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Search Results (2,128)

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17 pages, 8876 KiB  
Article
Effects of Deep Tillage on Wheat Regarding Soil Fertility and Rhizosphere Microbial Community
by Junkang Sui, Chenyu Wang, Changqing Ren, Feifan Hou, Yuxuan Zhang, Xueting Shang, Qiqi Zhao, Xuewen Hua, Xunli Liu and Hengjia Zhang
Microorganisms 2024, 12(8), 1638; https://doi.org/10.3390/microorganisms12081638 (registering DOI) - 10 Aug 2024
Viewed by 321
Abstract
Wheat production is intrinsically linked to global food security. However, wheat cultivation is constrained by the progressive degradation of soil conditions resulting from the continuous application of fertilizers. This study aimed to examine the effects of deep tillage on rhizosphere soil microbial communities [...] Read more.
Wheat production is intrinsically linked to global food security. However, wheat cultivation is constrained by the progressive degradation of soil conditions resulting from the continuous application of fertilizers. This study aimed to examine the effects of deep tillage on rhizosphere soil microbial communities and their potential role in improving soil quality, given that the specific mechanisms driving these observed benefits remain unclear. Soil fertility in this research was evaluated through the analysis of various soil parameters, including total nitrogen, total phosphorus, total potassium, available phosphorus, and available potassium, among others. The high-throughput sequencing technique was utilized to examine the rhizosphere microbial community associated with deep tillage wheat. The findings indicated that deep tillage cultivation of wheat led to reduced fertility levels in the 0–20 cm soil layer in comparison with non-deep tillage cultivation. A sequencing analysis indicated that Acidobacteria and Proteobacteria are the dominant bacterial phyla, with Proteobacteria being significantly more abundant in the deep tillage group. The dominant fungal phyla identified were Ascomycota, Mortierellomycota, and Basidiomycota. Among bacterial genera, Arthrobacter, Bacillus, and Nocardioides were predominant, with Arthrobacter showing a significantly higher presence in the deep tillage group. The predominant fungal genera included Mortierella, Alternaria, Schizothecium, and Cladosporium. Deep tillage cultivation has the potential to enhance soil quality and boost crop productivity through the modulation of soil microbial community structure. Full article
(This article belongs to the Special Issue Rhizosphere Microbial Community 3.0)
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<p>Bacterial (<b>a</b>) and fungal sobs curves (<b>b</b>) were examined to assess the impact of a 3% dissimilarity cutoff on the identification of uncovered operational taxonomic units (OTUs). The abbreviation “DT” refers to deep tillage-cultivated wheat rhizosphere soil group, while “CK” represents the non-deep tillage-cultivated wheat rhizosphere soil group.</p>
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<p>Bacterial and fungal Shannon curves (<b>a</b>,<b>b</b>) were examined to assess the impact of a 3% dissimilarity cutoff on the identification of uncovered operational taxonomic units (OTUs). The abbreviation “DT” refers to the deep tillage-cultivated wheat rhizosphere soil group, while “CK” represents the non-deep tillage-cultivated wheat rhizosphere soil group.</p>
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<p>PCoA analysis and ANOSIM analysis of deep tillage and non-deep tillage cultivation of wheat rhizosphere soil microbes. The abbreviation “DT” refers to the deep tillage-cultivated wheat rhizosphere soil group, while “CK” represents the non-deep tillage-cultivated wheat rhizosphere soil group. (<b>a</b>, PCoA analysis of DT. <b>b</b>, PCoA analysis of CK. <b>c</b>, ANOSIM analysis of DT. <b>d</b>, ANOSIM analysi of CK.)</p>
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<p>Communities of bacteria and fungi in the study group. (<b>a</b>) Relative abundances of bacteria at the phylum level; (<b>b</b>) relative abundances of bacteria at the genus level; (<b>c</b>) relative abundances of fungi at the phylum level; (<b>d</b>) relative abundances of fungi at the genus level. The relative abundances of major genera are illustrated in stacked bar graphs. The abbreviation “DT” refers to the deep tillage-cultivated wheat rhizosphere soil group, while “CK” represents the non-deep tillage-cultivated wheat rhizosphere soil group.</p>
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<p>Hierarchical clustering of bacterial and fungal distributions. (<b>a</b>) Relative abundances of bacteria at the phylum level; (<b>b</b>) relative abundances of bacteria at the genus level; (<b>c</b>) relative abundances of fungi at the phylum level; (<b>d</b>) relative abundances of fungi at the genus level. The abbreviation “DT” refers to the deep tillage-cultivated wheat rhizosphere soil group, while “CK” represents the non-deep tillage-cultivated wheat rhizosphere soil group.</p>
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<p>Significant test of differences between the two groups. (<b>a</b>) Significant differences in bacteria at the phylum level; (<b>b</b>) significant differences in bacteria at the genus level; (<b>c</b>) significant differences in fungi at the phylum level; (<b>d</b>) significant differences in fungi at the genus level. The abbreviation “DT” refers to the deep tillage-cultivated wheat rhizosphere soil group, while “CK” represents the non-deep tillage-cultivated wheat rhizosphere soil group.</p>
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<p>Unique and shared genera of (<b>a</b>) bacteria and (<b>b</b>) fungi for the two groups in Venn diagram form. The abbreviation “DT” refers to the deep tillage-cultivated wheat rhizosphere soil group, while “CK” represents the non-deep tillage-cultivated wheat rhizosphere soil group. We analyzed three replicates for every treatment.</p>
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<p>Discriminant analysis of muti-level species differences obtained through LEfSe analysis. (<b>a</b>) Differences in bacterial multi-level species in the DT and CK groups. (<b>b</b>) Differences in fungal multi-level species in the DT and CK groups. Differently colored nodes represent microbial communities that are significantly enriched in their corresponding groups and have a significant impact on inter-group differences.</p>
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11 pages, 1858 KiB  
Article
Differences in Metabolic Characteristics of Rhizosphere Fungal Community of Typical Arboreal, Shrubby and Herbaceous Species in Oasis of Arid Region
by Yunxiang Tan, Yunhang Lv, Mengyu Xv, Laiye Qu and Wenjuan Wang
J. Fungi 2024, 10(8), 565; https://doi.org/10.3390/jof10080565 (registering DOI) - 10 Aug 2024
Viewed by 149
Abstract
Populus euphratica, Tamarix ramosissima, and Sophora alopecuroides are, respectively, typical arboreal, shrubby, and herbaceous species in oases of arid regions. It is important to study the difference in metabolic characteristics of the rhizosphere fungal community of these plant species and their [...] Read more.
Populus euphratica, Tamarix ramosissima, and Sophora alopecuroides are, respectively, typical arboreal, shrubby, and herbaceous species in oases of arid regions. It is important to study the difference in metabolic characteristics of the rhizosphere fungal community of these plant species and their relationships with soil factors for the preservation of delicate arid oasis ecosystems with future environmental changes. In this study, we, respectively, collected 18 rhizosphere soil samples of P. euphratica, T. ramosissima, and S. alopecuroides to explore the difference in rhizosphere fungal metabolic characteristics of different plant life forms and their underlying driving factors. The results showed that (1) soil physicochemical properties (including soil water content, pH, etc.) were significantly different among different plant species (p < 0.05). (2) Rhizosphere fungal metabolic characteristics were significantly different between S. alopecuroides and T. ramosissima (ANOSIM, p < 0.05), which was mainly caused by the different utilization of carboxylic carbon. (3) The RDA showed that the main driving factors of the variations in rhizosphere fungal metabolic characteristics were different among different plant species. The main explanatory variables of the variations in the metabolic characteristics of the rhizosphere fungal community were carbon to nitrogen ratio (23%) and available potassium (17.4%) for P. euphratica, while soil organic carbon (23.1%), pH (8.6%), and total nitrogen (8.2%) for T. ramosissima, and soil clay content (36.6%) and soil organic carbon (12.6%) for S. alopecuroides. In conclusion, the variations in rhizosphere fungal metabolic characteristics in arid oases are dominantly affected by soil factors rather than plant life forms. Full article
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<p>The AWCD values of the different plant life forms during the Biolog-FF Microplate incubation.</p>
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<p>Differences in metabolic characteristics of rhizosphere fungal community among different plant life forms. Notes: * denotes <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Utilization intensities of the six kinds of carbon sources by the rhizosphere fungal community of the different plant life forms. Note: different letters indicate significant differences at the 0.05 level.</p>
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<p>Redundancy analysis of carbon metabolic characteristics of rhizosphere fungal community of different plants.</p>
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14 pages, 3698 KiB  
Article
Biological Control Potential of Bacillus subtilis Isolate 1JN2 against Fusarium Wilt on Cucumber
by Wei Yang, Lan Wang, Xiao Li, Haixia Yan, Beibei Zhong, Xinru Du, Qi Guo, Tingting He and Yuming Luo
Horticulturae 2024, 10(8), 843; https://doi.org/10.3390/horticulturae10080843 - 9 Aug 2024
Viewed by 206
Abstract
Cucumber is one of the top ten vegetables globally and is widely cultivated worldwide. However, Fusarium wilt, caused by Fusarium oxysporum f. sp. Cucumerinum, is one of the most serious soil-borne diseases in cucumber cultivation, causing significant economic losses. Biological control has [...] Read more.
