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Plant Structures

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Structural Insights into ABA Signaling Regulation

PYL2, PYL1, and HAB1 were expressed as H6-GST or H6Sumo fusion proteins in E. coli. Proteins were purified by Ni-NTA chromatography, followed by proteolytic release of tags and size-exclusion chromatography. For formation of PYL2-ABA and HAB1-PYL2-ABA complexes, ABA was mixed with PYL2 and HAB1-PYL2 at 5:1 ratios. Crystals were grown by vapor diffusion and diffraction data were collected from cryo-protected crystals at beamlines 21-ID-D and 21-ID–F at the Advanced Photon Source at Argonne National Laboratories. Structures were solved by molecular replacement in PHASER 26 (link) using the structure of the plant START protein Bet v 1 as model for PYL2 and the structure of the human PP2C PPM1B as model for HAB1. Models were manually fitted using O and Coot 27 (link),28 (link) and further refined using CNS and Refmac5 29 (link),30 (link).
Mutant proteins were expressed as H6GST-fusion proteins and purified by glutathione sepharose chromatography. Protein-protein interactions were determined by luminescence proximity AlphaScreen assay and by yeast two-hybrid assay. Biotinylated HAB1 for the luminescence proximity assay was generated by in vivo biotinylation of an avitag-HAB1 fusion protein. ABA binding was determined by scintillation proximity assay using ³H-labelled ABA. HAB1 phosphatase activity was measured by phosphate release from a SnRK2.6 phosphoprotein (Fig. 1-5) or from a generic pNPP phosphate substrate (Fig. 6b).
For transgenic studies, wildtype and mutant 35S::GFP-PYR1 constructs were transformed by the floral dip method into pyr1/pyl1/pyl2/pyl3 quadruple mutants. Mutant complementation of GFP⁺ seedlings was assayed by root length measurements. The ABA signal transduction pathway was reconstituted in protoplasts by transient transfection of PYL2, PP2C, SnRK2.6, and ABF2 expression plasmids. Activation of an ABA-inducible CBF3promoter-LUC reporter by PYL2 mutant proteins was determined by luciferase assays normalized for β-glucuronidase activity from a UQ10-GUS reporter. Full Methods accompany this paper at www.nature.com/nature.

Melcher K., Ng L.M., Zhou X.E., Soon F.F., Xu Y., Suino-Powell K.M., Park S.Y., Weiner J.J., Fujii H., Chinnusamy V., Kovach A., Li J., Wang Y., Li J., Peterson F.C., Jensen D.R., Yong E.L., Volkman B.F., Cutler S.R., Zhu J.K, & Xu H.E. (2009). A Gate-Latch-Lock Mechanism for Hormone Signaling by Abscisic Acid Receptors. Nature, 462(7273), 602-608.

Publication 2009

4-aminophenylphosphate Animals, Transgenic beta-Glucuronidase Biological Assay Biotinylation Chromatography Chromatography, Agarose Diffusion Escherichia coli Proteins Gel Chromatography Generic Drugs Glutathione Homo sapiens Luciferases Luminescent Measurements Mutant Proteins myotrophin Phosphates Phosphoproteins Phosphoric Monoester Hydrolases Plant Roots Plant Structures Plasmids Proteins Proteolysis Protoplasts Seedlings Signal Transduction Pathways Transfection Transients Yeast Two-Hybrid System Techniques

Structural Insights into ABA Signaling Regulation

Publication 2009

Stem-Loop Small RNA Prediction

The stem-loop smRNA prediction function takes input smRNA sequences in FASTA or plain text format. For each query smRNA, the software finds its perfectly matched genomic origins, and extracts various lengths of upstream and downstream sequences as precursors, assuming that the smRNA may originate from either the 5′ or 3′ end of precursors, with 10 nt extension at one end of the precursor each time, till reaching the user defined precursor length. The secondary structures of the extracted precursor sequences are then evaluated by the MFOLD program (20 (link)). Precursor sequences with stem-loop structure as the minimal free energy folding form and the corresponding query smRNA will be selected and reported in the result pages (Fig. 1a), either in html or text format. If the conservation analysis function of smRNA sequences is selected, the cross species conservation status of smRNAs will be analyzed by aligning the query small sequences to eight selected plant genomes using the BLAST program (allowing up to two mismatches in the smRNA sequences), and the ClustalW (21 (link)) alignment of the identified smRNA homologous sequences will be included in the outputs (Fig 1b). The repetitive sequence regions of the 26 preloaded genomes were identified by the RepeatMasker program (http://repeatmasker.org) and stored in the background database. Every genomic locus of the query sequences will be searched against the database to identify repeat sequence originated smRNAs. Parameters for users to adjust include smRNA conservation analysis, the minimal and maximal numbers of mismatched nucleotides within the query smRNA sequences in the obtained precursor structures, the maximal lengths of extracted precursors, and the permission of large loop sequence in the qualified precursors. Although the precursor structures of most canonical plant miRNAs are very short and well paired, there are still some with large bulge or hairpin loops, such as ath-MIR393a and ath-MIR167d (22 (link),23 (link)). Enable the “Retain large loop small RNA” function will include precursors with large loops in the prediction results.
Figure 1.

