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15 pages, 3548 KiB  
Article
Efficient Removal of Lead, Cadmium, and Zinc from Water and Soil by MgFe Layered Double Hydroxide: Adsorption Properties and Mechanisms
by Hua Deng, Shuyun Zhang, Qiuyan Li, Anyu Li, Weixing Gan and Lening Hu
Sustainability 2024, 16(24), 11037; https://doi.org/10.3390/su162411037 - 16 Dec 2024
Abstract
Both biochar and layered double hydroxide (LDH) have drawbacks in regard to the removal of heavy metals. The combined application of biochar and LDH not only solved the problem of the easy agglomeration of LDH but also effectively improved the heavy metal adsorption [...] Read more.
Both biochar and layered double hydroxide (LDH) have drawbacks in regard to the removal of heavy metals. The combined application of biochar and LDH not only solved the problem of the easy agglomeration of LDH but also effectively improved the heavy metal adsorption capacity of biochar. In this work, a MgFe–LDH banana straw biochar composite (MgFe–LDH@BB), with a regular hydrotalcite structure, was synthesized by employing a simple hydrothermal method. The composite showed an ultra-high adsorption capacity for lead (Pb), cadmium (Cd), and zinc (Zn) in water. A series of experiments were conducted to investigate the adsorption characteristics of MgFe–LDH@BB. At pH = 6.0, MgFe–LDH@BB demonstrated the effective adsorption of Pb, Cd, and Zn. In addition, the results showed that the adsorption of Pb, Cd, and Zn by MgFe–LDH@BB was rapid and conformed to pseudo-second-order kinetic and Langmuir models, indicating single-layer chemical adsorption. The maximum adsorption capacity of MgFe–LDH@BB for Pb, Cd, and Zn was 1112.6, 869.6, and 414.9 mg·g−1, respectively. Moreover, the adsorption mechanisms of MgFe–LDH@BB mainly included metal hydroxide/carbonate precipitation, complex formation with hydroxyl groups, and ion exchange. Meanwhile, MgFe–LDH@BB had the ability to immobilize heavy metals in soil. The surface-rich functional groups and cation exchange promoted the transformation of active heavy metal ions into a more stable form. Full article
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<p>Isotherms fitting results (<b>a</b>–<b>c</b>) and kinetics (<b>d</b>) fitting results of Pb, Cd, and Zn adsorption by MgFe–LDH@BB.</p>
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<p>Effect of pH on Pb, Cd, and Zn: (<b>a</b>) adsorption by MgFe–LDH@BB; and adsorption competition between heavy metal ions (<b>b</b>). The letters a–c in the figure mean differences in adsorption at different pH levels.</p>
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<p>XRD pattern (<b>a</b>), FTIR spectra (<b>b</b>), BET specific surface area (<b>c</b>), SVM (<b>d</b>), and TEM images (<b>e</b>,<b>f</b>) of MgFe–LDH@BB.</p>
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<p>SEM–EDS images of MgFe–LDH@BB before (<b>a</b>) and after adsorption Pb (<b>b</b>).</p>
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<p>XPS spectra before and after adsorption of Pb, Cd, and Zn by MgFe–LDH@BB (<b>a</b>–<b>d</b>) represent C1s, O1s, Mg1s, and Fe2p.</p>
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<p>Fraction of Pb in soil (<b>a</b>); HCl extraction rate of Pb after 1, 7, 15, 30, 60, and 90 d immobilization in soil (<b>b</b>). The letters a–d in the figure mean differences in heavy metal fractions in soil at different incubation times.</p>
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10 pages, 826 KiB  
Article
Complete Genome Sequence of a Novel Azospirillum Strain TA Isolated from Western Siberia Chernevaya Taiga Soil
by Mikhail Rayko, Irina Kravchenko and Alla Lapidus
Microorganisms 2024, 12(12), 2599; https://doi.org/10.3390/microorganisms12122599 - 16 Dec 2024
Abstract
A whole genome sequence of a new strain of the nitrogen-fixing bacterium Azospirillum doebereinerae, known for its diverse plant growth-promoting bacteria (PGPB), was obtained for the first time. The strain, designated Azospirillum doebereinerae AT, was isolated during a soil analysis in the [...] Read more.
A whole genome sequence of a new strain of the nitrogen-fixing bacterium Azospirillum doebereinerae, known for its diverse plant growth-promoting bacteria (PGPB), was obtained for the first time. The strain, designated Azospirillum doebereinerae AT, was isolated during a soil analysis in the Chernevaya taiga of Western Siberia, a unique and fertile forest ecosystem known for its diverse plant growth-promoting bacteria (PGPB). The A. doebereinerae genome under study is fully assembled into seven circular molecules, none of which are unequivocally plasmids, with a total length of 6.94 Mb and a G + C content of 68.66%. A detailed phylogenomic analysis confirmed its placement within the genus Azospirillum, specifically closely related to A. doebereinerae GSF71T. Functional annotation revealed genes involved in nitrogen metabolism, highlighting the potential of strain TA as a biofertilizer and plant growth-promoting agent. The findings contribute to our understanding of the genomic diversity and metabolic potential of the Azospirillum genus, and they are of interest for further study in the field of comparative bacterial genomics, given the strain’s multi-chromosomal genome structure. Full article
(This article belongs to the Section Environmental Microbiology)
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<p>Phylogenetic tree highlighting the position of strain TA relative to other type strains within the genus <span class="html-italic">Azospirillum</span>. The strains are shown along with the corresponding GenBank accession numbers of their 16S rRNA genes. The sequences were aligned using MAFFT, and the maximum-likelihood tree was constructed based on the Tamura–Nei model using IQ-TREE 2.</p>
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<p>Phylogenomic tree of all available complete <span class="html-italic">Azospirillum</span> genomes. A total of 124 single-copy orthologs were obtained using BUSCO5, aligned individually using MAFFT and concatenated into a super matrix. The maximum-likelihood tree was constructed based on the Tamura–Nei model using IQ-TREE 2, with 1000 ultrafast bootstrap replications.</p>
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17 pages, 4631 KiB  
Article
Effects of Soil Properties and Altitude on Phylogenetic and Species Diversity of Forest Plant Communities in Southern Subtropical China
by Guangyu Xue, Ji Zeng, Jianyou Huang, Xuguang Huang, Fujiang Liang, Junduo Wu and Xueping Zhu
Sustainability 2024, 16(24), 11020; https://doi.org/10.3390/su162411020 - 16 Dec 2024
Viewed by 53
Abstract
The altitudinal distribution pattern of biodiversity is a hot topic in ecological research. This study specifically aims to investigate how altitude influences the spatial distribution of species and phylogenetic and functional diversity within plant communities. By examining three range-gradient communities of Daqing Mountain-Community [...] Read more.
The altitudinal distribution pattern of biodiversity is a hot topic in ecological research. This study specifically aims to investigate how altitude influences the spatial distribution of species and phylogenetic and functional diversity within plant communities. By examining three range-gradient communities of Daqing Mountain-Community I (0–300 m), Community II (300–600 m), and Community III (600–900 m), we explore the interrelationship between species diversity, phylogenetic indices, and environmental drivers (altitude, soil physical properties, and chemical properties). We found (1) a correlation between species diversity and phylogenetic structure in Daqing Mountain. Species diversity decreased and then increased with increasing altitude; phylogenetic diversity decreased with increasing altitude, and the phylogenetic structure changed from dispersed to aggregated; (2) Altitude and soil physical and chemical properties are important drivers of species richness, phylogenetic diversity, and phylogenetic structure along the altitude gradient; (3) The structural equations showed that soil physical properties and altitude rise were the key factors contributing to the decrease in biodiversity in Daqing Mountain, with total soil porosity directly influencing soil physical properties and soil water content indirectly. This study not only reveals the pattern of plant diversity along the altitude of Daqing Mountain but also provides a basis for plant conservation planning, habitat maintenance, and management coordination. Full article
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<p>Distribution of sampling plots at the Tropical Daqing Mountain. (<b>A</b>) Position of Guangxi in China. (<b>B</b>) The location of Pingxiang City in Guangxi. (<b>C</b>) 238 sample plots located in Pingxiang City.</p>
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<p>Schematic design of systematic sampling plots.</p>
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<p>Pearson correlation between species diversity indices and phylogenetic indices of forest communities. * means <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. Blue indicates positive correlation, red indicates negative correlation; The darker the color, the stronger the correlation; PD: Phylogenetic diversity index, NRI: Net relatedness index, NTI: Net nearest taxa index.</p>
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<p>Pearson correlation between soil physical and chemical properties and altitude. * means <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. Blue indicates positive correlation, red indicates negative correlation; The darker the color, the stronger the correlation; ELE: Elevation, SWC: soil water content, SBD: soil bulk density, SBD: soil bulk density, SP: total porosity, TN: total nitrogen, SOM: Soil organic matter, TP: total phosphorus, TK: total potassium, C/N: carbon to nitrogen, N/P: nitrogen to phosphorus, C/P: carbon to phosphorus.</p>
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<p>Pathways of influence of altitude and soil physical and chemical properties on species diversity and phylogenetic diversity in forest communities. PD: Phylogenetic diversity index, ELE: ELE: Elevation, SWC: soil water content, SBD: soil bulk density, SP: total porosity, TN: total nitrogen, SOM: Soil organic matter, TP: total phosphorus, TK: total potassium, Chm: Soil chemical property, Phy: Soil physical property, Spc: Species diversity, Pil: Pielou index, Smp: Simpson index, Mrg: Margalef index, Latent Variables (Circles/Ellipses): These are theoretical constructs that are not directly observed but are inferred from various observed variables. In this diagram, we have PD (Path Dependence), Chm, Phy, and Spc, each with a value of 1.00, indicating perhaps the variance explained or the scaling of these latent constructs. Observed Variables (Rectangles): Represented by rectangles, these are the measurable variables. Examples here include TK, TP, SOM, TN, PH, SP, SBD, SWC, Pil, Smp, and Mrg. The numbers next to these rectangles could be standardized factor loadings, which indicate the strength of the association between the latent variable and the observed variable. Paths (Arrows): The arrows indicate the hypothesized relationships between the variables. Solid lines with arrows suggest positive or negative influences (as indicated by the sign next to the path coefficients), while dashed lines might indicate a non-significant or weaker path not focused on in the hypothesis. Path Coefficients: The numbers along the paths indicate the strength and direction of the relationship between the variables. For instance, a coefficient of –0.38 from PD to Chm suggests a negative relationship, whereas a coefficient of 0.89 from ELE to Phy suggests a strong positive relationship. Correlations (Double-headed Arrows): Double-headed arrows between the latent variables (like those between PD, Chm, Phy, and Spc) indicate correlations or covariances between these constructs. This suggests that the model posits some degree of association between these latent constructs.</p>
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20 pages, 6951 KiB  
Article
Dissecting Cytophagalysin: Structural and Biochemical Studies of a Bacterial Pappalysin-Family Metallopeptidase
by Eva Estevan-Morió, Juan Sebastián Ramírez-Larrota, Enkela Bushi and Ulrich Eckhard
Biomolecules 2024, 14(12), 1604; https://doi.org/10.3390/biom14121604 - 16 Dec 2024
Viewed by 212
Abstract
Cytophaga is a genus of Gram-negative bacteria occurring in soil and the gut microbiome. It is closely related to pathogenic Flavobacterium spp. that cause severe diseases in fish. Cytophaga strain L43-1 secretes cytophagalysin (CPL1), a 137 kDa peptidase with reported collagenolytic and gelatinolytic [...] Read more.