Cucumber is one of the top ten vegetables globally and is widely cultivated worldwide. However, Fusarium wilt, caused by Fusarium oxysporum f. sp. Cucumerinum, is one of the most serious soil-borne diseases in cucumber cultivation, causing significant economic losses. Biological control has great potential in the prevention of cucumber wilt disease, but the mechanism involved still needs further research. In this study, biocontrol isolate Bacillus subtilis 1JN2, which was isolated in our previous work, was evaluated in field conditions against Fusarium wilt, and the rhizosphere fungal diversity was analyzed. The results indicated that the biocontrol efficacy of B. subtilis 1JN2 reached 58.5% compared with the blank control, and the population density of F. oxysporum in the rhizosphere decreased from 495 copies/g of soil before inoculation to 20 copies/g 14 days after treatment. High-throughput sequencing demonstrated that after an inoculation of 1JN2, the populations that decreased significantly include the genera of Olpidium and Pseudallescheria, from more than 20% to less than 8%. And the most increased population belonged to the family Chaetomiaceae, from 6.82% to 18.77%, 12.39%, 44.41%, and 19.41% at the four sample time points after treatment. In addition, soil-related enzyme activities, including catalase, soil dehydrogenase, alkaline phosphatase, and polyphenol oxidase, were analyzed before and after treatment with 1JN2. The results indicated that all the enzyme activities showed an upward trend following inoculation. These findings demonstrate the potential of using B. subtilis 1JN2 as a biocontrol agent for controlling Fusarium wilt in cucumber. Full article
(This article belongs to the Special Issue Plant Disease Management and Pathogens Control in Horticulture)
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<p>The population density of <span class="html-italic">Fusarium oxysporum</span> f. sp. <span class="html-italic">cucumerinum</span> (FOC) in the rhizosphere soil of cucumber after treatment with <span class="html-italic">Bacillus subtilis</span> 1JN2. Values are means and standard deviations of three replicates.</p>
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<p>Trend of changes at the genus level of cucumber rhizosphere fungi at different time points after the treatment of field soil with the biocontrol agent <span class="html-italic">Bacillus subtilis</span> 1JN2. PLS-DA = Partial Least Squares Discriminant Analysis.</p>
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<p>Analysis of the rhizo-fungal community of cucumber after treatment with <span class="html-italic">Bacillus subtilis</span> 1JN2. (<b>A</b>) Circos analysis at the phyla level; (<b>B</b>) Circos analysis at the genus level; (<b>C</b>–<b>G</b>) analysis at the genus level by sampling time point. In (<b>A</b>,<b>B</b>), J1–J3, J4–6, J7–J9, J10–J12, and J13–J15 represent three repetitions of each sampling time point, respectively. The small semicircle (left half-circle) represents the composition of species in the sample. The color of the outer color band represents which group it comes from, the color of the inner color band represents the species, and the length represents the relative abundance of the species in the corresponding sample. The large semicircle (right half-circle) represents the distribution ratio of species in different samples at the taxonomic level, with the outer color band representing the species, the inner color band representing different groups, and the length representing the distribution ratio of the sample in a certain species.</p>
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<p>Analysis of the rhizo-fungal community of cucumber after treatment with <span class="html-italic">Bacillus subtilis</span> 1JN2. (<b>A</b>) Circos analysis at the phyla level; (<b>B</b>) Circos analysis at the genus level; (<b>C</b>–<b>G</b>) analysis at the genus level by sampling time point. In (<b>A</b>,<b>B</b>), J1–J3, J4–6, J7–J9, J10–J12, and J13–J15 represent three repetitions of each sampling time point, respectively. The small semicircle (left half-circle) represents the composition of species in the sample. The color of the outer color band represents which group it comes from, the color of the inner color band represents the species, and the length represents the relative abundance of the species in the corresponding sample. The large semicircle (right half-circle) represents the distribution ratio of species in different samples at the taxonomic level, with the outer color band representing the species, the inner color band representing different groups, and the length representing the distribution ratio of the sample in a certain species.</p>
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<p>Analysis of the rhizo-fungal community of cucumber after treatment with <span class="html-italic">Bacillus subtilis</span> 1JN2. (<b>A</b>) Circos analysis at the phyla level; (<b>B</b>) Circos analysis at the genus level; (<b>C</b>–<b>G</b>) analysis at the genus level by sampling time point. In (<b>A</b>,<b>B</b>), J1–J3, J4–6, J7–J9, J10–J12, and J13–J15 represent three repetitions of each sampling time point, respectively. The small semicircle (left half-circle) represents the composition of species in the sample. The color of the outer color band represents which group it comes from, the color of the inner color band represents the species, and the length represents the relative abundance of the species in the corresponding sample. The large semicircle (right half-circle) represents the distribution ratio of species in different samples at the taxonomic level, with the outer color band representing the species, the inner color band representing different groups, and the length representing the distribution ratio of the sample in a certain species.</p>
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<p>Rhizosphere soil enzyme activities of cucumber after treatment with <span class="html-italic">Bacillus subtilis</span> 1JN2. Values are means of standard deviations of three replicates.</p>
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17 pages, 1947 KiB  
Article
Radiation-Tolerant Fibrivirga spp. from Rhizosphere Soil: Genome Insights and Potential in Agriculture
by Sathiyaraj Srinivasan
Genes 2024, 15(8), 1048; https://doi.org/10.3390/genes15081048 - 9 Aug 2024
Viewed by 192
Abstract
The rhizosphere of plants contains a wide range of microorganisms that can be cultivated and used for the benefit of agricultural practices. From garden soil near the rhizosphere region, Strain ES10-3-2-2 was isolated, and the cells were Gram-negative, aerobic, non-spore-forming rods that were [...] Read more.
The rhizosphere of plants contains a wide range of microorganisms that can be cultivated and used for the benefit of agricultural practices. From garden soil near the rhizosphere region, Strain ES10-3-2-2 was isolated, and the cells were Gram-negative, aerobic, non-spore-forming rods that were 0.3–0.8 µm in diameter and 1.5–2.5 µm in length. The neighbor-joining method on 16S rDNA similarity revealed that the strain exhibited the highest sequence similarities with “Fibrivirga algicola JA-25” (99.2%) and Fibrella forsythia HMF5405T (97.3%). To further explore its biotechnological potentialities, we sequenced the complete genome of this strain employing the PacBio RSII sequencing platform. The genome of Strain ES10-3-2-2 comprises a 6,408,035 bp circular chromosome with a 52.8% GC content, including 5038 protein-coding genes and 52 RNA genes. The sequencing also identified three plasmids measuring 212,574 bp, 175,683 bp, and 81,564 bp. Intriguingly, annotations derived from the NCBI-PGAP, eggnog, and KEGG databases indicated the presence of genes affiliated with radiation-resistance pathway genes and plant-growth promotor key/biofertilization-related genes regarding Fe acquisition, K and P assimilation, CO2 fixation, and Fe solubilization, with essential roles in agroecosystems, as well as genes related to siderophore regulation. Additionally, T1SS, T6SS, and T9SS secretion systems are present in this species, like plant-associated bacteria. The inoculation of Strain ES10-3-2-2 to Arabidopsis significantly increases the fresh shoot and root biomass, thereby maintaining the plant quality compared to uninoculated controls. This work represents a link between radiation tolerance and the plant-growth mechanism of Strain ES10-3-2-2 based on in vitro experiments and bioinformatic approaches. Overall, the radiation-tolerant bacteria might enable the development of microbiological preparations that are extremely effective at increasing plant biomass and soil fertility, both of which are crucial for sustainable agriculture. Full article
(This article belongs to the Special Issue Feature Papers in Microbial Genetics in 2024)
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<p>The genome-based phylogenetic tree of ES10-3-2-2 and its related type strains determined using data from the Type Strain Genome Server. The phylogenetic tree was constructed using the calculated intergenomic distances to infer a balanced minimum evolution tree. This analysis utilized the FASTME v.2.1.6.1 software, incorporating Subtree Pruning and Regrafting (SPR) post-processing [<a href="#B17-genes-15-01048" class="html-bibr">17</a>] to refine the tree topology. Branch support was determined through 100 pseudo-bootstrap replicates. The resulting trees were midpoint-rooted [<a href="#B18-genes-15-01048" class="html-bibr">18</a>] and visualized using MEGA (v.8.2) software.</p>
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<p>Circular map of the Strain ES10-3-2-2’s chromosome and plasmids. The outer circle shows the scale in metabases (Mb). The representations, from the outer to the inner circle, are forward- and reverse-strand CDSs.</p>
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<p>Predicted components of T9SS and the gliding motility genes used in the genome of ES10-3-2-2. The representative image illustrates the gene components associated with the Type IX secretion system (T9SS) and gliding motility, as predicted by T9GPred [<a href="#B26-genes-15-01048" class="html-bibr">26</a>].</p>
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<p>Effect of Strain ES10-3-2-2 on plant-growth parameters. (<b>A</b>) The surface of leaves detached from 20-day-old (20 DAT) grown plants. Control (no bacterial suspension), Fe-EDTA-treated plant and 2 × 10<sup>6</sup> cfu/mL bacterial suspension. (<b>B</b>) Graphical representation of the fresh weights of shoots and roots at 20 DAT. The median and SE were calculated with eight plants per treatment. Significant differences between the treated plants were *, <span class="html-italic">p</span> &lt; 0.02 and **, <span class="html-italic">p</span> &lt; 0.05.</p>
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17 pages, 14291 KiB  
Article
Amaranth Plants with Various Color Phenotypes Recruit Different Soil Microorganisms in the Rhizosphere
by Xin-Ru Lin, Da Yang, Yu-Fei Wei, Dian-Cao Ding, Hui-Ping Ou and Shang-Dong Yang
Plants 2024, 13(16), 2200; https://doi.org/10.3390/plants13162200 - 8 Aug 2024
Viewed by 346
Abstract
To explore and utilize the abundant soil microorganisms and their beneficial functions, high-throughput sequencing technology was used to analyze soil microbial compositions in the rhizosphere of red and green amaranth varieties. The results showed that significant differences in soil microbial composition could be [...] Read more.
