band SAR data and related to forest penetration.</p> Full article ">Figure 2
<p>The distribution of the SCI publications related to the use of L-band SAR data and forest canopy penetration.</p> Full article ">Figure 3
<p>Top-20 research institutions that published SCI papers dealing with L-band SAR data and forest canopy penetration.</p> Full article ">Figure 4
<p>The-top 30 first authors who published SCI articles in the field of using L-band SAR data and dealing with forest canopy penetration.</p> Full article ">Figure 5
<p>Publications by journal in the field of using L-band SAR data and dealing with forest canopy penetration.</p> Full article ">
16 pages, 5386 KiB  
Article
Yield Difference between Different Cultivation Techniques under Ultrasonic Treatment Driven by Radiation Use Efficiency
by Sicheng Deng, Qichang Gu, Yizhu Wu, Wentao Yi, Jian Lu, Ligong Peng and Xiangru Tang
Plants 2024, 13(17), 2510; https://doi.org/10.3390/plants13172510 - 6 Sep 2024
Viewed by 645
Abstract
Ultrasonic treatment and optimal cultivation techniques are both conducive to the high yield of super rice in South China. Many previous studies have shown that the increase in intercepted photosynthetically active radiation (IPAR) and radiation use efficiency (RUE) is an important reason for [...] Read more.
Ultrasonic treatment and optimal cultivation techniques are both conducive to the high yield of super rice in South China. Many previous studies have shown that the increase in intercepted photosynthetically active radiation (IPAR) and radiation use efficiency (RUE) is an important reason for high rice yield. Field experiments were conducted over two years to evaluate the effects of IPAR and RUE on the yield under different treatments (CK: conventional cultivation technique without ultrasonic treatment; T1: conventional cultivation technique with ultrasonic treatment; T2: super rice-specific cultivation technique without ultrasonic treatment and T3: super rice-specific cultivation technique with ultrasonic treatment), with two representative rice varieties, Wufengyou-615 (WFY) and Jingnongsimiao (JNSM) during the late seasons of rice cultivation in South China. The super rice-specific cultivation technique and the ultrasonic treatment could significantly increase the yield, which was significantly (p < 0.01) and positively correlated with panicle number, grain-filling rate, and aboveground total dry weight. The higher grain yield depended more highly on higher RUE in the mid-tillering stage and maturity stage. The results of multiple-regression models also showed that the contributions of IPAR and RUE to yield were significant (p < 0.01). Conclusively, IPAR and RUE contributed a lot to yield progress of super rice in both super rice-specific cultivation techniques with fewer times of topdressing and ultrasonic treatment in South China. It is worth further studying how to reasonably improve the RUE of high-RUE varieties through other means. Full article
(This article belongs to the Section Crop Physiology and Crop Production)
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<p>Grain yield of two rice varieties under different treatments in 2022 and 2023. Vertical bars indicate standard errors (n = 3). Different lowercase letters indicate statistical differences among treatments at <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 2
<p>Yield components of two rice varieties under different treatments in 2022 and 2023. Vertical bars indicate standard errors (n = 3). Different lowercase letters indicate statistical differences among treatments at <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 3
<p>Aboveground total dry weight (TDW) of two rice varieties under treatments in MT (<b>a</b>), FL (<b>c</b>), and MA (<b>e</b>) in 2022, and MT (<b>b</b>), FL (<b>d</b>), and MA (<b>f</b>) in 2023. Vertical bars indicate standard errors (n = 3). Different lowercase letters indicate statistical differences among treatments at <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 4
<p>Crop growth rate (CGR) of two rice varieties under different treatments in 2022 and 2023. Vertical bars indicate standard errors (n = 3).</p> Full article ">Figure 5
<p>Harvest index (HI) of two rice varieties under different treatments in 2022 and 2023. Vertical bars indicate standard errors (n = 3). Different lowercase letters indicate statistical differences among treatments at <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 6
<p>Correlation matrix of various grain yield and population growth parameters (n = 48). GY, grain yield; PN, panicle number; SP, spikelets per panicle; GF, grain filling; GW, 1000-grain weight; HI, harvest index; MT, mid-tillering stage; FL, flowering stage; MA, maturity; WGS, whole growth stage; CGR, crop growth rate; DW, dry weight; IP, light interception percentage; PAR, photosynthetically active radiation; IPAR, intercepted photosynthetically active radiation; RUE, radiation use efficiency.</p> Full article ">Figure 7
<p>Relationships between grain yield and intercepted photosynthetically active radiation (IPAR) and radiation use efficiency (RUE) in MT, MT–FL, and FL–MA. Data were from all replicates across two years (n = 12). ** significant at <span class="html-italic">p</span> &lt; 0.01; ns, non-significant.</p> Full article ">Figure 8
<p>Maximum temperature, minimum temperature, and solar radiation from July to October in 2022 and 2023 in Zengcheng, Guangdong Province, China. DAT, growth date.</p> Full article ">
11 pages, 7014 KiB  
Communication
A Fast, Efficient, and Tissue-Culture-Independent Genetic Transformation Method for Panax notoginseng and Lilium regale
by Jie Deng, Wenyun Li, Xiaomin Li, Diqiu Liu and Guanze Liu
Plants 2024, 13(17), 2509; https://doi.org/10.3390/plants13172509 - 6 Sep 2024
Viewed by 1389
Abstract
The Agrobacterium-based transgenic technique is commonly used for gene function validation and molecular breeding. However, it is not suitable for plants with a low regeneration capacity or a low transformation rate, such as Panax notoginseng (Burk) F.H. Chen and Lilium regale Wilson. [...] Read more.