Cytophaga is a genus of Gram-negative bacteria occurring in soil and the gut microbiome. It is closely related to pathogenic Flavobacterium spp. that cause severe diseases in fish. Cytophaga strain L43-1 secretes cytophagalysin (CPL1), a 137 kDa peptidase with reported collagenolytic and gelatinolytic activity. We performed highly-confident structure prediction calculations for CPL1, which identified 11 segments and domains, including a signal peptide for secretion, a prosegment (PS) for latency, a metallopeptidase (MP)-like catalytic domain (CD), and eight immunoglobulin (Ig)-like domains (D3–D10). In addition, two short linkers were found at the D8–D9 and D9–D10 junctions, and the structure would be crosslinked by four disulfide bonds. The CPL1 CD was found closest to ulilysin from Methanosarcina acetivorans, which assigns CPL1 to the lower-pappalysin family within the metzincin clan of MPs. Based on the structure predictions, we aimed to produce constructs spanning the full-length enzyme, as well as PS+CD, PS+CD+D3, and PS+CD+D3+D4. However, we were successful only with the latter three constructs. We could activate recombinant CPL1 by PS removal employing trypsin, and found that both zymogen and mature CPL1 were active in gelatin zymography and against a fluorogenic gelatin variant. This activity was ablated in a mutant, in which the catalytic glutamate described for lower pappalyins and other metzincins was replaced by alanine, and by a broad-spectrum metal chelator. Overall, these results proved that our recombinant CPL1 is a functional active MP, thus supporting the conclusions derived from the structure predictions. Full article
(This article belongs to the Collection Feature Papers in 'Biomacromolecules: Proteins')
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<p>Biocomputational studies. (<b>A</b>) Sequence alignment of the prosegments (PSs) (green background) and catalytic domains (CDs) of CPL1 (UP Q46348), mirolysin (UP G8ULV1), and ulilysin (UP Q8TL28). Identical or equivalent residues are in red, and those shared by two sequences are in blue. The PS cysteine engaged in zinc-binding in the zymogen—putatively in CPL1—is framed. The extended zinc-binding motif, the residues shaping the common calcium site, and the Met-turn motif are shown over light-blue, orange, and cyan background, respectively. (<b>B</b>) Domain distribution along the chemical sequence predicted by <span class="html-italic">AlphaFold</span>, which foresees a signal peptide for secretion (SP), the PS, the CD, and immunoglobulin-like domains D3 through D10. Each domain is labelled, the respective limiting residues are indicated, and the average predicted local-distance difference test (pLDDT) is shown in parenthesis. In all cases, these values are close to or exceed the high-accuracy cut-off of ~90% [<a href="#B42-biomolecules-14-01604" class="html-bibr">42</a>], and are thus classed as high confidence. The only exception is the PS, whose prediction evinces an average pLDDT that is slightly lower, but still highly reliable for the main chain. Two short linkers (LNKs) would be intercalated between D8 and D9, and between D9 and D10. Predicted disulfide bonds are shown in orange. The cysteine putatively engaged in latency in the zymogen (C<sup>24</sup>) and the extended zinc-binding motif (H<sup>231</sup>–H<sup>241</sup>), as well as the Met-turn methionine (M<sup>284</sup>) and the maturation cleavage point (A<sup>66</sup>–E<sup>67</sup>; scissors) are further pinpointed. (<b>C</b>) pLDDT for each residue of the prediction (positions 1–1282) for each of the five distinct models obtained. (<b>D</b>) Sequence coverage for each residue of the prediction (positions 1–1282) vs. number of sequences. (<b>E</b>) Superposition of the five predicted models without further relaxation/minimization with each domain/segment in the colour of (<b>B</b>). Only PS, CD, D3, D4, and, roughly, D5 appear with similar relative orientations in all models. (<b>F</b>) Analysis of the predicted aligned error, which estimates if domains are correctly positioned relative to one another, for each residue of the prediction (positions 1–1282; model_1). Each segment/domain of (<b>B</b>) gives rise to a marine blue square along the diagonal. Off-diagonal blue values suggest well-predicted interactions between domains. (<b>G</b>) Superposition of the Cα-traces of the experimental structures of promirolysin (PS in sienna, CD in gold) and proulilysin (cyan/dodger blue) in standard orientation [<a href="#B15-biomolecules-14-01604" class="html-bibr">15</a>] onto the prediction of CPL1 (purple/pink). The CPL1 prediction matches proulilysin significantly better. The catalytic zinc (magenta sphere) and the common calcium (red sphere) of proulilysin are further displayed.</p>
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<p>Structural analysis of the predicted CPL1 domains. (<b>A</b>) Ribbon-type plot of the CPL1 PS and CD in cross-eye stereo. The secondary structure elements are labelled (α1p, α2p, α1–α9, and β1–β6). The putative cysteine-switch cysteine (C<sup>24</sup>), zinc-binding residues (H<sup>231</sup>, H<sup>235</sup> and H<sup>241</sup>), general base/acid glutamate (E<sup>232</sup>), Met-turn methionine (M<sup>284</sup>) and tyrosine-switch tyrosine (Y<sup>286</sup>), calcium-binding residues (D<sup>251</sup> and T<sup>256</sup>), as well as the putative disulfide-bonded cysteines (C<sup>247</sup>–C<sup>273</sup>; ① and C<sup>267</sup>–C<sup>292</sup>; ②) are shown for their side chains as sticks and numbered. The zinc and calcium cations were modelled based on the proulilysin (PDB 8CDB) and mature ulilysin (PDB 2CKI) structures. The putative maturation site (A<sup>66</sup>–E<sup>67</sup>) and the LNR-loop are highlighted by green and orange arrows, respectively. Depiction of the Ig-like domains (D3–D10) showing as ribbon- or Cα-plots (<b>B</b>) D3; (<b>C</b>) D5 (cyan Cα-plot) onto D3 (plum Cα-plot) in the same orientation as in (<b>B</b>); (<b>D</b>) D4; (<b>E</b>) D6 (brown Cα-plot) onto D4 (yellow Cα-plot) in the same orientation as (<b>D</b>); (<b>F</b>) D7; (<b>G</b>) D9 (orange Cα-plot) onto D7 (green Cα-plot) in the same orientation as (<b>F</b>); (<b>H</b>) D8 (disulfide bond C<sup>963</sup>–C<sup>1083</sup>; ①) and (<b>I</b>) D10. The β-strands and the N- and C-terminal residues are numbered in all cases.</p>
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<p>Recombinant CPL1 expression and purification. (<b>A</b>) Representative SDS-PAGE gels of nickel-affinity purifications of construct CPL1_1-3 (Q<sup>20</sup>–V<sup>444</sup>), both in its wild-type (left panel) and E<sup>232</sup>A-mutant (right panel) variants. Samples representing the flow through (FT), the wash-step with 20 mM imidazole (W20), and the first two elution fractions using 250 mM imidazole (E250-1/2) were analysed under reducing conditions. Lane M depicts the molecular-mass marker. The target protein migrated as a band at its expected molecular weight (~47 kDa). (<b>B</b>) Same as (<b>A</b>) for construct CPL1_1-4 (Q<sup>20</sup>–T<sup>591</sup>), which migrates as a ~63 kDa band as expected. (<b>C</b>) Representative calibrated size-exclusion chromatography profiles of the two constructs of (<b>A</b>) using bovine-serum albumin as the calibration standard, and with the conductivity trace shown in dark red (peak at ~21.3 mL), and (<b>D</b>) SDS-PAGE analyses of peak fractions B11 and B12 shown in (<b>C</b>) as orange bands. A retention volume of ~18.4 mL corresponds to an apparent molecular mass of ~41 kDa, which is consistent with the theoretic value (~47 kDa). (<b>E</b>,<b>F</b>) Same as (<b>C</b>,<b>D</b>) for the two constructs of (<b>B</b>). A retention volume of ~17.7 mL corresponds to an apparent molecular weight of ~61 kDa, which is consistent with the theoretic value (~63 kDa). SDS-PAGE gels were cropped for clarity. For full gel images, please refer to <a href="#app1-biomolecules-14-01604" class="html-app">Extended Data Figures S7–S10 in the supplement</a>.</p>
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<p>Trypsin-mediated activation and activity of CPL1 constructs. (<b>A</b>) SDS-PAGE analysis under non-reducing and reducing conditions, which shows that the trypsin-activated protease sample (act) shows a band ~7 kDa smaller than the non-activated sample (n.a.), which corresponds to the excision of the zymogenic N-terminal prosegment. (<b>B</b>) (<b>Left</b>) Average and standard deviation of relative activity of different amounts of activated wild-type CPL1_1-3 against the fluorogenic substrate DQ Gelatin (2 μg) compared to the non-activated zymogen. (<b>Right</b>) Ratio of activities between both protein variants (n = 16). (<b>C</b>) (<b>Left</b>) Fluorescence resulting from the turnover of DQ Gelatin by activated wild-type CPL1_1-3 and CPL1_1-4. The values shown in salmon for the latter are recalculated from the recorded curve at 3.2 nM and correspond to the same concentration as those for CPL1_1-3, and are therefore marked with an asterisk (*). (<b>Right</b>) Normalized molarity values for the two constructs (n = 5), which reveal equivalent activity. (<b>D</b>) SDS-PAGE analysis of the incubation of human type-I atelocollagen (10 μg) with 2 μg of wild-type CPL1_1-3 (<b>left</b>), 0.5 μg of <span class="html-italic">Clostridium histolyticum</span> collagenase (centre), and 0.1 μg (++) or 1 μg (+++) of bovine trypsin (right). The + and − signs indicate the presence or absence of collagen and the respective protease (cytophagalysin, collagenase, or trypsin, as labeled below the gels), with increasing + signs denoting higher protease concentrations. SDS-PAGE gels were cropped for clarity. For full gel images, please refer to <a href="#app1-biomolecules-14-01604" class="html-app">Extended Data Figures S11 and S12 in the supplementary materials</a>.</p>