Output of stem-loop smRNA prediction. (A) The genomic mapping and stem-loop precursor prediction of the query smRNAs, detailed information of genomic location, precursor sequence, secondary structure of each predicted stem-loop smRNA loci and their folding energies are included. Sequence in red and capitalized letters is the query smRNA sequence; (B) shows the conservation status of the smRNA in 8 plant species; (C) shows the normalized reads of a given smRNA in smRNA biogenesis mutants and AGO-associated libraries.

Wu H.J., Ma Y.K., Chen T., Wang M, & Wang X.J. (2012). PsRobot: a web-based plant small RNA meta-analysis toolbox. Nucleic Acids Research, 40(Web Server issue), W22-W28.

Publication 2012

Anabolism Genome Genome, Plant MicroRNAs Nucleotides Plants Plant Structures Repetitive Region Sequence Alignment Sequence Analysis Stem, Plant Stem-Loop Sequence

Parrot Foraging Behavior and Seed Dispersal

When parrots were observed foraging, their number, food handling behavior, and the consumed part of each plant species were recorded, both within and outside transects conducted to determine their abundance. In the case of fruits, we noted whether parrots handled and consumed pulp of ripe or unripe fruits and their mature or immature seeds, respectively, and whether parrots dropped each food type beneath the canopy of food plants. The diameter of the smallest and largest axis (measured with callipers to the nearest mm) of a sample of ripe fruits and their seeds, as well as the number of seeds per fruit, was recorded in the field. After each feeding observation, we attempted to confirm what parrots were eating and wasting by searching for food remains on the ground beneath foraging sites.
When foraging parrots were observed departing from food plants with fruits in the beak or feet, we followed them visually with binoculars to attempt to determine whether they dropped ripe fruits and defleshed mature seeds during flight or at subsequent stopovers at foraging and perching sites. We measured the approximate distances moved from the mother plant with a laser rangefinder incorporated into the binoculars (see Tella et al. 2015). Some distances recorded should be considered conservative estimates when flying parrots were out of sight in the forest while transporting fruits or seeds.
To determine whether the fruit‐wasting behavior of parrots (e.g., Symes and Perrin 2003) facilitated the availability of seeds and other plant structures used as food by secondary seed dispersers and other organisms, we recorded the presence and abundance of entire ripe fruits, intact mature seeds separated from the pulp, and other vegetable matter dropped by parrots beneath the canopy of food plants. The identity of potential secondary dispersers was opportunistically recorded by direct observation and by recording the presence of feces containing seeds beneath and at a short distance from parent plants.

Blanco G., Hiraldo F., Rojas A., Dénes F.V, & Tella J.L. (2015). Parrots as key multilinkers in ecosystem structure and functioning. Ecology and Evolution, 5(18), 4141-4160.

Publication 2015

Beak Dental Pulp Epistropheus Feces Food Foot Forests Fruit Mothers Parent Parrots Plant Embryos Plants Plants, Edible Plant Structures Vegetables Vision

Molecular Docking of Plant Metabolites

Docking was performed with the AutoDock Vina package [88 (link)]. The AutoDock Tools 1.5.7 module was used to prepare and analyse the computational calculations. After optimization, the structures of the plant metabolites were positioned in the central portion of the respective catalytic site of each selected target (Cys145 to Mpro, Asp618 to RdRp, Tyr264 to PLpro, His250 to NSP15, Gln493 to RDB Spro and Arg273 to ACE-2). Gasteiger charges and polar hydrogens, required for potential calculations, were added after removal of water molecules, drugs and/or artefacts from the target structures [89 (link)]. The targets macromolecules structures were kept rigid, while the ligands did not have their mobility restricted, remaining free. The size of the grid box was set to 22.5 Å for each axis. The number of modes was set to 50, and the exhaustiveness was set to 24. The conformations of the best interaction energy of the ‘ligand + receptor’ complexes identified in molecular docking were selected based on free energy of binding, by visual inspection and analysis of residues that best interacted with the ligand [19 (link),20 (link),90 (link)]. Molecular analyses and complex representations were obtained using the UCSF Chimera package [91 (link)] and PoseView [92 (link)].