The Agrobacterium-based transgenic technique is commonly used for gene function validation and molecular breeding. However, it is not suitable for plants with a low regeneration capacity or a low transformation rate, such as Panax notoginseng (Burk) F.H. Chen and Lilium regale Wilson. In this study, a novel Agrobacterium transformation method based on injection in the meristems was developed using P. notoginseng and L. regale as experimental models. PCR analysis confirmed the successful integration of the reporter gene DsRed2 (Discosoma striata red fluorescence protein 2) into the genome of two experimental models. QRT-PCR and Western blot analysis demonstrated the transcriptional and translational expression of DsRed2. Additionally, laser confocal microscopy confirmed the significant accumulation of the red fluorescent protein in the leaves, stems, and roots of transformed P. notoginseng and L. regale. Most importantly, in the second year after injection, the specific bright orange fluorescence from DsRed2 expression was observed in the transgenic P. notoginseng and L. regale plants. This study establishes a fast, efficient, and tissue-culture-independent transgenic technique suitable for plants with a low regeneration capacity or a low transformation rate. This technique may improve the functional genomics of important medicinal and ornamental plants such as P. notoginseng and L. regale, as well as their molecular breeding. Full article
(This article belongs to the Section Plant Development and Morphogenesis)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Schematic diagram illustrating <span class="html-italic">Agrobacterium tumefaciens</span> injection into the rhizome of <span class="html-italic">Panax notoginseng</span>.</p> Full article ">Figure 2
<p><span class="html-italic">A. tumefaciens</span> injection into the rhizome of <span class="html-italic">P. notoginseng</span> is a stable and efficient transformation method. (<b>A</b>) Comparison of phenotype between the injected <span class="html-italic">P. notoginseng</span> plants (PT-3/4/5) and wild-type (WT) plant. (<b>B</b>) Gel electrophoresis showing <span class="html-italic">DsRed2</span> (<span class="html-italic">Discosoma striata red fluorescence protein 2</span>) amplification from genomic DNA of injected <span class="html-italic">P. notoginseng</span> leaves. P, WT, and PT1-PT14 represent PCR reaction of wild-type <span class="html-italic">P. notoginseng</span>, pCAMBIA2300S-<span class="html-italic">DsRed2</span> plasmids, and <span class="html-italic">DsRed2</span> transgenic <span class="html-italic">P. notoginseng</span>, respectively. (<b>C</b>) Expression of <span class="html-italic">DsRed2</span> in leaves of transgenic <span class="html-italic">P. notoginseng</span> as determined by qPCR. WT and PT-1/3/4/5/8/11/13 represent the qPCR reaction with the leaves of wild-type and <span class="html-italic">DsRed2</span> transgenic <span class="html-italic">P. notoginseng</span> plants, respectively. (<b>D</b>) Expression of <span class="html-italic">DsRed2</span> in the stems of transgenic <span class="html-italic">P. notoginseng</span> as determined by qPCR. WT and PT-1/3/4/5/8/11/13 represent the qPCR reaction with the stems of wild-type and <span class="html-italic">DsRed2</span> transgenic <span class="html-italic">P. notoginseng</span> plants, respectively. (<b>E</b>) Detection of DsRed2 protein expression in leaves of transgenic <span class="html-italic">P. notoginseng</span> through Western blot. WT-1/2 and PT-1/3/4/5/8/11/13 represent the leaf total protein of wild-type and <span class="html-italic">DsRed2</span> transgenic <span class="html-italic">P. notoginseng</span> plants, respectively. (<b>F</b>) Observation of specific red fluorescence in leaves, stems, and roots of transgenic <span class="html-italic">P. notoginseng</span> under a laser