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<p>CPL1 activity in gelatin zymography. (<b>A</b>) Representative gelatin zymograms (<b>left</b>) and SDS-PAGE analysis (<b>right</b>) under reducing conditions of wild-type variants CPL1_1-2 (<span class="html-italic">lane 1</span>), CPL1_1-4 (<span class="html-italic">lane 2</span>), and CPL1_1-3 (<span class="html-italic">lane 3</span>), which evince only minute activity due to the unfolding of the protein variants caused by the reducing conditions, as well as of inactive CPL1_1-3 E<sup>232</sup>A-mutant (<span class="html-italic">lane 4</span>). (<b>B</b>) Same as (<b>A</b>) under non-reducing conditions, which locally preserves the integrity of the recombinant proteins, thereby aiding in-gel refolding and consequently revealing significant activity for wild-type CPL1_1-2 (<span class="html-italic">lane 1</span>), CPL1_1-4 (<span class="html-italic">lane 2</span>), and CPL1_1-3 (<span class="html-italic">lane 3</span>), but not for the mutationally inactivated CPL1_1-3 variant (<span class="html-italic">lane 4</span>). SDS-PAGE gels and zymograms were cropped for clarity. For full gel and zymogram images, please refer to <a href="#app1-biomolecules-14-01604" class="html-app">Extended Data Figures S13 and S14 in the supplement</a>.</p>
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<p>Inhibition of activity of CPL1. (<b>A</b>) (<b>Left</b>) Complete activation of CPL1_1-3 (<span class="html-italic">lane act</span>) by trypsin in SDS-PAGE, as shown by the absence of the zymogen band that is found in <span class="html-italic">lane n.a.</span> In zymography (<b>Centre</b>), both samples exhibited activity for both activated and non-activated CPL1, with significantly increased activity observed in the activated sample. Importantly, no trypsin activity was detected in the CPL1 samples. For reference, trypsin activity (Right) at an apparent molecular weight of ~18 kDa is shown. In zymography however, both samples demonstrated activity for both activated and non-activated CPL1 (<b>Centre</b>), with activity enriched in the activated sample. Note, no trypsin activity was observed in CPL1 samples, and trypsin activity is shown at an apparent molecular weight of ~18 kDa (<b>Right</b>). (<b>B</b>) The activity of CPL1_1-3 against fluorogenic DQ Gelatin is efficiently inhibited by EDTA as expected for a metallopeptidase, yielding only residual values that are comparable to those of the E<sup>232</sup>A-mutant and trypsin, which does not cleave this substrate. Note that the CPL1_1-3 zymogen still has a residual activity of ~15% of the active form. SDS-PAGE gels and zymograms were cropped for clarity. For full gel and zymogram images, please refer to <a href="#app1-biomolecules-14-01604" class="html-app">Extended Data Figure S15 in the supplementary materials</a>.</p>
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17 pages, 4507 KiB  
Article
The Relationship Between Soil and Gut Microbiota Influences the Adaptive Strategies of Goitered Gazelles in the Qaidam Basin
by Yiran Wang, Bin Li, Bo Xu and Wen Qin
Animals 2024, 14(24), 3621; https://doi.org/10.3390/ani14243621 - 15 Dec 2024
Viewed by 382
Abstract
The gut microbiota is integral to the health and adaptability of wild herbivores. Interactions with soil microbiota can shape the composition and function of the gut microbiota, thereby influencing the hosts’ adaptive strategies. As a result, soil microbiota plays a pivotal role in [...] Read more.
The gut microbiota is integral to the health and adaptability of wild herbivores. Interactions with soil microbiota can shape the composition and function of the gut microbiota, thereby influencing the hosts’ adaptive strategies. As a result, soil microbiota plays a pivotal role in enabling wild herbivores to thrive in extreme environments. However, the influence of soil microbiota from distinct regions on host’s gut microbiota has often been overlooked. We conducted the first comprehensive analysis of the composition and diversity of gut and soil microbiota in goitered gazelles across six regions in the Qaidam Basin, utilizing source tracking and ecological assembly process analyses. Significant differences were observed in the composition and diversity of soil and gut microbiota among the six groups. Source tracking analysis revealed that soil microbiota in the GangciGC (GC) group contributed the highest proportion to fecal microbiota (8.94%), while the Huaitoutala (HTTL) group contributed the lowest proportion (1.80%). The GC group also exhibited the lowest α-diversity in gut microbiota. The observed differences in gut microbial composition and diversity among goitered gazelles from six regions in the Qaidam Basin were closely tied to their adaptive strategies. Ecological assembly process analysis indicated that the gut microbiota were primarily influenced by stochastic processes, whereas deterministic processes dominated most soil microbial groups. Both the differences and commonalities in gut and soil microbiota play essential roles in enabling these gazelles to adapt to diverse environments. Notably, the utilization pattern of soil microbiota by gut microbiota did not align with regional trends in gut microbial α-diversity. This discrepancy may be attributed to variations in environmental pressures and the gut’s filtering capacity, allowing gazelles to selectively acquire microbiota from soil to maintain homeostasis. This study highlights the significant regional variation in gut and soil microbiota diversity among goitered gazelle populations in the Qaidam Basin and underscores the critical role of soil-derived microbiota in their environmental adaptation. Full article
(This article belongs to the Section Ecology and Conservation)
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<p>Photographs of (<b>A</b>) goitered gazelles in side view, (<b>B</b>) goitered gazelles with offspring, (<b>C</b>) goitered gazelles in frontal view, (<b>D</b>) hoofprints of goitered gazelles, (<b>E</b>) desert landscapes in Wulan Town, and (<b>F</b>,<b>G</b>) fresh feces of goitered gazelles.</p>
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<p>Sampling points in the Qaidam Basin, Qinghai province, China (KK: Keke, 5 fecal samples. HES: Haersi, 8 fecal samples. KLK: Keluke, 5 fecal samples. HTTL: Huaitoutala, 6 fecal samples. GEM: Geermu, 5 fecal samples. GC: Gangci, 9 fecal samples).</p>
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<p>Venn diagram among 6 regions of (<b>A</b>) gut microbiota and (<b>B</b>) soil microbiota at the OTU level (F represents fecal; S represents soil).</p>
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<p>The top 5 phyla (<b>A</b>) and families (<b>B</b>) in relative abundance of gut microbiota among 6 regions based on the Wilcoxon rank sum test (F represents fecal; * represents <span class="html-italic">p</span> &lt; 0.05; ** represents 0.001 &lt; <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>The top 5 genera (<b>A</b>) in relative abundance and (<b>B</b>) <span class="html-italic">Akkermansia</span> of gut microbiota among 6 regions based on the Wilcoxon rank sum test (F represents fecal; * represents <span class="html-italic">p</span> &lt; 0.05; ** represents 0.001 &lt; <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>The top 5 phyla (<b>A</b>), families (<b>B</b>) and genera (<b>C</b>) in relative abundance of soil microbiota among 6 regions based on the Wilcoxon rank sum test (S represents soil; * represents <span class="html-italic">p</span> &lt; 0.05; ** represents 0.001 &lt; <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>The α-diversity of the Chao1 index (<b>A</b>) and Shannon index (<b>B</b>) in gut microbiota at the OTU level among 6 regions based on the Wilcoxon rank sum test (F represents fecal; * represents <span class="html-italic">p</span> &lt; 0.05; ** represents 0.001 &lt; <span class="html-italic">p</span> &lt; 0.01; *** represents <span class="html-italic">p</span> &lt; 0.001).</p>
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<p>The α-diversity of Chao1 index (<b>A</b>) and Shannon index (<b>B</b>) in soil microbiota at the OTU level among 6 regions based on the Wilcoxon rank sum test (S represents soil; * represents <span class="html-italic">p</span> &lt; 0.05; ** represents 0.001 &lt; <span class="html-italic">p</span> &lt; 0.01; *** represents <span class="html-italic">p</span> &lt; 0.001).</p>
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<p>NMDS analysis among 6 regions of (<b>A</b>) gut microbiota and (<b>B</b>) soil microbiota at the OTU level based on Bray–Curtis distance matrices (F represents fecal; S represents soil).</p>
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<p>Analysis of Modified Stochastic Ratio (MST) of gut and soil microbiota (F represents fecal; S represents soil).</p>
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33 pages, 3624 KiB  
Review
Mechanisms and Impact of Rhizosphere Microbial Metabolites on Crop Health, Traits, Functional Components: A Comprehensive Review
by Qingxia Chen, Yingjie Song, Yuxing An, Yinglin Lu and Guohua Zhong
Molecules 2024, 29(24), 5922; https://doi.org/10.3390/molecules29245922 - 15 Dec 2024
Viewed by 670
Abstract
Current agricultural practices face numerous challenges, including declining soil fertility and heavy reliance on chemical inputs. Rhizosphere microbial metabolites have emerged as promising agents for enhancing crop health and yield in a sustainable manner. These metabolites, including phytohormones, antibiotics, and volatile organic compounds, [...] Read more.