Lopes A.J., Calado G.P., Fróes Y.N., de Araújo S.A., França L.M., Paes A.M., de Morais S.V., da Rocha C.Q, & Vasconcelos C.C. (2022). Plant Metabolites as SARS-CoV-2 Inhibitors Candidates: In Silico and In Vitro Studies. Pharmaceuticals, 15(9), 1045.

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Publication 2022

Catalytic Domain Chimera Epistropheus Hydrogen Ligands Muscle Rigidity Pharmaceutical Preparations Plant Structures Range of Motion, Articular

Most recents protocols related to «Plant Structures»

Evaluating Island Plant-Pollinator Community Structure

Plant-pollinator community structure and network architecture on islands might differ from the mainland reference sites both as a result of fragmentation processes and of biases in sampling effort. The purpose of the null model is to determine whether variation in plant-pollinator community structure and network architecture on islands is significantly greater (or less) than expected from a simple ‘passive sampling effect’ from the continuous mainland reference pool (10.6084/m9.figshare.20477889). In selecting the reference pool, we follow the principle of Gotelli and Graves (1996)⁸² that “A null model is a pattern-generating model that is based on randomization of ecological data..[…].. designed to produce a pattern that would be expected in the absence of a particular ecological mechanism”. In our case, we are interested in the null pattern of community diversity and network structure that would be expected in the absence of fragmentation and reduction in island area. Given that we do not have ‘pre-fragmentation’ data to directly test re-assembly trajectories through time on each island, the combined set of sampling plots from the adjacent ‘unfragmented’ mainland is the most appropriate reference pool available for the null draws. To do this, we compiled plant-pollinator interaction data from all sampling transects on the mainland edge and interior, respectively, then used these as our expected ‘reference’ pools. From edge and interior reference pools (separately), we used two methods to simulate ‘null communities’ and ‘null networks’: (1) Null model I: a random draw (with replacement) of the same number of transects from the mainland as observed on each of the 41 sampled islands (that is, constraining the number of sampling transects used to acquire a null estimate of floral resources, plant richness, pollinator richness and pollinator abundance); (2) Null model II: a random draw (with replacement) of the same number of pairwise interactions, while ensuring the same numbers of plant and pollinator species were selected as those observed on each of the 41 sampled islands (that is, constraining both network abundance and network size) (10.6084/m9.figshare.20477889). The concepts, step-by-step procedures and R code for the null-draw methods are presented in Supplementary Methods 5 and Code availability.
We calculated the standardized effect size, that is, SES = (α_obs − α_null) / s.d.(α_null) as a measure of the magnitude and direction of the difference between the observed and the null values for each island^{83 (link)}. A positive or negative value of SES indicates that the observed value is above or below the mean of the null distribution, respectively. We used approximate statistical significance at the 5% level for a two-tailed test when estimating significance. If the observed values differed significantly from the null model values, this indicates that the observed values showed non-random assembly trajectories as a result of forest area loss and fragmentation, over and above any stochastic effects due to confounding bias in sampling effort between islands. In contrast, if the observed values were not significantly different from the null model, variation in diversity or network structure was considered indistinguishable from the stochastic biases that might be expected from passive sampling effects.

Ren P., Didham R.K., Murphy M.V., Zeng D., Si X, & Ding P. (2023). Forest edges increase pollinator network robustness to extinction with declining area. Nature Ecology & Evolution, 7(3), 393-404.