To explore and utilize the abundant soil microorganisms and their beneficial functions, high-throughput sequencing technology was used to analyze soil microbial compositions in the rhizosphere of red and green amaranth varieties. The results showed that significant differences in soil microbial composition could be found in the rhizosphere of amaranth plants with different color phenotypes. Firstly, soil bacterial compositions in the rhizosphere were significantly different between red and green amaranths. Among them, Streptomyces, Pseudonocardia, Pseudolabrys, Acidibacter, norank_ f_ Micropepsaceae, Bradyrhizobium, and Nocardioides were the unique dominant soil bacterial genera in the rhizosphere of red amaranth. In contrast, Conexibacter, norank_f_norank_o_norank_c_TK10, and norank_f_ norank_o_ norank_ c_AD3 were the special dominant soil bacterial genera in the rhizosphere of green amaranth. Additionally, even though the soil fungal compositions in the rhizosphere were not significantly different between red and green amaranths, the abundance of the dominant soil fungal genera in the rhizosphere showed significant differences between red and green amaranths. For example, unclassified_k__Fungi, Fusarium, Cladophialophora, unclassified_c__Sordariomycetes and unclassified_p__Chytridiomycota significantly enriched as the dominant soil fungal genera in the rhizosphere of the red amaranth. In contrast, Aspergillues only significantly enriched as the dominant soil fungal genus in the rhizosphere of green amaranth. All of the above results indicated that amaranth with various color phenotypes exactly recruited different microorganisms in rhizosphere, and the enrichments of soil microorganisms in the rhizosphere could be speculated in contributing to amaranth color formations. Full article
(This article belongs to the Section Plant–Soil Interactions)
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<p>Comparison of soil bacterial communities in the rhizospheres of red and green amaranth plants; (<b>a</b>) the non-metric multidimensional scaling (NMDS) of soil bacteria in the rhizospheres of red and green amaranth plants at the operational taxonomic unit (OTU) level; (<b>b</b>) the partial least squares discriminant analysis (PLS-DA) of soil bacteria in the rhizospheres of red and green amaranth plants at the operational taxonomic unit (OTU) level; (<b>c</b>) the non-metric multidimensional scaling (NMDS) of soil fungi in the rhizospheres of red and green amaranth plants at the operational taxonomic unit (OTU) level; (<b>d</b>) the partial least squares discriminant analysis (PLS-DA) of soil fungi in the rhizospheres of red and green amaranth plants at the operational taxonomic unit (OTU) level.</p>
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<p>Venn plot analysis of soil bacteria (<b>a</b>,<b>b</b>) and fungi (<b>c</b>,<b>d</b>) in the rhizosphere of red and green amaranths at the genus and operational taxonomic unit (OTU) levels.</p>
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<p>Composition of soil bacterial communities in the rhizosphere of red and green amaranth plants at the phylum (<b>a</b>) and genus (<b>b</b>) levels. Composition of soil fungi communities in the rhizosphere of red and green amaranth plants at the phylum (<b>c</b>) and genus (<b>d</b>) levels.</p>
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<p>LEfSe analysis of soil bacterial (<b>a</b>) and fungi (<b>b</b>) communities in the rhizospheres of red and green amaranth plants.Score plots of bacterial (<b>c</b>) and fungi (<b>d</b>) communities in the rhizospheres of red and green amaranth plants.Pathologically, nodes indicate microbial taxa that are significantly enriched in the corresponding group and have a significant effect on the differences between groups (p, phylum; C, class; 0, order; f, family; and g, genus). (<span class="html-italic">p</span> &lt; 0.05, LDA scores ≥ 3.5).</p>
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<p>Collinear network analysis of the soil bacterial (<b>a</b>,<b>b</b>) and fungi (<b>c</b>,<b>d</b>) communities in the rhizospheres of red and green amaranths. Different levels are indicated by different prefixes (p, phylum; g, genus). The size of the nodes in the graph indicates the size of the species abundance, and different colors indicate different species; the colors of the connecting lines indicate positive and negative correlations, with red indicating positive correlation and green indicating negative correlation (<span class="html-italic">p</span> &lt; 0.05); the thickness of the lines indicates the size of the correlation coefficient; the coarser the line is, the greater the correlation between the species; and the greater the number of lines is, the closer the connection between the species and other species.</p>
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<p>Functional analysis of soil bacteria (<b>a</b>,<b>b</b>) and fungi (<b>c</b>) in the rhizospheres of red and green amaranths. Different lowercase letters indicate significant differences between soil microbes of different amaranth color varieties (<span class="html-italic">p</span> &lt; 0.05).</p>
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18 pages, 7706 KiB  
Article
Maize–Soybean Rotation and Intercropping Increase Maize Yield by Influencing the Structure and Function of Rhizosphere Soil Fungal Communities
by Liqiang Zhang, Yuhan Yang, Zehang Zhao, Yudi Feng, Baoyin Bate, Hongyu Wang, Qiuzhu Li and Jinhu Cui
Microorganisms 2024, 12(8), 1620; https://doi.org/10.3390/microorganisms12081620 - 8 Aug 2024
Viewed by 297
Abstract
Soil-borne diseases are exacerbated by continuous cropping and negatively impact maize health and yields. We conducted a long-term (11-year) field experiment in the black soil region of Northeast China to analyze the effects of different cropping systems on maize yield and rhizosphere soil [...] Read more.
Soil-borne diseases are exacerbated by continuous cropping and negatively impact maize health and yields. We conducted a long-term (11-year) field experiment in the black soil region of Northeast China to analyze the effects of different cropping systems on maize yield and rhizosphere soil fungal community structure and function. The experiment included three cropping systems: continuous maize cropping (CMC), maize–soybean rotation (MSR), and maize–soybean intercropping (MSI). MSI and MSR resulted in a 3.30–16.26% lower ear height coefficient and a 7.43–12.37% higher maize yield compared to CMC. The richness and diversity of rhizosphere soil fungi were 7.75–20.26% lower in MSI and MSR than in CMC. The relative abundances of Tausonia and Mortierella were associated with increased maize yield, whereas the relative abundance of Solicoccozyma was associated with decreased maize yield. MSI and MSR had higher proportions of wood saprotrophs and lower proportions of plant pathogens than CMC. Furthermore, our findings indicate that crop rotation is more effective than intercropping for enhancing maize yield and mitigating soil-borne diseases in the black soil zone of Northeast China. This study offers valuable insights for the development of sustainable agroecosystems. Full article
(This article belongs to the Special Issue State-of-the-Art Environmental Microbiology in China (2023–2024))
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<p>Monthly average rainfall and average temperature during the sampling period (2019–2023).</p>
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<p>Experimental design. (<b>a</b>) Aerial photograph of the long-term field experiment; (<b>b</b>) diagram of the field distribution of the three cropping patterns; 1–4 indicate replicate plots; (<b>c</b>–<b>e</b>) diagram of the distribution of crops planted in each treatment; 65 cm refers to the space between rows, while 20.5 cm and 7.7 cm refer to the space between plants within a given row. Continuous maize cropping (CMC), maize–soybean rotation (MSR), maize–soybean intercropping (MSI).</p>
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<p>Effects of cropping system on maize growth in 2019–2023. (<b>a</b>) Plant height, (<b>b</b>) stem diameter, (<b>c</b>) ear height, and (<b>d</b>) ear position coefficient. Values are means ± SE, n = 10 replicates. The lowercase letters (a–c) indicate differences among cropping systems within the same year, while the uppercase letters (A–D) indicate differences among years under the same cropping system (<span class="html-italic">p</span> &lt; 0.05). MSI, maize–soybean intercropping; MSR, maize–soybean rotation; CMC, continuous maize cropping.</p>
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<p>Effects of cropping system on maize yield and yield components in 2019–2023. (<b>a</b>) Ear length, (<b>b</b>) ear diameter, (<b>c</b>) grain number per ear, (<b>d</b>) bald tip length, (<b>e</b>) 100-grain weight, and (<b>f</b>) yield. Values are mean ± SE, n = 10 replicates. The lowercase letters (a–c) indicate differences among cropping systems within the same year, while the uppercase letters (A–D) indicate differences among years under the same cropping pattern (<span class="html-italic">p</span> &lt; 0.05). MSI, maize–soybean intercropping; MSR, maize–soybean rotation; CMC, continuous maize cropping.</p>
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<p>Effects of cropping system on maize rhizosphere soil fungal richness and diversity. (<b>a</b>) Chao1, (<b>b</b>) number of observed species, (<b>c</b>) Faith’s phylogenetic diversity (PD), and (<b>d</b>) Shannon diversity. The two ends of the box plot are the upper and lower quartiles, the horizontal line in the middle indicates the median, and the lines connecting the two ends are the minimum and maximum values (<span class="html-italic">n</span> = 5). MSI, maize–soybean intercropping; MSR, maize–soybean rotation; CMC, continuous maize cropping.</p>
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<p>NMDS plot of maize rhizosphere soil fungal communities under different cropping systems. Stress = 0.1091. MSI, maize–soybean intercropping; MSR, maize–soybean rotation; CMC, continuous maize cropping.</p>
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<p>Composition of fungal genera in maize rhizosphere soil under different cropping systems. MSI, maize–soybean intercropping; MSR, maize–soybean rotation; CMC, continuous maize cropping.</p>
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<p>Co-occurrence networks of maize rhizosphere soil fungi under different cropping systems. (<b>a</b>) Maize–soybean intercropping, (<b>b</b>) maize–soybean rotation, and (<b>c</b>) continuous maize cropping. Circles indicate different genera. Genera from the same phylum are labeled with the same color. The size of the circles indicates the average abundance of the genera, lines indicate correlations between two genera, and the line thickness indicates the strength of the correlation. Orange lines indicate positive correlations and purple lines indicate negative correlations.</p>
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<p>Function of maize rhizosphere soil fungi under different cropping patterns predicted using FUNGuild. (<b>a</b>–<b>c</b>) Primary metabolic pathways ((<b>a</b>) MSI; (<b>b</b>) MSR; (<b>c</b>) CMC)) and (<b>d</b>) secondary metabolic pathways. MSI, maize–soybean intercropping; MSR, maize–soybean rotation; CMC, continuous maize cropping.</p>
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<p>Effects of cropping systems and fungal community structure and function on maize yield. (<b>a</b>) Structural equation model (SEM) of the effects of different cropping systems on the structure and function of the fungal community in maize rhizosphere soil and on yield. Blue arrows indicate negative correlations and red arrows indicate positive correlations between variables. MSI, maize–soybean intercropping; MSR, maize–soybean rotation; CMC, continuous maize cropping; FCD, fungal community diversity; FCR, fungal community richness; PP, relative abundance of plant pathogens. (<b>b</b>) Correlation analysis between dominant soil fungal genera and maize yield. * <span class="html-italic">p</span> &lt; 0.05.</p>
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19 pages, 4100 KiB  
Article
Impact of Intercropping Five Medicinal Plants on Soil Nutrients, Enzyme Activity, and Microbial Community Structure in Camellia oleifera Plantations
by Azuo Bajiu, Kai Gao, Guangyu Zeng and Yuanhao He
Microorganisms 2024, 12(8), 1616; https://doi.org/10.3390/microorganisms12081616 - 8 Aug 2024
Viewed by 222
Abstract
Intercropping medicinal plants plays an important role in agroforestry that can improve the physical, chemical, and biological fertility of soil. However, the influence of intercropping medicinal plants on the Camellia oleifera soil properties and bacterial communities remains elusive. In this study, five intercropping [...] Read more.