Current agricultural practices face numerous challenges, including declining soil fertility and heavy reliance on chemical inputs. Rhizosphere microbial metabolites have emerged as promising agents for enhancing crop health and yield in a sustainable manner. These metabolites, including phytohormones, antibiotics, and volatile organic compounds, play critical roles in promoting plant growth, boosting resistance to pathogens, and improving resilience to environmental stresses. This review comprehensively outlines the mechanisms through which rhizosphere microbial metabolites influence crop health, traits, functional components, and yield. It also discusses the potential applications of microbial secondary metabolites in biofertilizers and highlights the challenges associated with their production and practical use. Measures to overcome these challenges are proposed, alongside an exploration of the future development of the functional fertilizer industry. The findings presented here provide a scientific basis for utilizing rhizosphere microbial metabolites to enhance agricultural sustainability, offering new strategies for future crop management. Integrating these microbial strategies could lead to increased crop productivity, improved quality, and reduced dependence on synthetic chemical inputs, thereby supporting a more environmentally friendly and resilient agricultural system. Full article
15 pages, 3664 KiB  
Article
Poly-Glutamic Acid Regulates Physiological Characteristics, Plant Growth, and the Accumulation of the Main Medical Ingredients in the Root of Salvia miltiorrhiza Under Water Shortage
by Changjuan Shan and Yibo Zhang
Agronomy 2024, 14(12), 2977; https://doi.org/10.3390/agronomy14122977 - 13 Dec 2024
Viewed by 265
Abstract
To supply information concerning the application of poly-glutamic acid (PGA) in the drought-resistant cultivation of red sage (Salvia miltiorrhiza), we investigated the role of PGA in regulating the physiological characteristics, plant growth, and the accumulation of the main medical components in [...] Read more.
To supply information concerning the application of poly-glutamic acid (PGA) in the drought-resistant cultivation of red sage (Salvia miltiorrhiza), we investigated the role of PGA in regulating the physiological characteristics, plant growth, and the accumulation of the main medical components in the root under water shortage. The findings showed that different levels of water shortage (WS) all suppressed the photosynthetic function by reducing the net photosynthetic rate (Pn), Soil and plant analyzer development (SPAD) value, maximum photochemical efficiency of PSII (Fv/Fm), photochemical quenching (qP), and actual photochemical efficiency of PSII (Y(II)), as well as increasing non-photochemical quenching (qN). Compared with WS, PGA plus WS enhanced the photosynthetic function by reducing qN and increasing the other indicators above. For water metabolism, WS increased stomatal limit value (Ls) and water use efficiency (WUE), but decreased transpiration rate (Tr) and stomatal conductance (Gs). Compared with WS, PGA plus WS decreased Ls and increased Tr, Gs, and WUE. Meanwhile, WS enhanced the antioxidant capacity by increasing superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) activities. However, WS increased malondialdehyde (MDA) content. Compared with WS, PGA plus WS enhanced the above antioxidant enzymes. In this way, PGA reduced MDA content and improved the antioxidant capacity under WS. In addition, WS decreased the shoot and root biomass, but increased the root/shoot ratio. Compared with WS, PGA plus WS further increased the root/shoot ratio and shoot and root biomass. For medical ingredients, WS decreased the yield of rosmarinic acid (RosA) and salvianolic acid B (SalB), but increased the yield of dihydrotanshinone (DHT), cryptotanshinone (CTS), tanshinone I (Tan I), and tanshinone ⅡA (Tan ⅡA). Compared with WS, PGA plus WS increased the yield of these medical ingredients. Our findings clearly suggested that PGA application was an effective method to enhance sage drought tolerance and the yield of the main medical ingredients in sage root. This provides useful information for its application in sage production under WS. Full article
(This article belongs to the Section Water Use and Irrigation)
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<p>Effects of PGA on SPAD value (<b>A</b>) and Pn (<b>B</b>) under WS. Different letters represent significant differences between treatments at <span class="html-italic">p</span> &lt; 0.05 as determined by DMRT. The plants were treated as below. Control, 60% field water capacity; 50%FC, 50% field water capacity; 50%FC + PGA-1, 50% field water capacity + 0.22 g/L PGA; 50%FC + PGA-2, 50% field water capacity + 0.44 g/L PGA; 50%FC + PGA-3, 50% field water capacity + 0.88 g/L PGA; 40%FC, 40% field water capacity; 40%FC + PGA-1, 40% field water capacity + 0.22 g/L PGA; 40%FC + PGA-2, 40% field water capacity + 0.44 g/L PGA; 40%FC + PGA-3, 40% field water capacity + 0.88 g/L PGA.</p>
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<p>Effects of PGA on gas exchange parameters Tr (<b>A</b>), Gs (<b>B</b>), Ls (<b>C</b>), and WUE (<b>D</b>) under WS. Different letters represent significant differences between treatments at <span class="html-italic">p</span> &lt; 0.05 as determined by DMRT. The plants were treated as below. Control, 60% field water capacity; 50%FC, 50% field water capacity; 50%FC + PGA-1, 50% field water capacity + 0.22 g/L PGA; 50%FC + PGA-2, 50% field water capacity + 0.44 g/L PGA; 50%FC + PGA-3, 50% field water capacity + 0.88 g/L PGA; 40%FC, 40% field water capacity; 40%FC + PGA-1, 40% field water capacity + 0.22 g/L PGA; 40%FC + PGA-2, 40% field water capacity + 0.44 g/L PGA; 40%FC + PGA-3, 40% field water capacity + 0.88 g/L PGA.</p>
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<p>Effects of PGA on Y(Ⅱ) (<b>A</b>), F<sub>v</sub>/F<sub>m</sub> (<b>B</b>), q<sub>N</sub> (<b>C</b>), and q<sub>P</sub> (<b>D</b>) under WS. Different letters represent significant differences between treatments at <span class="html-italic">p</span> &lt; 0.05 as determined by DMRT. The plants were treated as below. Control, 60% field water capacity; 50%FC, 50% field water capacity; 50%FC + PGA-1, 50% field water capacity + 0.22 g/L PGA; 50%FC + PGA-2, 50% field water capacity + 0.44 g/L PGA; 50%FC + PGA-3, 50% field water capacity + 0.88 g/L PGA; 40%FC, 40% field water capacity; 40%FC + PGA-1, 40% field water capacity + 0.22 g/L PGA; 40%FC + PGA-2, 40% field water capacity + 0.44 g/L PGA; 40%FC + PGA-3, 40% field water capacity + 0.88 g/L PGA.</p>
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<p>Effects of PGA on SOD (<b>A</b>), POD (<b>B</b>), and CAT (<b>C</b>) activities, and MDA content (<b>D</b>) under WS. Different letters represent significant differences between treatments at <span class="html-italic">p</span> &lt; 0.05 as determined by DMRT. The plants were treated as below. Control, 60% field water capacity; 50%FC, 50% field water capacity; 50%FC + PGA-1, 50% field water capacity + 0.22 g/L PGA; 50%FC + PGA-2, 50% field water capacity + 0.44 g/L PGA; 50%FC + PGA-3, 50% field water capacity + 0.88 g/L PGA; 40%FC, 40% field water capacity; 40%FC + PGA-1, 40% field water capacity + 0.22 g/L PGA; 40%FC + PGA-2, 40% field water capacity + 0.44 g/L PGA; 40%FC + PGA-3, 40% field water capacity + 0.88 g/L PGA.</p>
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<p>Effects of PGA on shoot biomass (<b>A</b>) and root biomass (<b>B</b>), root/shoot ratio (<b>C</b>), and root volume (<b>D</b>) under WS. Different letters represent significant differences between treatments at <span class="html-italic">p</span> &lt; 0.05 as determined by DMRT. The plants were treated as below. Control, 60% field water capacity; 50%FC, 50% field water capacity; 50%FC + PGA-1, 50% field water capacity + 0.22 g/L PGA; 50%FC + PGA-2, 50% field water capacity + 0.44 g/L PGA; 50%FC + PGA-3, 50% field water capacity + 0.88 g/L PGA; 40%FC, 40% field water capacity; 40%FC + PGA-1, 40% field water capacity + 0.22 g/L PGA; 40%FC + PGA-2, 40% field water capacity + 0.44 g/L PGA; 40%FC + PGA-3, 40% field water capacity + 0.88 g/L PGA.</p>