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Publication 2023

Plants Plant Structures

Structural Equation Modeling of Forest Fragmentation Impacts

We used ‘piecewise’ SEM to partition the direct vs indirect effects of forest area loss and fragmentation (decreased area, proximity to edge and increased isolation) on plant-pollinator community structure (floral resources, plant richness, pollinator abundance and pollinator richness) and network architecture (relative connectance, nestedness, modularity and robustness). The hypothesized causal logic underpinning each path is presented in Supplementary Methods 4. The piecewise SEM consists of a series of separate linear models with local rather than global estimation of parameters and combines these into a single directed acyclic graph^{75 (link)}, which is particularly suited to hierarchical nested data structures and non-normal error distributions in models. Moreover, local estimation allows greater robustness in fitting smaller data sets^{76 (link)}, and we follow the recommendation of Grace et al.⁷⁷ in ensuring that we have more than five samples per variable estimated in the model. We tested the causal structure of the hypothesized model (Fig. 2) using ‘piecewiseSEM’ v.2.1.0, which extends SEM to non-normal distribution models^{76 (link)}. Specifically, models testing the direct and indirect effects of fragmentation on plant richness, pollinator richness and abundance used Poisson generalized linear mixed-effects models (GLMMs) in ‘lme4’ v.1.1–23⁷⁸, while fragmentation effects on floral resources and the four network attributes were tested with linear mixed-effects models (LMMs). We used ln-transformation of forest area and distance of isolation to linearize relationships. Models contained a random effect for island identity to account for non-independence of paired edge versus interior transects sampled within each island. Overall model fit was tested using Shipley’s d-separation test via a Fisher’s C statistic and χ²-based P value^{75 (link),79 (link)}. We selected a ‘final’ SEM by sequentially removing model predictors (direct paths) with the lowest AIC value until all remaining paths were significant and the ‘global’ SEM P value was non-significant (that is, no remaining ‘missing’ paths). Direct, indirect and total effects for the SEM were calculated using the ‘semEff’ package v.0.6.0⁸⁰, with effect sizes adjusted for multicollinearity among predictors^{81 (link)}. The 95% confidence interval for effects was calculated using 1,000 bootstrapped estimates for each response. Model-predicted total effects are presented using partial regression coefficients calculated using the ‘predEff’ function in the ‘semEff’ package.

Ren P., Didham R.K., Murphy M.V., Zeng D., Si X, & Ding P. (2023). Forest edges increase pollinator network robustness to extinction with declining area. Nature Ecology & Evolution, 7(3), 393-404.

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Publication 2023

6H,8H-3,4-dihydropyrimido(4,5-c)(1,2)oxazin-7-one Forests isolation Plants Plant Structures

Plant Functional Trait Survey Across Asia

The dataset of plant functional traits was collected during a large-scale field survey in 72 typical natural ecosystems using a unified sampling standard from 2013–2019. These sites include evergreen broadleaf forests, deciduous broadleaf forests, evergreen coniferous forests, deciduous coniferous forests, shrublands, meadows, steppes, and sparse grasslands, spanning broad environmental gradients and with high environmental heterogeneity. Standardized sampling and measurement protocols were applied to each vegetation and soil surveys. Specifically, the surveyed sites extended from 18.74°N to 53.33°N and 78.47°E to 128.89°E, with mean annual temperatures ranging from −3.8 °C to 22.2 °C, and mean annual precipitation ranging from 32–1942 mm. Plant samples were collected using the quadrat method (30 m × 40 m for the forest, 10 m × 10 m for shrubland, and 1 m × 1 m for grassland) to investigate the community structure during the plant growth peak period from July to August (see Text S3 for more information on the sampling protocol). In each plot within a site, key plant community structure variables were measured, including species identity, species number, plant height, diameter at breast height (DBH; basal stem diameter for shrubs) for all woody plants with DBH ≥ 1 cm, and aboveground biomass for herbaceous species. The measured individual-level functional traits for woody and herbaceous plants included leaf area (LA, cm²), leaf dry mass (LM, g), specific leaf area (SLA, cm²/g), leaf nitrogen concentration (LNC, mg/g), and leaf phosphorus concentration (LPC, mg/g), closely related to plant photosynthesis and growth^{29 (link),49 (link)} (Text S4). Functional traits were divided into size traits, reflecting plant size and light competitiveness, and economic traits, reflecting leaf photosynthetic capacity and nutrient economic^{40 (link),50 (link)}. All of these traits selected in this study are closely related to the plant light competitiveness and ecosystem photosynthetic capacity. Soil samples from the 0–10 cm soil layer were collected via auger boring for analysis of total soil carbon (%), nitrogen (%), phosphorus (%), and soil pH (Text S5). For further details regarding plot setting, plant trait measurement, and soil analysis, see Text S2–4, and other sources published by this team^{51 (link)–53 (link)}.

Yan P., He N., Yu K., Xu L, & Van Meerbeek K. (2023). Integrating multiple plant functional traits to predict ecosystem productivity. Communications Biology, 6, 239.

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Publication 2023

Breast Carbon Ecosystem Forests Genetic Heterogeneity Light Nitrogen Nutrients Phosphorus Photosynthesis Plant Leaves Plants Plant Structures Stem, Plant Tracheophyta