Intercropping medicinal plants plays an important role in agroforestry that can improve the physical, chemical, and biological fertility of soil. However, the influence of intercropping medicinal plants on the Camellia oleifera soil properties and bacterial communities remains elusive. In this study, five intercropping treatment groups were set as follows: Curcuma zedoaria/C. oleifera (EZ), Curcuma longa/C. oleifera (JH), Clinacanthus nutans/C. oleifera (YDC), Fructus Galangae/C. oleifera (HDK), and Ficus simplicissima/C. oleifera (WZMT). The soil chemical properties, enzyme activities, and bacterial communities were measured and analyzed to evaluate the effects of different intercropping systems. The results indicated that, compared to the C. oleifera monoculture group, YDC and EZ showed noticeable impacts on the soil chemical properties with a significant increase in total nitrogen (TN), nitrate nitrogen (NN), available nitrogen (AN), available phosphorus (AP), and available potassium (AK). Among them, the content of TN and AK in the rhizosphere soil of Camellia oleifera in the YDC intercropping system was the highest, which was 7.82 g/kg and 21.94 mg/kg higher than CK. Similarly, in the EZ intercropping system, the content of NN and OM in the rhizosphere soil of Camellia oleifera was the highest, which was higher than that of CK at 722.33 mg/kg and 2.36 g/kg, respectively. Curcuma longa/C. oleifera (JH) and Clinacanthus nutans/C. oleifera (YDC) had the most effect on soil enzyme activities. Furthermore, YDC extensively increased the activities of hydrogen peroxide and acid phosphatase enzymes; the increase was 2.27 mg/g and 3.21 mg/g, respectively. While JH obviously increased the urease activity, the diversity of bacterial populations in the rhizosphere soil of the intercropping plants decreased, especially the Shannon index of YDC and HDK. Compared with the monoculture group, the bacterial community abundance and structure of JH and YDC were quite different. The relative abundance of Actinobacteriota and Firmicutes was increased in YDC, and that of Acidobacteriota and Myxococcota was increased in JH. According to the redundancy analysis (RDA), pH, total potassium, and soil catalase activity were identified as the main factors influencing the microbial community structure of the intercropping systems. In conclusion, intercropping with JH and YDC increased the relative abundance of the dominant bacterial communities, improved the microbial community structure, and enhanced the soil nutrients and enzyme activities. Therefore, in the future, these two medicinal plants can be used for intercropping with C. oleifera. Full article
(This article belongs to the Section Plant Microbe Interactions)
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<p>Box diagram of ANOSIM/Adonis analysis at phylum level. CK: Monoculture of <span class="html-italic">C. oleifera</span>. EZ: <span class="html-italic">C. zedoaria</span>/<span class="html-italic">C. oleifera</span>, JH: <span class="html-italic">C. longa</span>/<span class="html-italic">C. oleifera</span>, YDC: <span class="html-italic">C. nutans</span>/<span class="html-italic">C. oleifera</span>, HDK: <span class="html-italic">F. Galangae</span>/<span class="html-italic">C. oleifera</span>, WZMT: <span class="html-italic">F. simplicissima</span>/<span class="html-italic">C. oleifera</span>.</p>
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<p>Kruskal–Wallis H test results of Shannon index. The head and tail of a line segment marked with significance indicate the difference between the two samples. *, **, and ***: differences significant at the 0.05, 0.01, and 0.001 levels, respectively.</p>
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<p>Venn diagram of the composition of bacterial communities in the rhizospheres of <span class="html-italic">C. oleifera</span> after intercropping with different medicinal plants. Different colours represent different treatments; the numbers refer to the numbers of species common to multiple treatments in overlapping and non-overlapping sections.</p>
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<p>Relative abundance of species composition in different medicinal plants. (<b>a</b>) Relative abundance of species composition at phylum level; (<b>b</b>) Relative abundance of species composition at genus level. Among the top 50 bacteria of genus level, the top 10 dominant bacteria of existing classification are selected in the figure. CK: Monoculture of <span class="html-italic">C. oleifera</span>. EZ: <span class="html-italic">C. zedoaria</span>/<span class="html-italic">C. oleifera</span>, JH: <span class="html-italic">C. longa</span>/<span class="html-italic">C. oleifera</span>, YDC: <span class="html-italic">C. nutans</span>/<span class="html-italic">C. oleifera</span>, HDK: <span class="html-italic">F. Galangae</span>/<span class="html-italic">C. oleifera</span>, WZMT: <span class="html-italic">F. simplicissima</span>/<span class="html-italic">C. oleifera</span>.</p>
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<p>Variations in microbial communities in the rhizospheres of <span class="html-italic">C. oleifera</span> after intercropping with different medicinal plants. At a given taxonomic level, the Y-axis represents the species, the X-axis represents the average relative abundance of species in different treatments, and columns of different colours represent different treatments. *, **, and ***: differences significant at the 0.05, 0.01, and 0.001 levels, respectively. (<b>a</b>): Between-group significant difference test plot at the phylum level. (<b>b</b>): Between-group significant difference test plot at the genus level.</p>
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<p>Variations in microbial communities in the rhizospheres of <span class="html-italic">C. oleifera</span> after intercropping with different medicinal plants. At a given taxonomic level, the Y-axis represents the species, the X-axis represents the average relative abundance of species in different treatments, and columns of different colours represent different treatments. *, **, and ***: differences significant at the 0.05, 0.01, and 0.001 levels, respectively. (<b>a</b>): Between-group significant difference test plot at the phylum level. (<b>b</b>): Between-group significant difference test plot at the genus level.</p>
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<p>Heat map of correlations between medicinal plants and bacterial communities, with positive correlations in red and negative correlations in blue. EZ: <span class="html-italic">C. zedoaria</span>/<span class="html-italic">C. oleifera</span>, JH: <span class="html-italic">C. longa</span>/<span class="html-italic">C. oleifera</span>, YDC: <span class="html-italic">C. nutans</span>/<span class="html-italic">C. oleifera</span>, HDK: <span class="html-italic">F. Galangae</span>/<span class="html-italic">C. oleifera</span>, WZMT: <span class="html-italic">F. simplicissima</span>/<span class="html-italic">C. oleifera</span>, CK: Monoculture of <span class="html-italic">C. oleifera</span>.</p>
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<p>Redundancy analyses (RDA) of bacterial communities and soil properties and enzyme activities in five medicinal plants—<span class="html-italic">C. oleifera</span> intercropping systems. OM—organic matter, TN—total nitrogen, TP—total phosphorus, TK—total potassium, AN—ammonium nitrogen, NN—nitrate nitrogen, AP—available phosphorus, AK—available potassium, S-UE—soil urease activity, S-CAT—soil catalase activity, S-SC—soil sucrase activity, S-ACP—soil acid phosphatase activity. EZ: <span class="html-italic">C. zedoaria</span>/<span class="html-italic">C. oleifera</span>, JH: <span class="html-italic">C. longa</span>/<span class="html-italic">C. oleifera</span>, YDC: <span class="html-italic">C. nutans</span>/<span class="html-italic">C. oleifera</span>, HDK: <span class="html-italic">F. Galangae</span>/<span class="html-italic">C. oleifera</span>, WZMT: <span class="html-italic">F. simplicissima</span>/<span class="html-italic">C. oleifera</span>, CK: Monoculture of <span class="html-italic">C. oleifera</span>.</p>
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15 pages, 2764 KiB  
Article
Study on the Diversity of Bacterial Communities in the Rhizosphere Soils of Different Wild Celery Species in Jilin Province
by Shanshan Chen, Yan Zou, Chunbo Zhao, Shuang Liu, Yue Yu, Junhai Jiang, Yue Zou and Jianlei Qiao
Agronomy 2024, 14(8), 1735; https://doi.org/10.3390/agronomy14081735 - 7 Aug 2024
Viewed by 250
Abstract
The bacterial communities in the rhizosphere soil of plants facilitate the cycling of nutrient elements in the rhizosphere and regulate soil fertility. By analyzing the microecological structure of rhizosphere soil surrounding wild celery, we can provide a basis for the bionic cultivation of [...] Read more.
