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Search Results (320)

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15 pages, 2199 KiB  
Article
Effects of High-Grain Diet on Performance, Ruminal Fermentation, and Rumen Microbial Flora of Lactating Holstein Dairy Cows
by Kexin Wang, Damin Song, Xuelei Zhang, Osmond Datsomor, Maocheng Jiang and Guoqi Zhao
Animals 2024, 14(17), 2522; https://doi.org/10.3390/ani14172522 - 30 Aug 2024
Viewed by 534
Abstract
The objectives of the current study were to evaluate the fluctuations in production performance, rumen fermentation, and microbial community in lactating dairy cows fed a high-grain diet (HG). In this study, 16 healthy Holstein lactating dairy cattle with similar milk yields of 16.80 [...] Read more.
The objectives of the current study were to evaluate the fluctuations in production performance, rumen fermentation, and microbial community in lactating dairy cows fed a high-grain diet (HG). In this study, 16 healthy Holstein lactating dairy cattle with similar milk yields of 16.80 ± 4.30 kg/d, days in milk 171.44 ± 23.25 days, and parity 2.2 ± 1.5 times were selected and randomly allocated into two groups. One group was fed a low-grain diet (LG; 40% concentrate, DM basis; n = 8), and the other group was fed a high-grain diet (HG; 60% concentrate, DM basis; n = 8). The experiment lasted 6 weeks, including 1 week for adaptation. The experimental results showed that the milk fat content in the milk of lactating cows in the HG group was significantly reduced (p < 0.05), and the milk urea nitrogen (MUN) content showed an increasing trend (0.05 < p < 0.10) compared with the LG group. Compared with the LG group, rumen fluid pH was significantly decreased after feeding a high-grain diet, and contents of total volatile fatty acids (TVFA), acetate, propionate, and butyrate were significantly increased (p < 0.05). The acetate/propionate significantly decreased (p < 0.05). HG group significantly increased the abundance of Prevotella and Bacteroides in rumen fluid while significantly reducing the abundance of Methanobrevibacter and Lachnospiraceae ND3007_group (p < 0.05). Microorganisms with LDA scores > 2 were defined as unique, with the bacterial genus Anaerorhabdus_furcosa_group identified as a biomarker for the LG group, and the unique bacterial genus in the HG group were Prevotella, Stenotrophomonas, and Xanthomonadaceae. The prediction results of microbial function showed that a total of 18 KEGG differential pathways were generated between the two treatment groups, mainly manifested in metabolic pathways, signal transduction, and the immune system. In conclusion, the HG group promoted rumen fermentation by altering the microbial composition of lactating cows. Our findings provide a theoretical basis for the rational use of high-grain diets to achieve high yields in intensive dairy farming. Full article
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Figure 1
<p>Principal coordinate analysis (PCoA) and α-diversity indexes of bacterial community structure between the LG and HG treatments. (<b>A</b>) α-diversity indexes; (<b>B</b>) PCoA based on weighted Unifrac matrix; (<b>C</b>) PCoA based on unweighted Unifrac matrix. LG = Low-grain diet; HG = High-grain diet.</p>
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<p>Distribution of rumen bacterial community composition in LG and HG groups. (<b>A</b>) Phylum level; (<b>B</b>) genus level. LG = Low-grain diet; HG = High-grain diet.</p>
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<p>The linear discriminant analysis effect size (LEfSe) of the changes in the rumen bacterial community between LG and HG groups. (<b>A</b>) LDA score. The LDA score was derived from the LEfSe analysis, which showed the biomarker taxa LDA score &gt; 2 of rumen microbiota in LG and HG groups; (<b>B</b>) LG and HG group cladogram. A cladogram showing the relationships among taxa at phylum, class, order, family, and genus levels was generated using LEfSe analysis (n = 6 per group); (<b>C</b>) Predicting bacterial phenotype abundance heatmap. (<b>D</b>) The significant differential rumen bacteria affected between LG and HG groups. LG = Low-grain diet; HG = High-grain diet. * Indicates 0.01 &lt; <span class="html-italic">p</span> ≤ 0.05, ** indicates <span class="html-italic">p</span> ≤ 0.01.</p>