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<p>Effects of PGA on the yield of main water-soluble medical ingredients (<b>A</b>) and main fat-soluble medical ingredients (<b>B</b>) in root under WS. Different letters represent significant differences between treatments at <span class="html-italic">p</span> &lt; 0.05 as determined by DMRT. The plants were treated as below. Control, 60% field water capacity; 50%FC, 50% field water capacity; 50%FC + PGA-1, 50% field water capacity + 0.22 g/L PGA; 50%FC + PGA-2, 50% field water capacity + 0.44 g/L PGA; 50%FC + PGA-3, 50% field water capacity + 0.88 g/L PGA; 40%FC, 40% field water capacity; 40%FC + PGA-1, 40% field water capacity + 0.22 g/L PGA; 40%FC + PGA-2, 40% field water capacity + 0.44 g/L PGA; 40%FC + PGA-3, 40% field water capacity + 0.88 g/L PGA.</p>
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<p>Pearson correlation analysis between parameters related to plant growth and medical ingredient yield and parameters related to physiological characteristics, measured in June and August. The abbreviations in this figure are as follows: SPAD = soil and plant analyzer development; Pn = net photosynthetic rate; Tr = transpiration rate; Gs = stomatal conductance; Ls = stomatal limit value; WUE = water use efficiency; Y(Ⅱ) = actual photochemical efficiency of PSII; F<sub>v</sub>/F<sub>m</sub> = maximum photochemical efficiency of PSII; q<sub>N</sub> = non-photochemical quenching; q<sub>P</sub> = photochemical quenching; SOD = superoxide dismutase; POD = peroxidase; CAT = catalase; MDA = malondialdehyde; RosA = rosmarinic acid; SalB = salvianolic acid B; DHT = dihydrotanshinone; CTS = cryptotanshinone; Tan I = tanshinone I and Tan ⅡA = tanshinone ⅡA.</p>
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47 pages, 5882 KiB  
Article
Meta-Study on Sulphur Supply of Various Crop Species in Organic Farming Between 1998 and 2023 in European Countries—Part 1: Effects of Sulphur Supply on Plant Dry Biomass, Nitrogen Uptake, Legume N2 Fixation and Sulphur Fertilizer Requirement Determinations
by Hartmut Kolbe
Agronomy 2024, 14(12), 2975; https://doi.org/10.3390/agronomy14122975 - 13 Dec 2024
Viewed by 381
Abstract
Sulphur is an essential nutrient that fulfils various important functions in plants, including the formation of amino acids, proteins, chlorophyll and the support of nitrogen uptake, e.g., in legumes. The sulphur content of the atmosphere due to industrial combustion has fallen sharply in [...] Read more.
Sulphur is an essential nutrient that fulfils various important functions in plants, including the formation of amino acids, proteins, chlorophyll and the support of nitrogen uptake, e.g., in legumes. The sulphur content of the atmosphere due to industrial combustion has fallen sharply in recent decades, which has ultimately led to yield and quality deficiencies on farms. In this summarised study, data from 98 sites in Europe were recorded from 1998 to 2023. The sulphur fertiliser trials were conducted on farms, and experimental stations under organic farming conditions. A total of 1169 treatment variants and 598 standard variants without S-fertilisation were analysed. Fertilisation was carried out with various sources of Sulphur in different quantities and forms, usually directly before or during crop cultivation. The amounts of plant-available S in the soil were determined at depths of 0–90 cm. Site characteristics such as Smin, Nmin, soil type, pH value, precipitation and the extent of livestock farming were recorded. A sufficient amount of data was available for each experimental aspect to quantitatively describe the influence of increasing S supply to the soil or plant species groups (permanent grassland, lucerne-clover-grass, grain legumes and cereals) from severe deficiency to oversupply. The analyses therefore focused on establishing relationships between yield responses, correlations with the nitrogen uptake of crop species and N2 fixation in legumes and the nutrient supply with plant-available sulphur. An assessment procedure was drawn up for soil supply with available sulphur that is too low (classes A, B), optimal (class C: 20–30 kg S ha−1) and too high (classes D, E). The results were also used to develop practical methods for determining fertiliser requirements for different crop species and the crop rotation in organic farming. Full article
(This article belongs to the Section Soil and Plant Nutrition)
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<p>Effects of S fertilisation on the dry biomass yields (in relative values compared to no fertilisation = 100%) of lucerne-clover-grass, grain legumes and permanent grassland depending on the S<sub>min</sub> supply of the soil.</p>
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<p>Effects of S fertilisation depending on the S<sub>min</sub> supply of the soil on the N content (<b>top</b>) and N removal (<b>bottom</b>) of lucerne-clover grass, grain legumes and permanent grassland (in relative values compared to no fertilisation = 100%).</p>
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<p>Effect of S fertilisation depending on the S<sub>min</sub> supply of the soil on the legume proportions (<b>top</b>) and the calculated N<sub>2</sub> fixation (<b>bottom</b>) in lucerne-clover grass, grain legume and permanent grassland cultivation (in relative values compared to no fertilisation = 100%).</p>
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<p>Effect of direct S fertilisation or as an after-effect of fertilisation of legume preceding crops depending on the S<sub>min</sub> supply of the soil on the dry biomass yields of non-legume species (in relative values compared to no fertilisation = 100%; LCG = lucerne-clover-grass, GL = grain legumes).</p>
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<p>Effect of direct S fertilisation or as an after-effect of fertilisation of legume preceding crops depending on the S<sub>min</sub> supply of the soil on the N content (<b>top</b>) and N removal (<b>bottom</b>) of non-legume species (in relative values compared to no fertilisation = 100%; LCG = lucerne-clover-grass, GL = grain legumes).</p>
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<p>Summarised results on the influence of the S<sub>min</sub> supply of the soil in combination with direct S fertilisation on the dry biomass yields of the crop species (in relative values compared to no fertilisation = 100%).</p>
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<p>Summarised results on the influence of S<sub>min</sub> supply of the soil in combination with direct S fertilisation on the N content (<b>top</b>) and N removal (<b>bottom</b>) of the crop species (in relative values compared to no fertilisation = 100%).</p>
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<p>Summarised results on the influence of S<sub>min</sub> supply of the soil in combination with direct S fertilisation on the legume portions (<b>top</b>) and N<sub>2</sub> fixation (<b>bottom</b>) of forage, grain legume and legume-nonlegume stands (in relative values compared to no fertilisation = 100%).</p>
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<p>Effects of different S fertilisers on the relative biomass yields of lucerne-clover-grass depending on the S<sub>min</sub> supply of the soil.</p>
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<p>Effects of fertilisation levels with sulphur on the relative dry biomass yield differences in the investigated plant groups.</p>
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<p>Relationships between the average S uptake of the total plants and the calculated biomass yield differences due to additional S fertilisation (100% = without fertilisation) for grassland, legume and non-legume crops of different species.</p>
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15 pages, 5778 KiB  
Review
Research Status and Prospects of Sweet Potato Harvesters’ Conveying and Separation Mechanisms
by Jiwen Peng, Yemeng Wang, Baoliang Peng, Guangyu Xue, Lianglong Hu and Gongpu Wang
Sustainability 2024, 16(24), 10957; https://doi.org/10.3390/su162410957 - 13 Dec 2024
Viewed by 326
Abstract
With the advancement of technology and the national effort to promote the development of agricultural mechanization, sweet potato harvester technology is continuously evolving. To solve the problems related to high skin breakage rates and high damage rates during sweet potato harvesting, it is [...] Read more.