scanning confocal microscope. WT and PT-3 represent the wild-type and <span class="html-italic">DsRed2</span> transgenic <span class="html-italic">P. notoginseng</span> plants, respectively. (<b>G</b>) Observation of specific fluorescence in transgenic <span class="html-italic">P. notoginseng</span> with a handle fluorescence detector. WT and PT-3 represent the wild-type and <span class="html-italic">DsRed2</span> transgenic <span class="html-italic">P. notoginseng</span> plants in the second year after injection, respectively. <span class="html-italic">T</span>-test was used to reveal the statistical difference of <span class="html-italic">DsRed2</span> expression level between wild type and transgenic plants (** <span class="html-italic">p</span> &lt; 0.01).</p> Full article ">Figure 3
<p>Schematic diagram illustrating <span class="html-italic">A. tumefaciens</span> injection into the meristems of <span class="html-italic">Lilium regale</span> bulb.</p> Full article ">Figure 4
<p><span class="html-italic">A. tumefaciens</span> injection into the meristems of the <span class="html-italic">L. regale</span> bulb is a fast and efficient transformation method. (<b>A</b>) Comparison of phenotype between the injected <span class="html-italic">L. regale</span> plants (T-1/2/3) and wild-type (WT) plant. (<b>B</b>) Gel electrophoresis showing <span class="html-italic">DsRed2</span> amplification from injected <span class="html-italic">L. regale</span> leaves. T-1~T-20, P, and WT represent PCR reaction of <span class="html-italic">DsRed2</span> transgenic <span class="html-italic">L. regale</span>, pCAMBIA2300S-<span class="html-italic">DsRed2</span> plasmids, and wild-type, respectively. (<b>C</b>) Expression of <span class="html-italic">DsRed2</span> in leaves of transgenic <span class="html-italic">L. regale</span> as determined by qPCR. WT and T-1/2/3/5/6/7/9/10/11/19/20/21 represent qPCR reaction of the wild-type and <span class="html-italic">DsRed2</span> transgenic <span class="html-italic">L. regale</span> plants. (<b>D</b>) Detection of DsRed2 protein expression in leaves of transgenic <span class="html-italic">L. regale</span> through Western blot. WT and T-1/5/6/19/20/21 represent the leaf total protein of wild-type and <span class="html-italic">DsRed2</span> transgenic <span class="html-italic">L. regale</span> plants. (<b>E</b>) Observation of specific red fluorescence in leaves, stems, roots, and scales of transgenic <span class="html-italic">L. regale</span> under a laser scanning confocal microscope. WT and T-6 represent the wild-type and <span class="html-italic">DsRed2</span> transgenic <span class="html-italic">L. regale</span> plants, respectively. (<b>F</b>) Observation of specific fluorescence in transgenic <span class="html-italic">L. regale</span> with a handle fluorescence detector. WT and T-6 represent the wild-type and <span class="html-italic">DsRed2</span> transgenic <span class="html-italic">L. regale</span> in the second year after injection, respectively. <span class="html-italic">T</span>-test was used to reveal the statistical difference of <span class="html-italic">DsRed2</span> expression level between wild type and transgenic plants (** <span class="html-italic">p</span> &lt; 0.01).</p> Full article ">
18 pages, 4733 KiB  
Systematic Review
Meta-Analysis of the Impact of Far-Red Light on Vegetable Crop Growth and Quality
by Minggui Zhang, Jun Ju, Youzhi Hu, Rui He, Jiali Song and Houcheng Liu
Plants 2024, 13(17), 2508; https://doi.org/10.3390/plants13172508 - 6 Sep 2024
Viewed by 952
Abstract
Far-red lights (FRs), with a wavelength range between 700 and 800 nm, have substantial impacts on plant growth, especially horticultural crops. Previous studies showed conflicting results on the effects of FRs on vegetable growth and quality. Therefore, we conducted a meta-analysis on the [...] Read more.