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<p>Spearman correlation between rumen fermentation characteristics and rumen microbiota (genus level). Red indicates a significant positive correlation (<span class="html-italic">p</span> &lt; 0.05), while blue indicates a significant negative correlation (<span class="html-italic">p</span> &lt; 0.05). A-P = Acetate/propionate. * Indicates 0.01 &lt; <span class="html-italic">p</span> ≤ 0.05, ** indicates <span class="html-italic">p</span> ≤ 0.01.</p>
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<p>Predicted functional profile of PICRUSt2 in microorganisms. LG = Low-grain diet; HG = High-grain diet.</p>
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14 pages, 3631 KiB  
Article
Salt-Reduced Fish Sauce Produced under Pressurized Carbon Dioxide Treatment Using Sardinops melanostictus, Trachurus japonicus, Konosirus punctatus, Odontamblyopus lacepedii, Their Collective Mixture, and Unused Fish Mixture
by Johma Tagawa, Mikihide Demura and Seiji Noma
Foods 2024, 13(17), 2646; https://doi.org/10.3390/foods13172646 - 23 Aug 2024
Viewed by 396
Abstract
Fish sauce is produced at high salt concentrations (>20%) to inhibit the growth of harmful microorganisms. The salt-reduced fish sauce (10% salt) was prepared under pressurized CO2 (pCO2) conditions at 30 °C and 5 MPa for 3 months ( [...] Read more.
Fish sauce is produced at high salt concentrations (>20%) to inhibit the growth of harmful microorganisms. The salt-reduced fish sauce (10% salt) was prepared under pressurized CO2 (pCO2) conditions at 30 °C and 5 MPa for 3 months (FSCO2), from Sardinops melanostictus, Odontamblyopus lacepedii, Trachurus japonicus, Konosirus punctatus, and their collective mixture, as well as unused fish mixture obtained from the Ariake Sea in Japan. FSCO2 exhibited significantly better microbial quality and free amino acid content, lighter color, standardized odor (dashi-like odor), and umami richness qualities compared to fish sauces prepared using the conventional method (FScon) (20% salt), as previously demonstrated, after a fermentation period of 2 months. Bacterial flora analysis implied that the standardization of odor and umami richness may not be the result of specific microbial metabolism. Even when using previously unused fish, it was possible to produce FSCO2 equivalent to that produced by conventional sardines and other fish. These results indicate that the quality of fish sauce can be improved. The flavor of FSCO2 became similar regardless of the type of fish and fermentation period using pCO2 during fermentation, leading to the effective utilization of unutilized fish as a resource for high-quality salt-reduced fish sauce. Full article
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<p>Appearances of fish sauce mashes. S, <span class="html-italic">S. melanostictus</span>; O, <span class="html-italic">O. lacepedii</span>; T, <span class="html-italic">T. japonicus</span>; K, <span class="html-italic">K. punctatus</span>; M, collective mixture; S*, <span class="html-italic">S. melanostictus</span>; U*, unused fish mixture. * The set of experiments comparing <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">F</mi> <mi mathvariant="normal">S</mi> </mrow> <mrow> <msub> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">O</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </msub> </mrow> </semantics></math> prepared from <span class="html-italic">S. melanostictus</span> and an unused fish mixture.</p>
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<p>Viable mesophilic counts of each fish sauce. S, <span class="html-italic">S. melanostictus</span>; O, <span class="html-italic">O. lacepedii</span>; T, <span class="html-italic">T. japonicus</span>; K, <span class="html-italic">K. punctatus</span>; M, collective mixture; S*, <span class="html-italic">S. melanostictus</span>; U*, unused fish mixture. Gray bars indicate <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">F</mi> <mi mathvariant="normal">S</mi> </mrow> <mrow> <mi mathvariant="normal">c</mi> <mi mathvariant="normal">o</mi> <mi mathvariant="normal">n</mi> </mrow> </msub> </mrow> </semantics></math>. *<sup>1</sup> Not detected. * Set of experiments comparing <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">F</mi> <mi mathvariant="normal">S</mi> </mrow> <mrow> <msub> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">O</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </msub> </mrow> </semantics></math> prepared from <span class="html-italic">S. melanostictus</span> (S*) and an unused fish mixture (U*).</p>