Temperate Desert Ecosystem Fencing Study

The study area was located at the Linze Inland River Basin Integrated Research Station (LIRBIRS, 39° 21’ N, 100° 07’ E, 1,367 m above sea level) of the Ecosystem Research Network of the Chinese Academy of Sciences, in Pingchuan Town, Linze County, in the central part of the Hexi Corridor, Gansu Province, China (Figure 1A). The study area has a temperate continental desert climate, characterized by sparse precipitation throughout the year, which is concentrated in summer, as well as a dry climate, long day lengths, and intense solar radiation; the annual precipitation is 124.3 mm and the average annual temperature is 7.6°C. The annual average wind speed is 3.2 m·s^-1, the maximum wind speed is 21 m·s^-1, and the prevailing wind direction is northwesterly (Zhao et al., 2003 (link)). The precipitation and maximum wind speed dynamics during the fencing period (Figure 1C), which were determined using meteorological data obtained from the desert ecosystem meteorological long-term experiment sample sites at LIRBIRS, indicated fluctuations but no significant annual increases or decreases. The study area is dominated by semi-fixed dunes with a simple plant community structure and few species. Xerophytes and semi-shrubby vegetation dominate. The xerophytes and ultra-xerophytes do not typically include short-lived or annual plants. Representative plants include Reaumuria songorica, Nitraria tangutorum, Calligonum chinense, Haloxylon ammodendron, and Agriophyllum squarrosum, which exhibit a mixed spatial distribution, constituting a unique patch vegetation pattern. The dominant herb species are Artemisia scoparia, Suaeda glauca, Halogeton arachnoideus, and Eragrostis minor, with sporadic Zygophyllum kansuense and Allium mongolicum.

Zhang Y., Wang G., Gou Q., Zhang Y., Liu J, & Gao M. (2023). Succession of a natural desert vegetation community after long-term fencing at the edge of a desert oasis in northwest China. Frontiers in Plant Science, 14, 1091446.

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Publication 2023

Allium Artemisia Chinese Climate Desert Climate Ecosystem Eragrostis Halogeton Plants Plant Structures Rivers Scoparia Solar Energy Suaeda Wind Zygophyllum

Comparative Analysis of Aconitum Chloroplast Genomes

The cp genomes of 6 reported Aconitum species and 1 exogenous species were loaded from the NCBI website, which are, A. flavum (MW839582.1), A. pendulum (MW839578.1), A. brachypodum (NC_041579.1), A. vilmorinianum (MH063436.1), A. kusnezoffii (MK253471.1), A. carmichaelii (KY407560.1), and D. yunnanense (MW246158.1). CGView software was used to evaluate the cp genome structures of the eight plants [39 (link)]. Mauve v2.3.1 was used to analyze at the homology and covariance of cp sequences [40 (link)]. For broad comparison of homologous gene sequences from different plants, the MAFFT v7.310 (automatic mode) [41 (link)] was employed. Nucleotide diversity (PI) values for each gene were calculated using DNAsp v5.0 [42 (link)]. The IR, SSC, and LSC region boundary information was visualized using the SVG package in Perl. MAFFT v7.310 software was used to compare gene sequences, and Ka/Ks Calculator v2.0 software was utilized to calculate the Ka/Ks values of the genes.

Yanfei N., Tai S., Chunhua W., Jia D, & Fazhong Y. (2023). Complete chloroplast genome sequences of the medicinal plant Aconitum transsectum (Ranunculaceae): comparative analysis and phylogenetic relationships. BMC Genomics, 24, 90.

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Publication 2023

Aconitum Genes Genome Nucleotides Plants Plant Structures

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More about "Plant Structures"

Plant Structures refer to the various anatomical components that make up the physical form and internal organization of plants.
This includes the root system, stem, leaves, flowers, fruits, and other specialized botanical structures.
Understanding plant morphology and anatomy is crucial for research in areas like plant physiology, developmental biology, and ecology.
Researchers can leverage the PubCompare.ai platform to optimize their plant structure research protocols, enhance reproducibility, and identify the best techniques and products for their studies.
By utilizing AI-driven comparisons of relevant literature, preprints, and patents, PubCompare.ai streamlines the plant structure research process and boosts the efficacy of these important investigations.
Synonyms and related terms include plant anatomy, plant parts, plant organs, botanical structures, and phytomorphology.
Abbreviations like SEM (Scanning Electron Microscope), TEM (Transmission Electron Microscope), and EDS (Energy-Dispersive X-ray Spectroscopy) are also relevant to plant structure research.
Key subtopics encompass the study of root systems, stems, leaves, flowers, fruits, and other specialized structures like trichomes, stomata, and vascular tissues.
Techniques like histology, microscopy (e.g., FEI Quanta 200 FEG SEM, H-7650 TEM), and chemical analysis (e.g., Ruthenium(III) chloride hydrate) are commonly employed to investigate plant structures.
Statistical analysis tools like SPSS 22.0 for Windows and software like MolProbity can also play a role in the quantitative assessment and modeling of plant structures.
Specialized imaging platforms such as Maestro and SMZ800N microscopes further enhance the visualization and analysis capabilities for plant structure research.