Far-red lights (FRs), with a wavelength range between 700 and 800 nm, have substantial impacts on plant growth, especially horticultural crops. Previous studies showed conflicting results on the effects of FRs on vegetable growth and quality. Therefore, we conducted a meta-analysis on the influence of FRs on vegetable growth, aiming to provide a comprehensive overview of their effects on the growth and nutritional indicators of vegetables. A total of 207 independent studies from 55 literature sources were analyzed. The results showed that FR treatment had significant effects on most growth indicators, including increasing the fresh weight (+25.27%), dry weight (+21.99%), plant height (+81.87%), stem diameter (+12.91%), leaf area (+18.57%), as well as reducing the content of chlorophyll (−11.88%) and soluble protein (−11.66%), while increasing soluble sugar content (+19.12%). Further subgroup analysis based on various factors revealed significant differences in the effects of FR on different physiological indicators, such as FR intensity, plant species, duration of FR exposure, and the ratio of red light to FR. In general, moderate FR treatment is beneficial for vegetable growth. This study provides important references and guidelines for optimizing the application of FR in the future. Full article
(This article belongs to the Special Issue Light and Plant Nutrition)
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<p>The forest plot displays the response ratios of various physiological indicators of vegetables to FR (FFT: First Flowering Time. FDW: Fruit Dry Weight. FFW: Fruit Fresh Weight. DPPH: DPPH Radical Scavenging Rate. VC: Vitamin C. MDA: Malondialdehyde. CAT: Catalase. POD: Peroxidase. SOD: Superoxide Dismutase. SP: Soluble Protein. SS: Soluble Sugar. Caro: Carotenoids. Chlo: Chlorophyll. SPAD: Relative Chlorophyll Content. Lue: Light Energy Utilization Efficiency. SLA: Specific Leaf Area. LA: Leaf Area. LN: Leaves Number. HL: Hypocotyl Length. SD: Stem Diameter. PH: Plant Height. DW: Dry Weight. FW: Fresh Weight), along with the main analysis parameters. The numbers in parentheses refer to the sample sizes of independent studies. The parameters of the meta-analysis include the size of the effect size, 95% CI, the percentage change rate, and the zero-hypothesis test (two−tailed <span class="html-italic">p</span>-value).</p> Full article ">Figure 2
<p>Subgroup Analysis Forest Plot illustrates the overall effect size of the response ratio of FR on the fresh weight of vegetables and the main analysis parameters. The numbers in parentheses refer to the sample size of independent studies. Parameters for meta-analysis include the effect size, 95% confidence interval (CI), percentage change rate, Q-test <span class="html-italic">p</span>-value for heterogeneity (P<sub>-hetero</sub>), I<sup>2</sup> statistic, and the null-hypothesis test (two-sided <span class="html-italic">p</span>-value).</p> Full article ">Figure 3
<p>Subgroup Analysis Forest Plot illustrates the overall effect size of the response ratio of FR on the dry weight of vegetables and the main analysis parameters. The numbers in parentheses refer to the sample size of independent studies. Parameters for meta-analysis include the effect size, 95% confidence interval (CI), percentage change rate, Q-test <span class="html-italic">p</span>-value for heterogeneity (P<sub>-hetero</sub>), I<sup>2</sup> statistic, and the null-hypothesis test (two-sided <span class="html-italic">p</span>-value).</p> Full article ">Figure 4
<p>Subgroup Analysis Forest Plot illustrates the overall effect size of the response ratio of FR on the leaf area of vegetables and the main analysis parameters. The numbers in parentheses refer to the sample size of independent studies. Parameters for meta-analysis include the effect size, 95% confidence interval (CI), percentage change rate, Q-test <span class="html-italic">p</span>-value for heterogeneity (P<sub>-hetero</sub>), I<sup>2</sup> statistic, and the null-hypothesis test (two-sided <span class="html-italic">p</span>-value).</p> Full article ">Figure 5
<p>Subgroup Analysis Forest Plot illustrates the overall effect size of the response ratio of FR on the plant height of vegetables and the main analysis parameters. The numbers in parentheses refer to the sample size of independent studies. Parameters for meta-analysis include the effect size, 95% confidence interval (CI), percentage change rate, Q-test <span class="html-italic">p</span>-value for heterogeneity (P<sub>-hetero</sub>), I<sup>2</sup> statistic, and the null-hypothesis test (two-sided <span class="html-italic">p</span>-value).</p> Full article ">Figure 6
<p>Subgroup Analysis Forest Plot illustrates the overall effect size of the response ratio of FR on the chlorophyll content of vegetables and the main analysis parameters. The numbers in parentheses refer to the sample size of independent studies. Parameters for meta-analysis include the effect size, 95% confidence interval (CI), percentage change rate, Q-test <span class="html-italic">p</span>-value for heterogeneity (P<sub>-hetero</sub>), I<sup>2</sup> statistic, and the null-hypothesis test (two-sided <span class="html-italic">p</span>-value).</p> Full article ">Figure 7
<p>The global geographical distribution of the 55 research articles included in the analysis.</p> Full article ">Figure 8
<p>Different proportions of research on vegetables from various families.</p> Full article ">Figure 9
<p>The PRISMA flow diagram systematically reviews the process of literature screening for meta-analysis. It illustrates the number of identified records, the number of included and excluded records, as well as the reasons for exclusion.</p> Full article ">Figure 10
<p>The cumulative number of articles published annually from January 2014 to April 2024.</p> Full article ">
12 pages, 1555 KiB  
Article
The Hole Truth: Why Do Bumble Bees Rob Flowers More Than Once?