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<p>The numbers of peaks commonly observed in each fish sauce. S, <span class="html-italic">S. melanostictus</span>; O, <span class="html-italic">O. lacepedii</span>; T, <span class="html-italic">T. japonicus</span>; K, <span class="html-italic">K. punctatus</span>; M, collective mixture.</p>
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<p>Comparison of tastes detected in <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">F</mi> <mi mathvariant="normal">S</mi> </mrow> <mrow> <msub> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">O</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">F</mi> <mi mathvariant="normal">S</mi> </mrow> <mrow> <mi mathvariant="normal">c</mi> <mi mathvariant="normal">o</mi> <mi mathvariant="normal">n</mi> </mrow> </msub> </mrow> </semantics></math> with radar charts and their PCA. Blue and red lines/dots in the graphs indicate <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">F</mi> <mi mathvariant="normal">S</mi> </mrow> <mrow> <msub> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">O</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">F</mi> <mi mathvariant="normal">S</mi> </mrow> <mrow> <mi mathvariant="normal">c</mi> <mi mathvariant="normal">o</mi> <mi mathvariant="normal">n</mi> </mrow> </msub> </mrow> </semantics></math>, respectively. Blue and red dots on PCA graphs show <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">F</mi> <mi mathvariant="normal">S</mi> </mrow> <mrow> <msub> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">O</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">F</mi> <mi mathvariant="normal">S</mi> </mrow> <mrow> <mi mathvariant="normal">c</mi> <mi mathvariant="normal">o</mi> <mi mathvariant="normal">n</mi> </mrow> </msub> </mrow> </semantics></math>, respectively. S, <span class="html-italic">S. melanostictus</span>; O, <span class="html-italic">O. lacepedii</span>; T, <span class="html-italic">T. japonicus</span>; K, <span class="html-italic">K. punctatus</span>; M, collective mixture; S*, <span class="html-italic">S. melanostictus</span>; U*, unused fish mixture. * The set of experiments comparing <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">F</mi> <mi mathvariant="normal">S</mi> </mrow> <mrow> <msub> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">O</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </msub> </mrow> </semantics></math> prepared from <span class="html-italic">S. melanostictus</span> (S*) and an unused fish mixture (U*).</p>
Full article ">Figure 5
<p>PCA analysis of free amino acid relative composition of fish sauces. Blue and red dots in the graphs indicate <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">F</mi> <mi mathvariant="normal">S</mi> </mrow> <mrow> <msub> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">O</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">F</mi> <mi mathvariant="normal">S</mi> </mrow> <mrow> <mi mathvariant="normal">c</mi> <mi mathvariant="normal">o</mi> <mi mathvariant="normal">n</mi> </mrow> </msub> </mrow> </semantics></math>, respectively. S, <span class="html-italic">S. melanostictus</span>; O, <span class="html-italic">O. lacepedii</span>; T, <span class="html-italic">T. japonicus</span>; K, <span class="html-italic">K. punctatus</span>; M, collective mixture.</p>
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<p>16S rRNA amplicon sequencing analysis and presented as relative ratios of microbiota. S, <span class="html-italic">S. melanostictus</span>; O, <span class="html-italic">O. lacepedii</span>; T, <span class="html-italic">T. japonicus</span>; K, <span class="html-italic">K. punctatus</span>; M, collective mixture.</p>
Full article ">
17 pages, 3396 KiB  
Article
The Application of Carbon-Based Fertilizer Changed the Microbial Composition and Co-Occurrence Network Topological Properties of Vineyard Soil
by Ping Sun, Jiaqi Wu, Xianrui Lin, Chenfei Chen, Jianxi Zhu, Yi Wang, Jian Zhou, Huaxin Wang, Jiansheng Shen and Huijuan Jia
Horticulturae 2024, 10(8), 871; https://doi.org/10.3390/horticulturae10080871 - 18 Aug 2024
Viewed by 471
Abstract
Charcoal-based fertilizer could be used extensively and is environmentally friendly. An experiment was designed to investigate the effects of different charcoal-based fertilizer application methods on soil microbiology and grape quality in a vineyard to guide the cultivation of ‘Shine-Muscat’. A control treatment without [...] Read more.