The bacterial communities in the rhizosphere soil of plants facilitate the cycling of nutrient elements in the rhizosphere and regulate soil fertility. By analyzing the microecological structure of rhizosphere soil surrounding wild celery, we can provide a basis for the bionic cultivation of wild celery. In this experiment, rhizosphere soil samples from various wild celery varieties in Jilin Province were used as test materials, and high-throughput sequencing was employed to analyze and compare the rhizosphere bacterial community structures of these samples. After screening and removing chimeric sequences, a total of 1,020,108 high-quality sequences were obtained. Species classification results revealed that these bacteria encompassed 60 phyla, 183 classes, 431 orders, 702 families, and 1619 genera. There were certain differences in the composition and structure of bacterial communities among different rhizosphere soil samples. According to the richness indices, the performance order among samples was Tonghua water celery > Linjiang large-leaf celery > Linjiang old mountain celery > Tonghua large-leaf celery > Jiangyuan large-leaf celery > Tonghua old mountain celery > Linjiang water celery > artificially cultivated wild large-leaf celery > Huadian large-leaf celery > Huadian small-leaf celery > Dongfeng water celery > Jiangyuan old mountain celery. Among all bacterial communities, Pseudomonadota (37.79–22.48%) had the highest relative abundance across different regions, followed by Acidobacteriota (17.97–13.51%). RDA analysis indicated that soil pH, available phosphorus, available potassium, and alkali-hydrolyzable nitrogen in the celery rhizosphere were the primary factors influencing changes in bacterial communities. Based on the experimental analysis, it was demonstrated that there were differences in rhizosphere soil bacterial community diversity and composition among Tonghua large-leaf celery, Linjiang large-leaf celery, Jiangyuan large-leaf celery, Huadian large-leaf celery, Tonghua old mountain celery, Linjiang old mountain celery, Jiangyuan old mountain celery, Tonghua water celery, Linjiang water celery, Dongfeng water celery, Huadian small-leaf celery, and artificially cultivated wild large-leaf celery in Jilin Province. Full article
(This article belongs to the Section Soil and Plant Nutrition)
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<p>The layout of different sample plots demonstrating the geographical map of sampling sites.</p>
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<p>Sample dilution curve. Reads were clustered using Usearch software to obtain OTUs, and the dilution curve of each sample was drawn to reflect the rationality of sequencing data amount. When the curve tended to be flat, the amount of sequencing data was gradually reasonable, proving that the amount of sequencing data was saturated. The slope indicates the size of genetic richness.</p>
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<p>Alpha diversity of wild celery root soils in different regions. The Chao1 index represents the species richness; the Shannon index represents evenness; the Simpson index represents diversity.</p>
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<p>Shannon exponential curve. The bacterial Shannon index of soil samples tends to stabilize, indicating that the bacterial quantity in the samples is sufficiently large and approaching saturation.</p>
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<p>Shannon exponential curve. PCoA ranks the best eigenvalue based on the distance matrix. In the results, different colors represent different groups. The closer the sample distance is, the more similar the microbial composition structure between the samples, the less the difference.</p>
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<p>Composition of bacterial colonies in celery soil at gate level. The relative abundance of each sample/group is displayed in different forms. The bar chart is presented in the form of stacked bar graphs for a more intuitive comparison of sample abundance. In each level, we can intuitively see the expression of the dominant species and the changing trend in different treatments.</p>
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<p>Species abundance clustering at the soil genus level in celery roots. The middle horizontal axis is the sample name/group, and the vertical axis represents the relative abundance of a classification; different colors correspond to different species at the same level. The bar chart can show the composition of each sample/group with the high expression species and also observe the species composition, expression, and expression trends between groups.</p>
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<p>RDA analysis of soil bacterial community distribution and environmental factors in celery roots. Each point in the sample plot represents a sample, and the closer the distance between the two points is, the higher the community structure similarity between the two samples. Arrows represent different influencing factors, respectively. When the angle between influencing factors (between factors and samples) is acute, the two factors are positively correlated, and the blunt angle is negative. The longer the radiation, the greater the effect of this factor. The location of the sample projection point on the arrow approximately represents the numerical size of that factor in the corresponding sample.</p>
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23 pages, 4711 KiB  
Article
A Novel Plant-Derived Biopesticide Mitigates Fusarium Root Rot of Angelica sinensis by Modulating the Rhizosphere Microbiome and Root Metabolome
by Qi Liu, Waqar Ahmed, Guoli Li, Yilin He, Mohamed Mohany, Zhaoyu Li and Tong Shen
Plants 2024, 13(16), 2180; https://doi.org/10.3390/plants13162180 - 6 Aug 2024
Viewed by 355
Abstract
Fusarium root rot caused by the Fusarium species complex significantly affects the yield and quality of Angelica sinensis, a valuable medicinal herb. Traditional management primarily relies on chemical fungicides, which have led to pathogen resistance, environmental hazards, and concerns regarding public health [...] Read more.
Fusarium root rot caused by the Fusarium species complex significantly affects the yield and quality of Angelica sinensis, a valuable medicinal herb. Traditional management primarily relies on chemical fungicides, which have led to pathogen resistance, environmental hazards, and concerns regarding public health and the active components in A. sinensis. This study explores the efficacy of a novel plant-derived biopesticide Shi Chuang Zhi Feng Ning (T1; SCZFN), alongside Bacillus subtilis wettable powder (T2) and a chemical fungicide (T3), in controlling root rot and understanding their impacts on the rhizosphere microbial community and root metabolome. Results of the field experiment demonstrated that treatments T1 and T3 achieved control efficiencies of 73.17% and 75.45%, respectively, significantly outperforming T2 (39.99%) and the control. High-throughput sequencing revealed that all treatments altered the diversity and structure of microbial communities, with T1 and T2 reducing the abundance of taxa linked to root rot, such as Muribaculaceae spp., Humicola spp., Fusarium spp., and Mycochlamys spp. Treatment T1 notably enhanced beneficial bacterial taxa, including Acidobacteria spp., Nitrospira spp., and Pedosphaeraceae spp., involved in carbon cycling and plant growth promotion. Metabolomic analysis identified 39, 105, and 45 differentially expressed metabolites (DEMs) across the treatments, demonstrating T1’s potential to modulate the root metabolome effectively. Further, a correlation analysis demonstrated a stronger correlation between distinct microorganisms with significant influence and DEMs of T1 treatment compared to other treatments. These findings underscore biopesticide SCZFN’s role in enhancing plant health and disease suppression in A. sinensis, providing insights into its biocontrol mechanisms and supporting the development of sustainable disease management strategies in its cultivation. Full article
(This article belongs to the Special Issue Development of Biocontrol Products for Plant Diseases)
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<p><b>Venn diagram of the various treatments displays the distribution of common and shared amplicon sequence variants (ASVs).</b> The presence of intersecting numbers signifies shared ASVs, while the absence of intersections indicates unique ASVs. The ASVs distribution in bacteria (<b>A</b>), and the ASVs distribution in fungi (<b>B</b>). Application of biopesticide shi chuang zhi feng ning (SCZFN) (T1), application of <span class="html-italic">Bacillus subtilis</span> wettable powder (T2), application of fungicide Apron Advance (T3), application of water (CK).</p>
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<p><b>The dynamics of most dominant bacterial and fungal communities at the phyla level.</b> Relative abundance bar plots for the top 10 most abundant bacterial (<b>A</b>) and fungal (<b>B</b>) phyla. The box plot shows the significant difference and relative abundance of the differentially abundant bacterial (<b>C</b>) and fungal (<b>D</b>) phyla under different treatments. The lowercase letters on each box plot display significant differences among treatments (Wilcoxon test, <span class="html-italic">p</span> &lt; 0.05). Application of SCZFN (T1), application of <span class="html-italic">Bacillus subtilis</span> wettable powder (T2), application of fungicide Apron Advance (T3), and application of water (CK).</p>
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<p><b>The analysis of the patterns of taxonomic distribution and soil microbial composition at the genus level.</b> The relative abundance heat maps and phylum-level cluster maps of the top 35 bacteria (<b>A</b>) and fungi (<b>B</b>) under different treatments. Application of SCZFN (T1), application of <span class="html-italic">Bacillus subtilis</span> wettable powder (T2), application of fungicide Apron Advance (T3), and application of water (CK).</p>
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<p><b>Assessment of microbial community diversity and structure difference under different treatments.</b> Box plot showing the alpha diversity indices of bacterial (<b>A</b>) and fungal (<b>B</b>) communities under different treatments. Alpha diversity indices include Chao 1, Shannon, Simpson, and Pielou evenness. Different lowercase letters on each box plot represent the significant differences among treatments according to the Wilcoxon test at <span class="html-italic">p</span> &lt; 0.05. Principal coordinate analysis (PCoA) based on the Bray–Curtis distance matrix demonstrates the separation between soil bacterial and fungal communities under different treatments. PCoA for bacterial (<b>C</b>) and fungal (<b>D</b>) communities. Application of SCZFN (T1), application of <span class="html-italic">Bacillus subtilis</span> wettable powder (T2), application of fungicide Apron Advance (T3), and application of water (CK).</p>
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<p><b>Co-occurrence networks analysis of bacterial and fungal communities at genus level under different treatments.</b> Nodes represent microbial genera, and edges represent the interaction between microbes within a specific treatment, including the number of positive and negative edges. Application of SCZFN (T1), application of <span class="html-italic">Bacillus subtilis</span> wettable powder (T2), application of fungicide Apron Advance (T3), and application of water (CK).</p>
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<p><b>Correlation analysis between Top 10 bacterial-fungal genera and disease incidence according to Pearson correlation coefficient (PCC, <span class="html-italic">p</span> &lt; 0.05).</b> PCC between bacterial genera and disease incidence (<b>A</b>), and PCC between fungal genera and disease incidence (<b>B</b>). Asterisks indicates significant differences (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001).</p>
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<p><b>The analysis of d differentially expressed metabolites after comparison of different treatments and controls.</b> The presence of intersecting numbers signifies shared DEMs, while the absence of intersections indicates unique DEMs. Volcano map of the overall distribution of DEMs. (<b>A</b>–<b>C</b>) DEMs of different treatments compared to CK in positive mode, (<b>D</b>–<b>F</b>) DEMs of different treatments compared to CK in negative mode. The horizontal coordinate represents the difference in multiple changes of metabolites in different groups (log2(fold change)), and the vertical coordinate represents the difference in significance level (−log10(<span class="html-italic">p</span>-value)). Each point represents a metabolite. Significantly up-regulated metabolites are represented by red dots, and significantly down-regulated metabolites are represented by green dots. The size of the dot represents the VIP value. Application of SCZFN (T1), application of <span class="html-italic">Bacillus subtilis</span> wettable powder (T2), application of fungicide Apron Advance (T3), and application of water (CK).</p>
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<p><b>Analysis of KEGG enrichment pathway between treatments compared to CK in two ion modes (A–C).</b> The horizontal coordinate is the ratio of the number of differentiated metabolites in the corresponding metabolic pathway to the total number of identified metabolites in the pathway, and the vertical coordinate represents the difference in significance level (−log10(<span class="html-italic">p</span>-value)). The size of the dots represents the number of differentiated metabolites in the corresponding pathway. Application of SCZFN (T1), application of <span class="html-italic">Bacillus subtilis</span> wettable powder (T2), application of fungicide Apron Advance (T3), and application of water (CK).</p>
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<p><b>Interactions between different expressed metabolites and differential microbial communities with different treatments compared to CK.</b> The interactions among the top 30 distinct metabolites (The top 15 of each of the two ion modes) and diverse microbial communities were compared to CK under various treatments. (<b>A</b>–<b>C</b>) Correlations of the microbial community and top 30 DEMs were determined using the Mantel test. (<b>D</b>–<b>F</b>) Correlations analysis of the Top 10 bacterial-fungal genera and Top 30 DEMs according to Pearson correlation coefficient. Asterisks indicates significant differences (* <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). Application of SCZFN (T1), application of <span class="html-italic">Bacillus subtilis</span> wettable powder (T2), application of fungicide Apron Advance (T3), and application of water (CK).</p>
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21 pages, 9728 KiB  
Article
Maize, Peanut, and Millet Rotations Improve Crop Yields by Altering the Microbial Community and Chemistry of Sandy Saline–Alkaline Soils
by Liqiang Zhang, Jianguo Zhu, Yueming Zhang, Kexin Xia, Yuhan Yang, Hongyu Wang, Qiuzhu Li and Jinhu Cui
Plants 2024, 13(15), 2170; https://doi.org/10.3390/plants13152170 - 5 Aug 2024
Viewed by 488
Abstract
Crop rotation increases crop yield, improves soil health, and reduces plant disease. However, few studies were conducted on the use of intensive cropping patterns to improve the microenvironment of saline soils. The present study thoroughly evaluated the impact of a three-year maize–peanut–millet crop [...] Read more.