With the advancement of technology and the national effort to promote the development of agricultural mechanization, sweet potato harvester technology is continuously evolving. To solve the problems related to high skin breakage rates and high damage rates during sweet potato harvesting, it is necessary to develop a conveying and separation mechanism that facilitates low skin breakage rates, low injury rates, good potato–soil separation, and smooth transport. This paper elaborates on the working modes of mechanized sweet potato harvesting, focusing on the current state of research on the conveying and separating mechanisms of sweet potato harvesters both in China and elsewhere. The functions of different types of conveying chains are summarized, the conveying and separating technologies used for the mechanized harvesting of other root crops that can be applied to sweet potato harvesters are analyzed, and an outlook on the development trends regarding sweet potato harvesters is provided. Full article
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<p>Segmented harvesting process for sweet potatoes.</p>
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<p>Combined harvesting workflow for sweet potatoes.</p>
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<p>672-Harvester-Box Filling System.</p>
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<p>Secondary conveyor chain.</p>
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<p>TSP1900 tractor-type sweet potato harvester.</p>
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<p>OMEGA separation mechanism.</p>
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<p>GZA651 self-propelled sweet potato combine harvester.</p>
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<p>Intersection of primary conveying chain and secondary conveying chain.</p>
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<p>Low-damage multifunctional fresh sweet potato harvester.</p>
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<p>4GS-1500 series sweet potato segmented double-row harvester.</p>
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<p>4GLZ-1 self-propelled potato combine harvester.</p>
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<p>Lightweight sweet potato combine harvester.</p>
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<p>Diagram of linkage-type conveyor chain structure. (1) Rubber belt. (2) Rod.</p>
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<p>Schematic diagram of gridded plate conveyor chain structure. (1) Rubber belt. (2) Rod. (3) Grid plate.</p>
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<p>Schematic diagram of key components of clamping and conveying device. (1) Driven wheel. (2) Belt. (3) Tensioning mechanism. (4) Active wheel.</p>
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<p>Vertical ring conveyor technology vertical ring separation screen diagram. (1) Outer frame. (2) Partition.</p>
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<p>Structural schematic of key components of roller pushing-based conveying–separation technology. (1) Drive roller shaft. (2) Dial.</p>
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15 pages, 1743 KiB  
Article
Characteristics of Dissolved Organic Matter (DOM) Combined with As in Fe-Rich Red Soils of Tea Plantations in the Southern Anhui Province, East China
by Youru Yao, Juying Li, Kang Ma, Jingyi Zhang, Yuesheng Lin, Huarong Tan, Jia Yu and Fengman Fang
Agriculture 2024, 14(12), 2289; https://doi.org/10.3390/agriculture14122289 - 13 Dec 2024
Viewed by 292
Abstract
Dissolved organic matter (DOM) is widely present in soil environments and plays a crucial role in controlling the morphology, environmental behavior, and hazards of arsenic (As) in soil. In the Fe-rich red soil of tea plantations, the decomposition of tea tree litter complicates [...] Read more.
Dissolved organic matter (DOM) is widely present in soil environments and plays a crucial role in controlling the morphology, environmental behavior, and hazards of arsenic (As) in soil. In the Fe-rich red soil of tea plantations, the decomposition of tea tree litter complicates DOM properties, leading to more uncertain interactions between DOM, Fe, and As. This study focused on three tea plantations in Huangshan City to investigate the contents of DOM, Fe, and As in surface red soils (Ferralsols) and establish their correlations. Three-dimensional fluorescence spectroscopy and PARAFAC analysis methods were used to analyze the DOM components and fluorescence signatures. Additionally, the process and mechanism of the binding of DOM-Fe with As were explored through laboratory experiments on the morphological transformation of As by DOM-Fe. The results showed that the pH values of the soils in the three tea plantations ranged from 3.9 to 5.2, and the entire sample was strongly acidic. The DOM exhibited strong intrinsic properties and low humification, containing three types of humic acid components and one intermediate protein component. The DOC content in the Fe-rich red soil did not have a direct correlation with Fe and As, but the interaction of DOM fractions with Fe significantly influenced the As content. Specifically, the interaction of protein-like fractions with Fe had a more pronounced effect on the As content. The maximum sorption rate of As by DOM was 15.45%, and this rate increased by 49 to 75% with the participation of Fe. In the configuration of the metal electron bridge, Fe acts as a cation, forming a connecting channel between the negatively charged DOM and As, thus enhancing the DOM’s binding capacity to As. DOM-Fe compounds bind As through surface pores and functional groups. These findings provide deeper insights into the influence of DOM on As behavior in Fe-rich soil environments and offer theoretical support for controlling As pollution in red soil. Full article
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<p>Distribution of the study area and sampling sites.</p>
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<p>Linear correlation plots of DOC, Fe, and As contents of tea plantation soils. The colored area around the regression line indicates the 95% confidence interval.</p>
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<p>Distribution of fluorescence index of soil DOM in tea plantations in Huangshan City. (<b>a</b>) distribution of fluorescence index and autochthonous index; (<b>b</b>) distribution of β/α and humification indicators. FI, fluorescence index; BIX, autochthonous index; β/α, the proportion of the newborn DOM in the overall DOM; HIX, humification indicators.</p>
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<p>EEM-PARAFAC identification of spectral characteristics of DOM components. Component C1 (Ex/Em = 345/414 nm), UVA HA component. Component C2 (Ex/Em = 390/464 nm), terrigenous humic-like component. Component C3 (Ex/Em = 290(325)/338 nm), protein-like T-peak. Component C4 (Ex/Em = 390/406 nm), hydrophobic HA components.</p>
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<p>Correlation analyses of As and Fe with DOM fractions and spectral indices (<b>a</b>), and the effect of DOM fractions and Fe interaction on As content (<b>b</b>–<b>e</b>). The size of the squares and numbers in (<b>a</b>) indicate changes in correlation coefficients, and asterisks (*) indicate the level of significance (“*” indicates <span class="html-italic">p</span> &lt; 0.05, “**” indicates <span class="html-italic">p</span> &lt; 0.01, and “***” indicates <span class="html-italic">p</span> &lt; 0.001). (<b>b</b>–<b>e</b>) refer to C1 ∩ Fe, C2 ∩ Fe, C3 ∩ Fe and C4 ∩ Fe, respectively. The <span class="html-italic">x</span>-axis is the soil Fe content, <span class="html-italic">y</span>-axis is the maximal fluorescence intensity of DOM fractions, and <span class="html-italic">z</span>-axis is the linear predictive value of As content.</p>
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<p>Combination rate of As by DOM-Fe with different content ratios. (<b>a</b>) The relative percentage of free state As and combination As in water at different DOM concentrations. (<b>b</b>) The combination ratio of DOM to As in water at different concentrations of DOM and Fe.</p>
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<p>SEM images of DOM-Fe before and after bonding with As ((<b>a</b>) before bonding; (<b>b</b>) after bonding).</p>
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14 pages, 3502 KiB  
Article
Preliminary Study of Distribution of Soil Available Nutrients in Loquat (Eriobotrya japonica) Orchards and Their Responses to Environmental Factors Based on Path Analysis Model
by Yue Zhao, Linzhong Gong, Furong Wang, Yong Liu, Xiaoyan Ai, Wei Zhu, Yang Zhang, Zhimeng Gan, Huaping He and Huiliang Wang
Agronomy 2024, 14(12), 2970; https://doi.org/10.3390/agronomy14122970 - 13 Dec 2024
Viewed by 254
Abstract
Soil available nutrients (SANs) can be rapidly converted, absorbed, and utilized by crops. The study of the spatial distribution and variation of SANs, as well as their response to environmental factors, is crucial for precision fertilization and soil ecosystem function regulation. In this [...] Read more.
Soil available nutrients (SANs) can be rapidly converted, absorbed, and utilized by crops. The study of the spatial distribution and variation of SANs, as well as their response to environmental factors, is crucial for precision fertilization and soil ecosystem function regulation. In this study, 220 soil surface-layer samples (0–20 cm) were collected in 2019 from loquat orchards in the mid-low mountain hilly areas of central China to explore the spatial distribution and variation of SANs, as well as the effects of environmental factors (including the topography, vegetation index, soil property, and climate) on SANs, using a path analysis model. The results showed that the available potassium (AK) and ammonium nitrogen (AN) levels exhibited a moderate average content, which was 123.14 mg·kg−1 and 119.03 mg·kg−1, respectively, whereas available phosphorus (AP) levels displayed a high average content (26.78 mg·kg−1), and all three SANs showed an uneven spatial distributions. The nugget effect values of AK and AN ranged from 25% to 75%, indicating moderate spatial variation, and those of AP were <25%, suggesting high spatial variation. Furthermore, the mean annual precipitation (MAP) had a direct positive effect on AK levels, while slope had an indirect effect on AK levels through the ratio vegetation index (RVI), suggesting that precipitation had greater impact on AK levels than topography. Soil erosion had a direct negative effect on AP and AN levels, accelerating the loss of SANs. The MAP and soil type (ST) directly affected soil AN content. Specifically, sufficient precipitation and fine soil facilitated the storage and conversion of AN in soil. Taken together, our path analysis indicated that all the four categories of environmental factors had direct or indirect effects on SANs, and our geostatistical analysis revealed the spatial distribution and variation law of SANs in the study area. Our findings offer a theoretical basis and valuable references for achieving precision fertilization in orchards and improving loquat yield and quality. Full article
(This article belongs to the Section Soil and Plant Nutrition)
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<p>The location of the study area and sampling sites with the digital elevation model (DEM).</p>
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<p>The results of the boxplot statistical analysis of AK, AP, and AN under different slope, RVI, ST, SE, and MAP levels. AK: available potassium; AP: available phosphorus; AN: ammonium nitrogen; RVI: ratio vegetation index; ST: soil type; SE: soil erosion; and MAP: mean annual precipitation.</p>
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<p>Spatial distribution maps for (<b>a</b>) AK, (<b>b</b>) AP, and (<b>c</b>) AN in the study area. AK: available potassium; AP: available phosphorus; AN: ammonium nitrogen.</p>
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<p>The correlation between environmental variables and AK, AP, and AN. The dot size represents the correlation coefficient value (the larger the dot size, the higher the coefficient value). Positive and negative correlations are indicated by blue and red dots, respectively. AK: available potassium; AP: available phosphorus; AN: ammonium nitrogen; Ele: elevation; TWI: topographic wetness index; DVI: difference vegetation index; EVI: enhanced vegetation index; RVI: ratio vegetation index; NDVI: normalized difference vegetation index; SRC: soil retention capacity; ST: soil type; SWHC: soil water-holding capacity; SE: Soil erosion modulus; MAP: mean annual precipitation; MAT: mean annual temperature; and NPP: net primary production. *, <span class="html-italic">p</span> &lt; 0.05; **, <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>A path model of the relationship between the topography, vegetation index, soil property, climate, and AK, AP, and AN. The thickness of the line represents the importance of environmental factors. Blue indicates a positive correlation, while red denotes a negative correlation. The fitting test results show that the model has desirable fitting (<math display="inline"><semantics> <mrow> <msup> <mi>χ</mi> <mn>2</mn> </msup> <mo>/</mo> <mi>d</mi> <mi>f</mi> <mo>=</mo> </mrow> </semantics></math> 2.31, GFI = 0.959, NFI = 0.904). AK: available potassium; AP: available phosphorus; AN: ammonium nitrogen; RVI: ratio vegetation index; SE: soil erosion modulus; MAP: mean annual precipitation; and ST: soil type.</p>
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27 pages, 6572 KiB  
Article
Predictive Models for Correlation of Compaction Characteristics of Weakly Cohesive Soils
by Carmen Nicoleta Debeleac, Andrei Buraga and Daniel Sorin Miron
Appl. Sci. 2024, 14(24), 11647; https://doi.org/10.3390/app142411647 - 13 Dec 2024
Viewed by 318
Abstract
In this paper, an investigation was conducted to characterize the behavior of weakly cohesive soil subjected to vibratory compaction. Thus, the authors developed a model for weakly cohesive soils, defined by inter-parametric laws that consider their initial state and predict the evolution of [...] Read more.