Charcoal-based fertilizer could be used extensively and is environmentally friendly. An experiment was designed to investigate the effects of different charcoal-based fertilizer application methods on soil microbiology and grape quality in a vineyard to guide the cultivation of ‘Shine-Muscat’. A control treatment without fertilization and six other treatments were set up. Four treatments applied carbon-based fertilizer as a base fertilizer with or without potassium fulvic acid, a complex microbial agent, or Bacillus subtilis, and two treatments were only applied with two applications of carbon-based fertilizer or compound fertilizer during the expansion period. The results showed that the bacterial phyla were mainly Proteobacteria and Bacteroidetes. Ascomycota, Basidiomycota, and Mortierellomycota dominated the fungal community. At the genus level, the composition of fungi, compared to bacteria, varied significantly, while the dominant flora differed among fertilization practices. Application of charcoal-based fertilizer enriched beneficial microorganisms, while chemical fertilizers enriched pathogenic microorganisms. The addition of microbial fungicides and biostimulants for a period reduced the size of the microbial network, lowered positive correlations, and enhanced resistance to adverse conditions and diseases and there was no significant correlation between agronomic traits and microbial network topology. A combination of soil microbial and grape agronomic traits suggests that a charcoal-based fertilizer base, with microbial fungicides applied, is the optimal fertilization regimen for grape. Full article
(This article belongs to the Section Viticulture)
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<p>Analysis of soil microbial diversity in different treatment groups: (<b>A</b>) bacterial ACE index; (<b>B</b>) bacterial Shannon index; (<b>C</b>) bacterial principal co-ordinates analysis (PCoA); (<b>D</b>) fungal ACE index; (<b>E</b>) fungal Shannon index; (<b>F</b>) fungal PCoA. Note: PC1 and PC2, principal co-ordinates 1 and 2, respectively; CK: no fertilizer; Cbf: 400 g/plant charcoal-based fertilizer as the base fertilizer; CbfP: 400 g/plant charcoal-based fertilizer as the base fertilizer + 40 g/plant potassium fulvic acid in the expansion period; CbfM: 400 g/plant charcoal-based fertilizer as the base fertilizer + 10 g/plant composite microbial fungus in the expansion period; CbfB: 400 g/plant charcoal-based fertilizer as the base fertilizer + 10 g/plant <span class="html-italic">Bacillus subtilis</span> in the expansion period; Cbf2: two applications of 200 g/plant charcoal-based fertilizer in the expansion period; Cf: two applications of 100 g/plant composite fertilizer in the expansion period; * and ** indicate <span class="html-italic">p</span> &lt; 0.05 and <span class="html-italic">p</span> &lt; 0.01, respectively.</p>
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<p>Relative abundance of bacteria in different treatment groups at the (<b>A</b>) phylum and (<b>B</b>) genus levels. CK: no fertilizer; Cbf: 400 g/plant charcoal-based fertilizer as the base fertilizer; CbfP: 400 g/plant charcoal-based fertilizer as the base fertilizer + 40 g/plant potassium fulvic acid in the expansion period; CbfM: 400 g/plant charcoal-based fertilizer as the base fertilizer + 10 g/plant composite microbial fungus in the expansion period; CbfB: 400 g/plant charcoal-based fertilizer as the base fertilizer + 10 g/plant <span class="html-italic">Bacillus subtilis</span> in the expansion period; Cbf2: two applications of 200 g/plant charcoal-based fertilizer in the expansion period; Cf: two applications of 100 g/plant composite fertilizer in the expansion period.</p>