Crop rotation increases crop yield, improves soil health, and reduces plant disease. However, few studies were conducted on the use of intensive cropping patterns to improve the microenvironment of saline soils. The present study thoroughly evaluated the impact of a three-year maize–peanut–millet crop rotation pattern on the crop yield. The rhizosphere soil of the crop was collected at maturity to assess the effects of crop rotation on the composition and function of microbial communities in different tillage layers (0–20 cm and 20–40 cm) of sandy saline–alkaline soils. After three years of crop rotation, the maize yield and economic benefits rose by an average of 32.07% and 22.25%, respectively, while output/input grew by 10.26%. The pH of the 0–40 cm tillage layer of saline–alkaline soils decreased by 2.36%, organic matter rose by 13.44%–15.84%, and soil-available nutrients of the 0–20 cm tillage layer increased by 11.94%–69.14%. As compared to continuous cropping, crop rotation boosted soil nitrogen and phosphorus metabolism capacity by 8.61%–88.65%. Enrichment of Actinobacteria and Basidiomycota increased crop yield. Crop rotation increases microbial community richness while decreasing diversity. The increase in abundance can diminish competitive relationships between species, boost synergistic capabilities, alter bacterial and fungal community structure, and enhance microbial community function, all of which elevate crop yields. The obtained insights can contribute to achieving optimal management of intensive cultivation patterns and green sustainable development. Full article
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<p>Layout of the experiment’s cropping modes and arrangement of plants. Maize–peanut–millet rotations (CPM), peanut–millet–maize rotations (PMC), millet–maize–peanut rotations (MCP), and maize continuous cropping (CCC). (<b>a</b>) is an aerial view and two photographs of the experimental site, (<b>b</b>) enlists the crops planted in each treatment for both rotation and continuous cropping in 2020–2022, and the numbers between the two columns are the spacing of the rows (R), and the numbers between the two rows are the spacing of the plants (S).</p>
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<p>Effects of different cropping systems on soil pH (<b>a</b>), EC (<b>b</b>), and organic matter (SOM) (<b>c</b>). CK is for the 2019 continuous cropping treatment. CCC is for 2020–2022 continuous cropping treatment, CPM, PMC, and MCP are for 2020–2022 rotation treatment, where C is for maize, P is peanut, and M is for millet. The bars represent the mean ± SE, n = 45 (CK, n = 15) replicates. Capital letters (A, B) on the error line denote differences between soil depth (0–20 cm and 20–40 cm) under the same treatment (<span class="html-italic">p</span> &lt; 0.05). Small letters (a–c) on the error line indicate differences between treatments under the same soil depth (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Effects of different cropping systems on soil total nitrogen (TN) (<b>a</b>), total phosphorus (TP) (<b>b</b>), total potassium (TK) (<b>c</b>), available nitrogen (AN) (<b>d</b>), rapidly available phosphorus (AP) (<b>e</b>), and available potassium (AK) (<b>f</b>). CK is for the 2019 continuous cropping treatment. CCC is for 2020–2022 continuous cropping treatment, CPM, PMC, and MCP are for 2020–2022 rotation treatment, where C is for maize, P is peanut and M is for millet. The bars represent the mean ± SE, n = 45 (CK, n = 15) replicates. Capital letters (A, B) on the error line indicate differences between soil depth (0–20 cm and 20–40 cm) under the same treatment (<span class="html-italic">p</span> &lt; 0.05). Small letters (a–c) on the error line indicate differences between treatments under the same soil depth (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Effects of different cropping systems on alpha diversity of bacterial (<b>a</b>,<b>c</b>) and fungal (<b>b</b>,<b>d</b>) communities. Chao1 index (<b>a</b>,<b>b</b>) and Shannon index (<b>c</b>,<b>d</b>). CCC is for 2020–2022 continuous cropping treatment, CPM, PMC, and MCP are for 2020–2022 rotation treatment, where C is for maize, P is peanut, and M is for millet. The box plot has the upper and lower quartiles at each end, the horizontal line in the middle indicates the median, and the lines connecting the two ends are the minimum and maximum values (n = 15). Capital letters (A, B) on the boxes’ maximum values line indicate differences between soil depth (0–20 cm and 20–40 cm) under the same treatment (<span class="html-italic">p</span> &lt; 0.05). Small letters (a–c) on the error line indicate differences between treatments under the same soil depth (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Effects of different cropping systems on the composition of the bacterial (<b>a</b>,<b>b</b>) and fungal (<b>c</b>,<b>d</b>) communities. A total of 0–20 cm (<b>a</b>,<b>c</b>) and 20–40 cm (<b>b</b>,<b>d</b>) soil depth. CCC is for 2020–2022 continuous cropping treatment, CPM, PMC, and MCP are for 2020–2022 rotation treatment, where C is for maize, P is peanut, and M is for millet. The bars represent the mean ± SE, n = 15 replicates. Lowercase letters to the left of the error line (a–c) indicate differences between treatments for the same colony at the same soil depth (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Principal component analysis of the soil microbial communities. Bacteria, 0–20 cm (<b>a</b>) and 20–40 cm (<b>b</b>) soil depth, fungal, 0–20 cm (<b>c</b>), and 20–40 cm (<b>d</b>) soil depth. CCC is for 2020–2022 continuous cropping treatment, CPM, PMC, and MCP are for 2020–2022 rotation treatment, where C is for maize, P is peanut, and M is for millet. n = 15 replicates. The dashed ellipse represents the 95% confidence interval.</p>
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<p>Co-occurrence network model of bacterial (<b>a</b>,<b>b</b>) and fungal (<b>c</b>,<b>d</b>) communities under varied cropping patterns. Top layer 0–20 cm (<b>a</b>,<b>c</b>) (matching CCC, CPM, PMC, and MCP by 97% similarity) and deeper 20–40 cm (<b>b</b>,<b>d</b>) (matching CCC, CPM, PMC, and MCP by 97% similarity) soil depth. The circles indicate different species (genus level), with size denoting the average abundance. The blue and red lines denote the positive and negative correlation, respectively, with thickness associated with the degree of correlation.</p>
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<p>Correlation analysis between soil dominant bacteria and environmental factors. Bacteria (<b>a</b>,<b>b</b>) and fungal (<b>c</b>,<b>d</b>), 0–20 cm (<b>a</b>,<b>c</b>), and 20–40 cm (<b>b</b>,<b>d</b>) soil depth, with red segments being positively correlated and dark grey segments being negatively correlated, and asterisks indicating a significant correlation (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01). Soil organic matter (SOM), total nitrogen (TN), total phosphorus (TP), total potassium (TK), available nitrogen (AN), rapidly available phosphorus (AP), and available potassium (AK).</p>
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<p>The effect of dominant microorganisms (<b>a</b>,<b>b</b>) and soil chemistry properties (<b>c</b>,<b>d</b>) on yield. Soil organic matter (SOM), total nitrogen (TN), total phosphorus (TP), total potassium (TK), available nitrogen (AN), rapidly available phosphorus (AP), and available potassium (AK). Structural equation model (<b>e</b>), positive associations are indicated by blue arrows, while negative relationships are depicted by red arrows, and black dashed arrows indicate no significant correlation between the two. Statistical significance is denoted by asterisks (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01). CFI denotes the comparative fit index and RMSEA denotes the root mean square error of approximation.</p>
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23 pages, 4610 KiB  
Article
Phenanthrene-Degrading and Nickel-Resistant Neorhizobium Strain Isolated from Hydrocarbon-Contaminated Rhizosphere of Medicago sativa L.
by Sergey Golubev, Margarita Rasterkovskaya, Irina Sungurtseva, Andrey Burov and Anna Muratova
Microorganisms 2024, 12(8), 1586; https://doi.org/10.3390/microorganisms12081586 - 4 Aug 2024
Viewed by 350
Abstract
Pollutant degradation and heavy-metal resistance may be important features of the rhizobia, making them promising agents for environment cleanup biotechnology. The degradation of phenanthrene, a three-ring polycyclic aromatic hydrocarbon (PAH), by the rhizobial strain Rsf11 isolated from the oil-polluted rhizosphere of alfalfa and [...] Read more.