In this paper, an investigation was conducted to characterize the behavior of weakly cohesive soil subjected to vibratory compaction. Thus, the authors developed a model for weakly cohesive soils, defined by inter-parametric laws that consider their initial state and predict the evolution of state parameters resulting from static and vibratory compaction processes, depending on the number of equipment passes. Four types of soil were proposed for testing, with different initial characteristics such as dry density, longitudinal modulus, and moisture content. Some correlations between main parameters involved in the compaction process were established, considering soil mechanical properties, compaction equipment, and in situ technology applied. The results obtained in the computational environment were implemented to predict the performance compaction process for an overall assessment. This research contributes to database development by offering valuable insights for specialists aiming to apply Industry 4.0 digitalization practices, which stipulate the use of predictability laws in pre-assessing the degree of soil compaction (or settlement) to estimate and maximize the efficiency of road construction or foundation works. These insights help optimize design processes, enhance functional performance, improve resource utilization, and ensure long-term sustainability in large infrastructure projects built on these soils. Full article
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<p>The location of the compacted section and the measurement points and the method of carrying out the tests on the experimental ground polygon built on the construction site.</p>
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<p>Steps for developing a mathematical model of a mechanical system.</p>
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<p>Voigt–Kelvin model description.</p>
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<p>Dependence of the static modulus of linear deformation on the dry density for weakly cohesive soils (experiments under static conditions).</p>
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<p>Comparative representation of the law E<sub>st</sub> = f(ρ<sub>d</sub>): experimental results (in blue) versus the proposed analytical model (in red).</p>
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<p>Representation of the law E<sub>st</sub> = f(ρ<sub>d</sub>) for the four types of soil, corresponding to the four measurement points: (<b>a</b>) F; (<b>b</b>) G; (<b>c</b>) H; (<b>d</b>) I.</p>
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<p>The law of variation of the density in the dry state as a function of the soil moisture content with the layer thickness for: (<b>a</b>) h<sub>c</sub> = 26.4 cm (point F); (<b>b</b>) h<sub>c</sub> = 24.5 cm (point G); (<b>c</b>) h<sub>c</sub> = 28 cm (point H); (<b>d</b>) h<sub>c</sub> = 29.5 cm (point I).</p>
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<p>The law of variation of the density in the dry state as a function of the soil moisture content with the layer thickness for: (<b>a</b>) h<sub>c</sub> = 26.4 cm (point F); (<b>b</b>) h<sub>c</sub> = 24.5 cm (point G); (<b>c</b>) h<sub>c</sub> = 28 cm (point H); (<b>d</b>) h<sub>c</sub> = 29.5 cm (point I).</p>
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<p>The law of variation of the contact area according to the degree of soil compaction (experimental measurements in the soil channel).</p>
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<p>The law of variation of the contact area according to soil settlement (experimental measurements in the soil channel).</p>
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<p>The approximation laws for the variation of the width of the contact area of the front vibratory drum of the ABG DD16 roller, during soil compaction (after the first three passes made in static working regime with the VV 170 roller).</p>
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<p>The regression curve and its analytical expression for the function Δh/h<sub>c</sub> − p, corresponding to the determinations on loess from Giurgiu (DN 2—km 39 + 200—Movilița site) [<a href="#B6-applsci-14-11647" class="html-bibr">6</a>].</p>
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<p>Current and cumulative settlement variation for compactor passes.</p>
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<p>Compaction degree variation in function by roller passes for the soil with different initial compaction degree: (<b>a</b>) point F with D<sub>i</sub> = 74.5%; (<b>b</b>) point G with D<sub>i</sub> = 79.7%; (<b>c</b>) point H with D<sub>i</sub> = 78.5%; (<b>d</b>) point I with D<sub>i</sub> = 81.7%.</p>
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<p>Comparison between approximation laws for the degree of compaction, for the first three static passes, corresponding to measurements from point F (<b>a</b>), point G (<b>b</b>), and point H (<b>c</b>).</p>
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<p>The variation of the degree of compaction according to the initial condition of the soil, the thickness of the layer and the number of passes of the compactor, corresponding to the measurements from points F, G, H, I.</p>
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<p>Curves of the main parameters that characterized the overall compaction process.</p>
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<p>Simulation results: (<b>a</b>) A = f(c,k); (<b>b</b>) Q<sub>0</sub> = f(c,k); (<b>c</b>) T = f(c,k).</p>
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14 pages, 12089 KiB  
Article
Changes and Trade-Offs of Ecological Service Functions of Public Welfare Forests (2000–2019) in Southwest Zhejiang Province, China
by Ziqiang Liu, Deguo Han, Limin Ye, Yuanke Xu and Yong Zhang
Forests 2024, 15(12), 2197; https://doi.org/10.3390/f15122197 - 13 Dec 2024
Viewed by 310
Abstract
Studying the factors influencing ecosystem regulation services in southwestern Zhejiang is of great significance for formulating reasonable pricing strategies for forest ecosystem regulation services and optimizing ecological security. This study constructed a theoretical framework for analyzing forest ecosystem regulation services and assessed the [...] Read more.
Studying the factors influencing ecosystem regulation services in southwestern Zhejiang is of great significance for formulating reasonable pricing strategies for forest ecosystem regulation services and optimizing ecological security. This study constructed a theoretical framework for analyzing forest ecosystem regulation services and assessed the spatiotemporal evolution and influencing factors of forest ecosystem regulation services using InVEST model calculations and spatial autocorrelation analysis. The results showed that all ecosystem services of forests in the study improved from 2000 to 2019, with the exception of soil conservation. The water conservation function increased significantly from 2000 to 2019, with an overall increase of 3.53%. The biodiversity conservation function in 2019 also increased significantly, with an average increase of 2.16% compared with 2000. The synergies mainly occurred between water source regulation and soil conservation, soil conservation and biodiversity, and forest recreation and carbon storage. Forest Reserve was precipitation, canopy closure, elevation, and soil texture, and their driving forces differed at different time scales. The trade-offs mainly occurred between soil conservation and forest recreation, forest recreation and biodiversity, and carbon storage and biodiversity. The research results provide a reference for achieving ecological protection and high-quality development in the southwestern region of Zhejiang. Full article
(This article belongs to the Special Issue Advances in Forest Carbon, Water Use and Growth Under Climate Change)
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<p>Geographical location of the research area.</p>
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<p>Land use type and ecosystem service distribution pattern of non-profit forests in southwestern Zhejiang in 2000. (<b>a</b>–<b>f</b>) are land use, biological diversity, water conservation, net primary production (NPP), soil conservation, and forest recreation, respectively.</p>
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<p>Land use type and ecosystem service distribution pattern of non-profit forests in southwest Zhejiang in 2019. (<b>a</b>–<b>f</b>) are land use, biological diversity, water conservation, net primary production (NPP), soil conservation, and forest recreation, respectively.</p>
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<p>Ecosystem service trade-offs of non-profit forests in southwest Zhejiang in 2000 and 2019. Biological diversity, BD; water conservation, WC; net primary production, NPP; soil conservation, SC; forest recreation, FR. ** represents <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Contribution rates of ecosystem service trade-off intensity driving factors in southwest Zhejiang in 2000 based geographic detector analysis (q). Biological diversity, BD; water conservation, WC; net primary production, NPP; soil conservation, SC; forest recreation, FR.</p>
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<p>Contribution rates of ecosystem service trade-off intensity driving factors in southwest Zhejiang in 2019 based geographic detector analysis (q). Biological diversity, BD; water conservation, WC; net primary production, NPP; soil conservation, SC; forest recreation, FR.</p>
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17 pages, 4420 KiB  
Article
Metagenomic Analysis Revealing the Impact of Water Contents on the Composition of Soil Microbial Communities and the Distribution of Major Ecological Functional Genes in Poyang Lake Wetland Soil
by Yuxin Long, Xiaomei Zhang, Xuan Peng, Huilin Yang, Haiyan Ni, Long Zou and Zhong’er Long
Microorganisms 2024, 12(12), 2569; https://doi.org/10.3390/microorganisms12122569 - 13 Dec 2024
Viewed by 397
Abstract
Poyang Lake is the largest freshwater lake in China, which boasts unique hydrological conditions and rich biodiversity. In this study, metagenomics technology was used to sequence the microbial genome of soil samples S1 (sedimentary), S2 (semi-submerged), and S3 (arid) with different water content [...] Read more.