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<p>Relative abundance of fungi in different treatment groups at the (<b>A</b>) phylum, (<b>B</b>) genus, and (<b>C</b>) species levels. CK: no fertilizer; Cbf: 400 g/plant charcoal-based fertilizer as the base fertilizer; CbfP: 400 g/plant charcoal-based fertilizer as the base fertilizer + 40 g/plant potassium fulvic acid in the expansion period; CbfM: 400 g/plant charcoal-based fertilizer as the base fertilizer + 10 g/plant composite microbial fungus in the expansion period; CbfB: 400 g/plant charcoal-based fertilizer as the base fertilizer + 10 g/plant <span class="html-italic">Bacillus subtilis</span> in the expansion period; Cbf2: two applications of 200 g/plant charcoal-based fertilizer in the expansion period; Cf: two applications of 100 g/plant composite fertilizer in the expansion period.</p>
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<p>Results of linear discriminant effect size analysis for different treatments for: (<b>A</b>) bacteria and (<b>B</b>) fungi using Log10 Linear Discriminant Analysis (LDA) (&gt;3). CK: no fertilizer; Cbf: 400 g/plant charcoal-based fertilizer as the base fertilizer; CbfP: 400 g/plant charcoal-based fertilizer as the base fertilizer + 40 g/plant potassium fulvic acid in the expansion period; CbfM: 400 g/plant charcoal-based fertilizer as the base fertilizer + 10 g/plant composite microbial fungus in the expansion period; CbfB: 400 g/plant charcoal-based fertilizer as the base fertilizer + 10 g/plant <span class="html-italic">Bacillus subtilis</span> in the expansion period; Cbf2: two applications of 200 g/plant charcoal-based fertilizer in the expansion period; Cf: two applications of 100 g/plant composite fertilizer in the expansion period.</p>
Full article ">Figure 4 Cont.
<p>Results of linear discriminant effect size analysis for different treatments for: (<b>A</b>) bacteria and (<b>B</b>) fungi using Log10 Linear Discriminant Analysis (LDA) (&gt;3). CK: no fertilizer; Cbf: 400 g/plant charcoal-based fertilizer as the base fertilizer; CbfP: 400 g/plant charcoal-based fertilizer as the base fertilizer + 40 g/plant potassium fulvic acid in the expansion period; CbfM: 400 g/plant charcoal-based fertilizer as the base fertilizer + 10 g/plant composite microbial fungus in the expansion period; CbfB: 400 g/plant charcoal-based fertilizer as the base fertilizer + 10 g/plant <span class="html-italic">Bacillus subtilis</span> in the expansion period; Cbf2: two applications of 200 g/plant charcoal-based fertilizer in the expansion period; Cf: two applications of 100 g/plant composite fertilizer in the expansion period.</p>
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<p>Bacterial and fungal co-occurrence network diagram for the seven treatment groups. Node size and the number of node-connected edges are positively correlated. CK: no fertilizer; Cbf: 400 g/plant charcoal-based fertilizer as the base fertilizer; CbfP: 400 g/plant charcoal-based fertilizer as the base fertilizer + 40 g/plant potassium fulvic acid in the expansion period; CbfM: 400 g/plant charcoal-based fertilizer as the base fertilizer + 10 g/plant composite microbial fungus in the expansion period; CbfB: 400 g/plant charcoal-based fertilizer as the base fertilizer + 10 g/plant <span class="html-italic">Bacillus subtilis</span> in the expansion period; Cbf2: two applications of 200 g/plant charcoal-based fertilizer in the expansion period; Cf: two applications of 100 g/plant composite fertilizer in the expansion period.</p>
Full article ">Figure 6
<p>Microbial network topological complementary attributes correlated with grape agronomic traits. CK: no fertilizer; Cbf: 400 g/plant charcoal-based fertilizer as the base fertilizer; CbfP: 400 g/plant charcoal-based fertilizer as the base fertilizer + 40 g/plant potassium fulvic acid in the expansion period; CbfM: 400 g/plant charcoal-based fertilizer as the base fertilizer + 10 g/plant composite microbial fungus in the expansion period; CbfB: 400 g/plant charcoal-based fertilizer as the base fertilizer + 10 g/plant <span class="html-italic">Bacillus subtilis</span> in the expansion period; Cbf2: two applications of 200 g/plant charcoal-based fertilizer in the expansion period; Cf: two applications of 100 g/plant composite fertilizer in the expansion period.</p>