Pollutant degradation and heavy-metal resistance may be important features of the rhizobia, making them promising agents for environment cleanup biotechnology. The degradation of phenanthrene, a three-ring polycyclic aromatic hydrocarbon (PAH), by the rhizobial strain Rsf11 isolated from the oil-polluted rhizosphere of alfalfa and the influence of nickel ions on this process were studied. On the basis of whole-genome and polyphasic taxonomy, the bacterium Rsf11 represent a novel species of the genus Neorhizobium, so the name Neorhizobium phenanthreniclasticum sp. nov. was proposed. Analysis of phenanthrene degradation by the Rsf1 strain revealed 1-hydroxy-2-naphthoic acid as the key intermediate and the activity of two enzymes apparently involved in PAH degradation. It was also shown that the nickel resistance of Rsf11 was connected with the extracellular adsorption of metal by EPS. The joint presence of phenanthrene and nickel in the medium reduced the degradation of PAH by the microorganism, apparently due to the inhibition of microbial growth but not due to the inhibition of the activity of the PAH degradation enzymes. Genes potentially involved in PAH catabolism and nickel resistance were discovered in the microorganism studied. N. phenanthreniclasticum strain Rsf11 can be considered as a promising candidate for use in the bioremediation of mixed PAH–heavy-metal contamination. Full article
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<p>ML phylogenetic tree of strain Rsf11 and closely related type strains inferred from 16S rRNA gene sequences under the GTR+GAMMA model. The tree was rooted at the midpoint. The branches are scaled in terms of the expected number of substitutions per site. The numbers above the branches are support values when larger than 60% from ML (left) and MP (right) bootstrapping. GenBank accession numbers are shown in parentheses. The <span class="html-italic">Rhizobium terrae</span> and <span class="html-italic">Rhizobium populisoli</span> type strains were assigned to <span class="html-italic">Neorhizobium</span> [<a href="#B64-microorganisms-12-01586" class="html-bibr">64</a>] and <span class="html-italic">Rhizobium phenanthrenilyticum</span> type strain to <span class="html-italic">Neorhizobium petrolearium</span> [<a href="#B23-microorganisms-12-01586" class="html-bibr">23</a>,<a href="#B61-microorganisms-12-01586" class="html-bibr">61</a>].</p>
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<p>Phylogenetic tree of strain Rsf11 and closely related type strains inferred from GBDP distances calculated from genome sequences. The branch lengths are scaled in terms of GBDP distance formula <span class="html-italic">d</span><sub>5</sub>. The numbers near branches are GBDP pseudo-bootstrap support values &gt; 60% from 100 replications, with an average branch support of 74.4%. The tree was rooted at the midpoint. The <span class="html-italic">Rhizobium terrae</span> and <span class="html-italic">Rhizobium populisoli</span> type strains were assigned to <span class="html-italic">Neorhizobium</span> [<a href="#B64-microorganisms-12-01586" class="html-bibr">64</a>].</p>
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<p>Cultural-morphological characteristics of strain Rsf11: (<b>a</b>) 48 h colonies on YMA medium; (<b>b</b>) Gram-negative staining of microbial cells; (<b>c</b>) transmission electron microscopy of a single cell grown on YMA for 48 h; (<b>d</b>) transmission electron microscopy of Rsf11 cell division.</p>
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<p>Cultural-morphological characteristics of strain Rsf11: (<b>a</b>) 48 h colonies on YMA medium; (<b>b</b>) Gram-negative staining of microbial cells; (<b>c</b>) transmission electron microscopy of a single cell grown on YMA for 48 h; (<b>d</b>) transmission electron microscopy of Rsf11 cell division.</p>
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<p>Degradation of phenanthrene as a sole carbon and energy source and HNA formation during the growth of the Rsf11 strain in mineral medium.</p>
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<p>HPLC chromatogram of the ethyl acetate extract of the medium after 7 days’ cultivation of bacterial strain Rsf11 with phenanthrene (0.2 g L<sup>−1</sup>).</p>
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<p>Effect of nickel on growth of the Rsf11 strain in LB medium.</p>
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<p>Intracellular accumulation and extracellular adsorption of nickel by Rsf11 cells.</p>
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<p>Effect of nickel on growth and phenanthrene degradation by the Rsf11 strain.</p>
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<p>The effect of nickel on the activity of PQR and 3,4-PCD involved in the degradation of phenanthrene by the Rsf11 strain.</p>
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17 pages, 3247 KiB  
Article
Screening of As-Resistant Bacterial Strains from the Bulk Soil and the Rhizosphere of Mycorrhizal Pteris vittata Cultivated in an Industrial Multi-Polluted Site
by Giorgia Novello, Elisa Gamalero, Patrizia Cesaro, Daniela Campana, Simone Cantamessa, Nadia Massa, Graziella Berta, Guido Lingua and Elisa Bona
Soil Syst. 2024, 8(3), 87; https://doi.org/10.3390/soilsystems8030087 - 3 Aug 2024
Viewed by 254
Abstract
Arsenic (As) contamination poses significant environmental and health concerns globally, particularly in regions with high exposure levels due to anthropogenic activities. As phytoremediation, particularly through the hyperaccumulator fern Pteris vittata, offers a promising approach to mitigate arsenic pollution. Bacteria and mycorrhizal fungi [...] Read more.
Arsenic (As) contamination poses significant environmental and health concerns globally, particularly in regions with high exposure levels due to anthropogenic activities. As phytoremediation, particularly through the hyperaccumulator fern Pteris vittata, offers a promising approach to mitigate arsenic pollution. Bacteria and mycorrhizal fungi colonizing P. vittata roots are involved in As metabolism and resistance and plant growth promotion under stressful conditions. A total of 45 bacterial strains were isolated from bulk soil and the rhizosphere of mycorrhizal P. vittata growing in an industrial As-polluted site. Bacteria were characterized by their plant-beneficial traits, tolerance to sodium arsenate and arsenite, and the occurrence of As-resistant genes. This study highlights differences between the culturable fraction of the microbiota associated with the rhizosphere of mycorrhizal P. vittata plants and the bulk soil. Moreover, several strains showing arsenate tolerance up to 600 mM were isolated. All the bacterial strains possessed arsC genes, and about 70% of them showed arrA genes involved in the anaerobic arsenate respiration pathway. The possible exploitation of such bacterial strains in strategies devoted to the assisted phytoremediation of arsenic highlights the importance of such a study in order to develop effective in situ phytoremediation strategies. Full article
(This article belongs to the Special Issue Soil Bioremediation)
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<p>GPS image of the industrial site: the blue label indicates the experimental field where mycorrhizal <span class="html-italic">P. vittata</span> was cultivated (44°53′15″ N 8°40′07″ E). The site is located in northwestern Italy and is polluted by heavy metals due to the metallurgic planting facility’s activities.</p>
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<p>Image describing the different stages of the experimental procedure used: (<b>i</b>) soil sampling from the roots of <span class="html-italic">P. vittata</span> cultivated in the industrial site; (<b>ii</b>) bacterial isolation on agar plates; (<b>iii</b>) Gram staining and identification of the selected strains via MALDI (Matrix-152 Assisted Laser Desorption/Ionization) and TOF/TOF (UltrafleXtreme, Bruker) system; (<b>iv</b>) characterization of plant beneficial physiological traits and determination of the arsenite and arsenate tolerance of the bacterial strains; (<b>v</b>) occurrence of arsenate reductase and arsenate respiratory reductase genes assessed by PCR amplification. This image was created with BioRender (<a href="https://www.biorender.com" target="_blank">https://www.biorender.com</a>, accessed on 30 July 2024), Toronto, Canada.</p>
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<p>Distribution (frequency) of the bacterial genera found in bulk soil (<b>A</b>) and rhizosphere of mycorrhizal <span class="html-italic">P. vittata</span> (<b>B</b>) in the industrial site considered.</p>
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16 pages, 680 KiB  
Article
Isolation and Characterization of Bacillus Subtilis BSP1 from Soil: Antimicrobial Activity and Optimization of Fermentation Conditions
by Heba Hellany, Jean Claude Assaf, Sara Barada, Dalia el-Badan, Rana El Hajj, Sonia Abou Najem, Antoine G. Abou Fayad and Mahmoud I. Khalil
Processes 2024, 12(8), 1621; https://doi.org/10.3390/pr12081621 - 2 Aug 2024
Viewed by 434
Abstract
This study focused on the isolation, characterization, and evaluation of the antimicrobial and antioxidant activities of a crude extract from Bacillus subtilis isolated from rhizosphere soil. Through biochemical and physiological assessments, followed by whole genome sequencing, the isolate was confirmed as Bacillus subtilis [...] Read more.
This study focused on the isolation, characterization, and evaluation of the antimicrobial and antioxidant activities of a crude extract from Bacillus subtilis isolated from rhizosphere soil. Through biochemical and physiological assessments, followed by whole genome sequencing, the isolate was confirmed as Bacillus subtilis BSP1. We examined the antimicrobial activity of B. subtilis BSP1 metabolites against various pathogenic bacteria and fungi. To enhance its antibacterial efficacy, we optimized the fermentation medium to maximize the secretion of antibacterial agents. Our findings demonstrated that the crude extract exhibited notable antimicrobial properties against various pathogenic bacterial and fungal isolates. The antioxidant test revealed a dose-dependent increase in the extract’s DPPH scavenging activity and reducing power, with an impressive 98.9% DPPH scavenging activity at 30 mg/mL. Importantly, safety assessments indicated a lack of hemolytic activity on human red blood cells, with only 1.3% hemolysis at 100 mg/mL, suggesting its potential suitability for practical applications. In summary, Bacillus subtilis BSP1, isolated from soil, appears to be a promising candidate for antibiotic production. Its significant antimicrobial and antioxidant properties, combined with its safety profile, highlight its potential applications in medicine, agriculture, and biotechnology. Full article
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<p>Effect of different carbon sources, nitrogen sources, incubation temperature, and incubation hours on the antibacterial activity of <span class="html-italic">B. subtilis</span> BSP1. The <span class="html-italic">p</span>-values were calculated: * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Antioxidant scavenging potential of the bacterial extract of <span class="html-italic">B. subtilis</span> BSP1 and ascorbic acid using the DPPH assay.</p>
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14 pages, 2641 KiB  
Article
Addition of Fresh Herbs to Fresh-Cut Iceberg Lettuce: Impact on Quality and Storability
by Maria Grzegorzewska, Magdalena Szczech, Beata Kowalska, Anna Wrzodak, Monika Mieszczakowska-Frąc and Teresa Sabat
Agriculture 2024, 14(8), 1266; https://doi.org/10.3390/agriculture14081266 - 1 Aug 2024
Viewed by 349
Abstract
The aim of this study was to develop ready-to-eat vegetable–herb mixes with high nutritional and sensory values as well as good storability. In this regard, the suitability of fresh herbs (peppermint, oregano, green basil, red basil, and parsley) was tested for their use [...] Read more.