by Judith L. Bronstein, Goggy Davidowitz, Elinor M. Lichtenberg and Rebecca E. Irwin
Plants 2024, 13(17), 2507; https://doi.org/10.3390/plants13172507 - 6 Sep 2024
Viewed by 969
Abstract
Primary nectar-robbers feed through holes they make in flowers, often bypassing the plant’s reproductive organs in the process. In many robbed plants, multiple holes are made in a single flower. Why a flower should be robbed repeatedly is difficult to understand: a hole [...] Read more.
Primary nectar-robbers feed through holes they make in flowers, often bypassing the plant’s reproductive organs in the process. In many robbed plants, multiple holes are made in a single flower. Why a flower should be robbed repeatedly is difficult to understand: a hole signals that a nectar forager has already fed, which would seem likely to predict low rewards. We tested three explanations for this pattern in Corydalis caseana (Fumariaceae), a bumble bee pollinated and robbed plant: (1) multiple holes appear only after all flowers have been robbed once; (2) individual foragers make multiple holes during single visits; and (3) it is more profitable for bees to rob older flowers, even if they have already been robbed. We tested these hypotheses from 2014 to 2016 in a Colorado, USA population using data on robbing rates over time, floral longevity, nectar accumulation in visited and unvisited flowers, and the accumulation of robbing holes across the life of flowers. Multiple holes were already appearing when two-thirds of flowers still lacked a single hole, allowing us to reject the first hypothesis. The second hypothesis cannot offer a full explanation for multiple robbing holes because 35% of additional holes appeared in flowers one or more days after the first hole was made. Repeated sampling of bagged and exposed inflorescences revealed that flowers filled at a constant rate and refilled completely after being drained. Consequently, young flowers are of consistently low value to foragers compared to older flowers even if they had previously been robbed, consistent with the third hypothesis. While further studies are needed, these results offer a simple explanation for the paradoxical clustering of nectar-robbing damage in this and possibly other plant species. Full article
(This article belongs to the Special Issue Plant–Animal Interactions: Exploring Costs and Benefits in Highly Conditional Relationships)
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Figure 1

Figure 1
<p>Nectar volumes in flowers of known age. Sample sizes range from 7 flowers/day–44 flowers/day. We used day means (±SEM) to calculate the best fit models, even though the data are shown without these models. (<b>A</b>) Volumes in bagged flowers allowed to accumulate nectar for the shown number of days. The line is best described by a sigmoid curve (BIC = −4.75, R2 = 0.998). (<b>B</b>) Volumes in open flowers of known age (note the difference in scale). The best fit model is a quadratic polynomial (BIC = −19.6, R2 = 0.81). Significance among days in 1B was tested with a non-parametric Wilcoxon/Kruskal–Wallis rank sum test (<span class="html-italic">p</span> = 0.0071), and a post hoc test among days using a Tukey–Kramer HSD (alpha = 0.05) as indicated by the lower-case letters above the symbols. Sample sizes for each day are above the abscissa.</p> Full article ">Figure 2
<p>Nectar refilling dynamics. Control Day 2 flowers were bagged before opening, allowed to open in the bag, then sampled for nectar with a 2 μL capillary tube after two days. Total Refill flowers were similarly bagged before opening, but nectar was drained twice, on Day 1 and Day 2; the total amount of nectar they produced is shown. The volumes were not statistically different (two-sided <span class="html-italic">t</span>-test assuming unequal variances, t = 0.327, df = 36.36, <span class="html-italic">p</span> = 0.7458), indicating that flowers completely fill with nectar once drained.</p> Full article ">Figure 3
<p>The proportion of holes in haphazardly selected <span class="html-italic">C. caseana</span> flowers in (<b>A</b>) July of 2014 (n = 4939 flowers), (<b>B</b>) 2015 (n = 931 flowers), and (<b>C</b>) 2016 (n = 1804 flowers).</p> Full article ">Figure 4
<p>Proportion of sampled flowers on each date that were robbed (solid line) and the proportion of these that had more than one robbing hole (dashed line) in (<b>A</b>) 2014, (<b>B</b>) 2015, and (<b>C</b>) 2016.</p> Full article ">
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