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66 pages, 2244 KiB  
Review
Experimental Models in Unraveling the Biological Mechanisms of Mushroom-Derived Bioactives against Aging- and Lifestyle-Related Diseases: A Review
by Rajasekharan Sharika, Kuljira Mongkolpobsin, Panthakarn Rangsinth, Mani Iyer Prasanth, Sunita Nilkhet, Paweena Pradniwat, Tewin Tencomnao and Siriporn Chuchawankul
Nutrients 2024, 16(16), 2682; https://doi.org/10.3390/nu16162682 - 13 Aug 2024
Viewed by 1090
Abstract
Mushrooms have garnered considerable interest among researchers due to their immense nutritional and therapeutic properties. The presence of biologically active primary and secondary metabolites, which includes several micronutrients, including vitamins, essential minerals, and other dietary fibers, makes them an excellent functional food. Moreover, [...] Read more.
Mushrooms have garnered considerable interest among researchers due to their immense nutritional and therapeutic properties. The presence of biologically active primary and secondary metabolites, which includes several micronutrients, including vitamins, essential minerals, and other dietary fibers, makes them an excellent functional food. Moreover, the dietary inclusion of mushrooms has been reported to reduce the incidence of aging- and lifestyle-related diseases, such as cancer, obesity, and stroke, as well as to provide overall health benefits by promoting immunomodulation, antioxidant activity, and enhancement of gut microbial flora. The multifunctional activities of several mushroom extracts have been evaluated by both in vitro and in vivo studies using cell lines along with invertebrate and vertebrate model systems to address human diseases and disorders at functional and molecular levels. Although each model has its own strengths as well as lacunas, various studies have generated a plethora of data regarding the regulating players that are modulated in order to provide various protective activities; hence, this review intends to compile and provide an overview of the plausible mechanism of action of mushroom-derived bioactives, which will be helpful in future medicinal explorations. Full article
(This article belongs to the Section Phytochemicals and Human Health)
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<p>Overview of the different health-enhancing benefits exhibited by edible mushrooms reported in different <span class="html-italic">in vitro</span> and <span class="html-italic">in vivo</span> models.</p>
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<p>Schematic representation of <span class="html-italic">C. elegans</span> as a model to study the overall mechanisms of action of biomolecules isolated from mushrooms. The different biomolecules from mushrooms can modulate different transcription factors, including DAF-16 and SKN-1, regulate stress resistance, and improve anti-aging and neuroprotective potential.</p>
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<p>Schematic representation of the impact of dietary inclusion of mushrooms and their protective effects against neurodegenerative, neuropsychiatric, and neuronal inflammation-mediated disorders. Mushroom bioactives are able to improve NRF levels and thereby reduce the levels of ROS and improve neuronal outgrowth.</p>
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20 pages, 11207 KiB  
Article
Natural Foraging Selection and Gut Microecology of Two Subterranean Rodents from the Eurasian Steppe in China
by Zhenghaoni Shang, Kai Chen, Tingting Han, Fan Bu, Shanshan Sun, Na Zhu, Duhu Man, Ke Yang, Shuai Yuan and Heping Fu
Animals 2024, 14(16), 2334; https://doi.org/10.3390/ani14162334 - 13 Aug 2024
Viewed by 537
Abstract
As the most abundant group of mammals, rodents possess a very rich ecotype, which makes them ideal for studying the relationship between diet and host gut microecology. Zokors are specialized herbivorous rodents adapted to living underground. Unlike more generalized herbivorous rodents, they feed [...] Read more.