Poyang Lake is the largest freshwater lake in China, which boasts unique hydrological conditions and rich biodiversity. In this study, metagenomics technology was used to sequence the microbial genome of soil samples S1 (sedimentary), S2 (semi-submerged), and S3 (arid) with different water content from the Poyang Lake wetland; the results indicate that the three samples have different physicochemical characteristics and their microbial community structure and functional gene distribution are also different, resulting in separate ecological functions. The abundance of typical ANME archaea Candidatus Menthanoperedens and the high abundance of mcrA in S1 mutually demonstrate prominent roles in the methane anaerobic oxidation pathway during the methane cycle. In S2, the advantageous bacterial genus Nitrospira with ammonia oxidation function is validated by a large number of nitrification functional genes (amoA, hao, nxrA), manifesting in that it plays a monumental role in nitrification in the nitrogen cycle. In S3, the dominant bacterial genus Nocardioides confirms a multitude of antibiotic resistance genes, indicating their crucial role in resistance and their emphatic research value for microbial resistance issues. The results above have preliminarily proved the role of soil microbial communities as indicators predicting wetland ecological functions, which will help to better develop plans for restoring ecological balance and addressing climate change. Full article
(This article belongs to the Section Environmental Microbiology)
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<p>The soil sampling sites of different water contents in Poyang Lake wetland successively showing S1, S2, S3 from left to right.</p>
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<p>Microbial community composition. (<b>A</b>) Relative abundance of the major phyla based on metagenomic sequences in the Poyang Lake wetland, (<b>B</b>) Relative abundance of the major genus. Unassigned: A sequence that has not been accurately identified or classified as a known biological species. Unclassified: Although DNA sequences or genes have been identified to a certain level, they cannot be clearly localized to specific biological taxonomic units at more specific taxonomic levels. Other: Species with relatively low abundance.</p>
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<p>RDA analysis is used to reflect the correlation between physicochemical factors and microbial species (phylum level) in the soil of the Poyang Lake wetland. The dashed arrow in the figure represents the level of microbial phylum, while the solid arrow represents physical and chemical factors. The arrow representing microbial species is closer to a certain physicochemical factor, indicating that the physicochemical factor has the greatest impact on that species’ abundance. C_N represents the carbon-to-nitrogen ratio of the soil.</p>
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<p>The abundance of key enzymes involved in six carbon fixation pathways in three samples. * Marks the significance of differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The abundance of methane cycling and functional genes driven by soil microorganisms in Poyang Lake wetland. The pink line represents the process of methane generation, and the blue line represents the process of methane oxidation. The genes on the arrows are the functional genes of the key enzymes in this turnover process. The different shapes represent different samples, and the filling colors from light to deep represent the abundance of the gene from low to high, while the letters “a”, “b”, and “c” indicate significant differences between samples.</p>
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<p>The nitrogen cycling driven by soil microorganisms and the abundance of its functional genes in the Poyang Lake wetland. Different colored arrows represent different nitrogen cycling processes in soil, and the genes on the arrows are the functional genes of the key enzymes in this turnover process. The different shapes represent different samples, and the filling colors from light to deep represent the abundance of the gene from low to high, while the letters “a”, “b”, and “c” indicate significant differences between samples.</p>
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<p>The abundance of resistance genes secreted by microorganisms in soils with different moisture contents in Poyang Lake wetland. “*” means significant differences (<span class="html-italic">p</span> &lt; 0.05) and “**” means extremely significant differences (<span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Correlation heatmap between soil physicochemical factors and genes related to carbon decomposition, methane cycle, and nitrogen cycle. Red represents a positive correlation, blue represents a negative correlation. The darker the color, the stronger the correlation.</p>
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19 pages, 4931 KiB  
Article
Assessing the Biodegradation of Low-Density Polyethylene Films by Candida tropicalis SLNEA04 and Rhodotorula mucilaginosa SLNEA05
by Randa Harrat, Ghania Bourzama, Gaëtan Burgaud, Emmanuel Coton, Aymen Bourezgui and Boudjema Soumati
Diversity 2024, 16(12), 759; https://doi.org/10.3390/d16120759 - 12 Dec 2024
Viewed by 250
Abstract
Environmental pollution resulting from the accumulation of plastic waste poses a major ecological challenge. Biodegradation of these polymers relies on microorganisms capable of decomposing them, generally through the biodeterioration, biofragmentation, assimilation, and mineralization stages. This study evaluates the contribution and efficacy of indigenous [...] Read more.
Environmental pollution resulting from the accumulation of plastic waste poses a major ecological challenge. Biodegradation of these polymers relies on microorganisms capable of decomposing them, generally through the biodeterioration, biofragmentation, assimilation, and mineralization stages. This study evaluates the contribution and efficacy of indigenous soil yeasts isolated from a northeastern Algerian landfill in degrading low-density polyethylene (LDPE) plastic bag films. Candida tropicalis SLNEA04 and Rhodotorula mucilaginosa SLNEA05 were identified through internal transcribed spacer (ITS) and large subunit ribosomal RNA gene sequencing. These isolates were then tested for their ability to biodegrade LDPE films and utilized as the sole carbon source in vitro in a mineral salt medium (MSM). The biodegradation effect was examined using scanning electron microscopy (SEM), attenuated total reflectance–Fourier transform infrared (ATR-FTIR) spectroscopy, and X-ray diffraction (XRD). After 30 days of incubation at 25 °C, a significant weight loss was observed compared to the control for both cultures: 7.60% and 5.53% for C. tropicalis and R. mucilaginosa, respectively. SEM analysis revealed morphological alterations, including cracks and holes, ATR-FTIR detected new functional groups (alcohols, alkynes, aldehydes, alkenes and ketones), while XRD identified changes in the polymer crystallinity and phase composition. These findings underscore the potential of the two yeast isolates in LDPE biodegradation, offering promising insights for future environmental applications. Full article
(This article belongs to the Section Microbial Diversity and Culture Collections)
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<p>(<b>a</b>) Geographic location of the Boumehadjer landfill. (<b>b</b>) Photograph of waste at the Boumehadjer landfill.</p>
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<p>(<b>a</b>) Phylogenetic tree generated via maximum likelihood analysis of D1/D2 sequence data from <span class="html-italic">Candida</span> species. The tree was rooted using <span class="html-italic">Rhodotorula mucilaginosa</span> CBS 316. ML bootstrap support values greater than 50% are displayed near the nodes. The isolate of interest, SLNEA04, is indicated in bold. (<b>b</b>) Phylogenetic tree generated via maximum likelihood analysis of D1/D2 sequence data from <span class="html-italic">Rhodotorula</span> species. The tree was rooted using <span class="html-italic">Candida albicans NRRL Y-12983</span>. ML bootstrap support values greater than 50% are displayed near the nodes. The isolate of interest, SLNEA05, is indicated in bold.</p>
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<p>Kinetics of LDPE film degradation by <span class="html-italic">C. tropicalis</span> SLNEA04 and <span class="html-italic">R. mucilaginosa</span> SLNEA05 compared to the control (<span class="html-italic">n</span> = 3).</p>
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<p>Surface morphology of LDPE films exposed to <span class="html-italic">C. tropicalis</span> SLNEA04 (<b>d</b>–<b>f</b>) and <span class="html-italic">R. mucilaginosa</span> SLNEA05 (<b>g</b>–<b>i</b>) for 30 days of incubation under SEM at 1000×; 1500×, and 2000× compared to the control (<b>a</b>–<b>c</b>). White arrows highlight examples of areas where surface alterations are visible.</p>
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<p>ATR-FTIR spectrum analysis of LDPE films exposed to <span class="html-italic">C. tropicalis</span> SLNEA04 (<b>b</b>) and <span class="html-italic">R. mucilaginosa</span> SLNEA05 (<b>c</b>) for 30 days of incubation in MSM compared to the control (<b>a</b>).</p>
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<p>ATR-FTIR spectrum analysis of LDPE films exposed to <span class="html-italic">C. tropicalis</span> SLNEA04 (<b>b</b>) and <span class="html-italic">R. mucilaginosa</span> SLNEA05 (<b>c</b>) for 30 days of incubation in MSM compared to the control (<b>a</b>).</p>
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<p>XRD spectrum of LDPE films exposed to <span class="html-italic">C. tropicalis</span> SLNEA04 (<b>b</b>) and <span class="html-italic">R. mucilaginosa</span> SLNEA05 (<b>c</b>) for 30 days of incubation compared to the control (<b>a</b>). Asterisks (*) indicate new peaks that appeared following degradation.</p>
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<p>XRD spectrum of LDPE films exposed to <span class="html-italic">C. tropicalis</span> SLNEA04 (<b>b</b>) and <span class="html-italic">R. mucilaginosa</span> SLNEA05 (<b>c</b>) for 30 days of incubation compared to the control (<b>a</b>). Asterisks (*) indicate new peaks that appeared following degradation.</p>
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