The aim of this study was to develop ready-to-eat vegetable–herb mixes with high nutritional and sensory values as well as good storability. In this regard, the suitability of fresh herbs (peppermint, oregano, green basil, red basil, and parsley) was tested for their use in mixes with fresh-cut iceberg lettuce. Lettuce–herb mixtures were stored for 6 days at 5 °C. The reason for the decrease in the appearance of the salads was the browning of the cut surface of the lettuce, as well as discoloration on the cut herbs. Comparing the storage abilities of the cut herbs, red basil and parsley retained the best appearance for 6 d at 5 °C. A small addition of herbs to fresh-cut iceberg lettuce caused a significant increase (p < 0.05) in the contents of pro-health ingredients such as chlorophyll, carotenoids, L-ascorbic acid, and polyphenols in the mixes. There were large discrepancies in the sensory quality of the mixes, but the highest quality and consumer acceptance were found for salads with parsley (5% and 10%) and red basil (5%). After harvest, the fresh herbs were more contaminated by molds than the iceberg lettuce. Bacterial, yeast, and mold contamination increased during storage, but the rate of mold growth was much lower in the mixes with parsley compared to lettuce alone. In conclusion, the addition of parsley and mint contributed the most to the health-promoting and microbiological properties of iceberg lettuce salads. However, according to sensory evaluation, parsley and red basil contributed the most to improving the acceptability of the product in terms of best taste and shelf life. Full article
(This article belongs to the Section Agricultural Product Quality and Safety)
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<p>Mix of fresh-cut iceberg lettuce with parsley packed in GUILLIN W1/059C box and covered with GUILLIN W2/001 lid.</p>
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<p>The content of chemical compounds in mixes of iceberg lettuce with fresh herbs: (<b>A</b>)—chlorophyll, (<b>B</b>)—carotenoids, (<b>C</b>)—L-ascorbic acid, (<b>D</b>)—polyphenols. The analyses were carried out after 3 d of storage at 5 °C. The vertical bars represent means of 4 samples. Vertical lines represent standard deviation (SD). Each sample has a total weight of 100 g. The results are expressed in mg kg<sup>−1</sup> fresh weight. Means followed by the different letter are significantly different (<span class="html-italic">p</span> &lt; 0.05, Tukey test). The letter “a” means the lowest content and the further letters of the alphabet indicate a higher content.</p>
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<p>Principal component analysis of sensory profiling results of fresh-cut iceberg lettuce mixed with fresh herbs: control, red basil (5%), green basil (3%), peppermint (5%), parsley (5%), and parsley (10%).</p>
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<p>The microbiological contamination of (<b>A</b>) bacteria, (<b>B</b>) yeasts, and (<b>C</b>) molds on iceberg lettuce with fresh herbs. The vertical bars represent means of 5 samples (white bars—after 0 days of storage, gray bars—after 3 d of storage at 5 °C). The vertical lines represent standard deviation (SD) (n = 5). From each 100 g sample, 25 g of material was taken for microbiological analysis. Means followed by the different small letter are significantly different for the value on day 0. Means followed by the different capital letter are significantly different for the value on day 3 (<span class="html-italic">p</span> ˂ 0.05 Tukey test). The letter “a” or “A” means the lowest contamination and further letters of the alphabet indicate higher contamination.</p>
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21 pages, 4067 KiB  
Article
Variability in Maize Seed Bacterization and Survival Correlating with Root Colonization by Pseudomonas Isolates with Plant-Probiotic Traits
by Melani G. Lorch, Claudio Valverde and Betina C. Agaras
Plants 2024, 13(15), 2130; https://doi.org/10.3390/plants13152130 - 1 Aug 2024
Viewed by 306
Abstract
Seed treatment with plant growth-promoting bacteria represents the primary strategy to incorporate them into agricultural ecosystems, particularly for crops under extensive management, such as maize. In this study, we evaluated the seed bacterization levels, root colonization patterns, and root competitiveness of a collection [...] Read more.
Seed treatment with plant growth-promoting bacteria represents the primary strategy to incorporate them into agricultural ecosystems, particularly for crops under extensive management, such as maize. In this study, we evaluated the seed bacterization levels, root colonization patterns, and root competitiveness of a collection of autochthonous Pseudomonas isolates that have demonstrated several plant-probiotic abilities in vitro. Our findings indicate that the seed bacterization level, both with and without the addition of various protectants, is specific to each Pseudomonas strain, including their response to seed pre-hydration. Bacterization kinetics revealed that while certain isolates persisted on seed surfaces for up to 4 days post-inoculation (dpi), others experienced a rapid decline in viability after 1 or 2 dpi. The observed differences in seed bacterization levels were consistent with the root colonization densities observed through confocal microscopy analysis, and with root competitiveness quantified via selective plate counts. Notably, isolates P. protegens RBAN4 and P. chlororaphis subsp. aurantiaca SMMP3 demonstrated effective competition with the natural microflora for colonizing the maize rhizosphere and both promoted shoot and root biomass production in maize assessed at the V3 grown stage. Conversely, P. donghuensis SVBP6 was detected at very low levels in the maize rhizosphere, but still exhibited a positive effect on plant parameters, suggesting a growth-stimulatory effect during the early stages of plant development. In conclusion, there is a considerable strain-specific variability in the maize seed bacterization and survival capacities of Pseudomonas isolates with plant-probiotic traits, with a correlation in their root competitiveness under natural conditions. This variability must be understood to optimize their adoption as inputs for the agricultural system. Our experimental approach emphasizes the critical importance of tailoring seed bacterization treatments for each inoculant candidate, including the selection and incorporation of protective substances. It should not be assumed that all bacterial cells exhibit a similar performance. Full article
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<p>Variability in maize seed bacterization levels and protective effects within our <span class="html-italic">Pseudomonas</span> collection at the time of inoculation (0 dpi). The data represent transformed CFU values using the formula log<sub>10</sub>(x + 50), where x is the corresponding CFU value. This transformation is pertinent for statistical comparisons of data sets including CFU null data [<a href="#B57-plants-13-02130" class="html-bibr">57</a>]. Dashed lines indicate the log<sub>10</sub> value corresponding to a transformed null CFU count. (<b>a</b>) Recovery of culturable <span class="html-italic">Pseudomonas</span> from maize seeds inoculated in the presence (black) or absence (grey) of the commercial bacterial protectant Premax<sup>®</sup> (Rizobacter Argentina S.A., Pergamino, Argentina). Asterisks indicate statistically significant differences between treatments with or without Premax<sup>®</sup> (ANOVA with LSD-Fisher multiple comparison test; * <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001). Numbers above treatment bars indicate fold increase in bacterial recovery for treatments with Premax<sup>®</sup> compared to the corresponding control. (<b>b</b>) Effect of different additives on recovery of SVMP4-<span class="html-italic">yfp</span> from maize bacterized seeds. Different letters indicate statistically significant differences (ANOVA with LSD Fisher’s multiple comparison test, <span class="html-italic">p</span> &lt; 0.05). Numbers above bars indicate the fold increase in bacterial recovery for treatments with additives compared to the corresponding control.</p>
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<p>Pre-hydration of maize seeds before bacterization increases the recovery of culturable <span class="html-italic">Pseudomonas</span> cells at the time of inoculation (0 dpi). Treatments compared include those with (black) and without (grey) the addition of the commercial protectant Premax<sup>®</sup>, and with (dashed bars) or without (full bars) pre-hydration treatment (see main text for details). See <a href="#plants-13-02130-f001" class="html-fig">Figure 1</a> legend for the reference about data transformation. Dashed line indicates the log<sub>10</sub> value corresponding to a transformed null CFU count. Different letters indicate statistically significant differences between treatments for each isolate (ANOVA with LSD Fisher’s multiple comparison test, <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Time course recovery of <span class="html-italic">Pseudomonas</span> isolates from bacterized maize seeds is strain-dependent, showing a decline in the additive effect over time for some isolates. Bacterization levels (CFU) were determined in seeds sampled daily for up to 4 days after inoculation with (black) or without (grey) additives. Premax<sup>®</sup> treatment is denoted with black circles; trehalose–PVP mixture treatment is shown with triangles; and glycerol with squares. See <a href="#plants-13-02130-f001" class="html-fig">Figure 1</a> legend for the reference about data transformation. Asterisks indicate statistically significant differences between additive and control treatments on the same day (Two-way ANOVA with LSD Fisher’s multiple comparison test; *** <span class="html-italic">p</span> &lt; 0.001; ** <span class="html-italic">p</span> &lt; 0.01; * <span class="html-italic">p</span> &lt; 0.05). Black asterisks denote statistically significant differences for Premax<sup>®</sup> treatment; dark grey asterisks denote differences for trehalose–PVP treatment; light grey asterisks denote differences for glycerol treatment.</p>
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<p><span class="html-italic">Pseudomonas</span> isolates can achieve different colonization patterns on the root surface of maize seedlings after seed bacterization. Early root colonization (9 days after inoculation) was analyzed by confocal fluorescence microscopy of <span class="html-italic">Pseudomonas</span> isolate derivatives expressing different fluorescent proteins: eYFP (<b>a</b>–<b>d</b>), eCFP (<b>e</b>,<b>f</b>) and mCherry (<b>g</b>). All images were captured at the elongation zone of the maize roots (1 cm above the root tip) using a 20× objective and 4× digital zoom, except for (<b>g</b>) (<b>1</b>,<b>2</b>) (2× zoom) and (<b>f</b>) (<b>3</b>,<b>4</b>) (40× objective and 4× zoom). In all cases, pictures from right panels (<b>2</b>,<b>4</b>) correspond to transmitted light microscopy and those from left panels (<b>1</b>,<b>3</b>) to confocal fluorescence microscopy. Images are representative of colonization patterns on all the observed plant roots (2–4 replicates per treatment). Control treatments were also observed, obtaining in all cases images without any fluorescent bacterial cells. Scales are indicated with a white bar on each image, corresponding to 20 µm, except for (<b>f</b>) (<b>3</b>,<b>4</b>) (10 µm) and (<b>g</b>) (<b>1</b>,<b>2</b>) (50 µm).</p>
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<p><span class="html-italic">Pseudomonas</span> isolates showed different root competitiveness during the early colonization of maize roots in the presence of natural soil microflora. Root colonization was quantified by selective plate counts on S1 media supplemented with the corresponding antibiotic for every <span class="html-italic">Pseudomonas</span> derivative. See <a href="#plants-13-02130-f001" class="html-fig">Figure 1</a> legend for reference about data transformation. Dashed line indicates the log<sub>10</sub> value corresponding to a transformed null CFU count. Data was corrected to express the values by the dry root weight. Different letters indicate statistically significant differences between treatments (Kruskal–Wallis non-parametric test, with the uncorrected Dunn’s multiple comparison test).</p>
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<p>Bacterial inoculation improved several plant parameters of maize up to the V3 growth stage. After one month of incubation, we measured the shoot biomass, fresh (<b>a</b>) and dry (<b>b</b>); the shoot height (<b>c</b>); the fresh root biomass (<b>d</b>); the root length (<b>e</b>); and the shoot/root biomass ratio (SB/RB, <b>f</b>). For all the measured data, we included a non-inoculated control (white) and the reference treatment 1008 (dark grey). Different letters indicate statistically significant differences between treatments (ANOVA with LSD Fisher’s multiple comparison test, <span class="html-italic">p</span> &lt; 0.05).</p>
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