As the most abundant group of mammals, rodents possess a very rich ecotype, which makes them ideal for studying the relationship between diet and host gut microecology. Zokors are specialized herbivorous rodents adapted to living underground. Unlike more generalized herbivorous rodents, they feed on the underground parts of grassland plants. There are two species of the genus Myospalax in the Eurasian steppes in China: one is Myospalax psilurus, which inhabits meadow grasslands and forest edge areas, and the other is M. aspalax, which inhabits typical grassland areas. How are the dietary choices of the two species adapted to long-term subterranean life, and what is the relationship of this diet with gut microbes? Are there unique indicator genera for their gut microbial communities? Relevant factors, such as the ability of both species to degrade cellulose, are not yet clear. In this study, we analyzed the gut bacterial communities and diet compositions of two species of zokors using 16S amplicon technology combined with macro-barcoding technology. We found that the diversity of gut microbial bacterial communities in M. psilurus was significantly higher than that in M. aspalax, and that the two species of zokors possessed different gut bacterial indicator genera. Differences in the feeding habits of the two species of zokors stem from food composition rather than diversity. Based on the results of Mantel analyses, the gut bacterial community of M. aspalax showed a significant positive correlation with the creeping-rooted type food, and there was a complementary relationship between the axis root-type-food- and the rhizome-type-food-dominated (containing bulb types and tuberous root types) food groups. Functional prediction based on KEGG found that M. psilurus possessed a stronger degradation ability in the same cellulose degradation pathway. Neutral modeling results show that the gut flora of the M. psilurus has a wider ecological niche compared to that of the M. aspalax. This provides a new perspective for understanding how rodents living underground in grassland areas respond to changes in food conditions. Full article
(This article belongs to the Section Ecology and Conservation)
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<p>Schematic diagram of the sampling points for two species of zokors. “□” indicates the capture location of <span class="html-italic">M. psilurus</span> and “○” indicates the capture location of the <span class="html-italic">M. aspalax</span>.</p>
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<p>Food composition of the two species of zokors. (<b>A</b>) Top 10 species in the diet of <span class="html-italic">M. psilurus</span>. (<b>B</b>) Top 10 species in the diet of <span class="html-italic">M. aspalax</span>. (<b>C</b>). <span class="html-italic">t</span>-test for between-group differences in food composition between the two species of zokors, with <span class="html-italic">p</span>-values corrected using the Fdr method, and confidence intervals calculated using Welch’s t-method, with a confidence level of 0.95. <span class="html-italic">p</span>-values after correction are shown on the right side, denoted as * 0.01 &lt; <span class="html-italic">p</span> ≤ 0.05, ** 0.001 &lt; <span class="html-italic">p</span> ≤ 0.01 and *** <span class="html-italic">p</span> ≤ 0.001.</p>
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<p>Microbial diversity and species composition in the guts of two species of zokors. (<b>A</b>) Venn diagram showing the percentages of OTUs common and unique to the two species of zokors. (<b>B</b>) Test of intergroup differences in ACE index of the gut bacterial community of the two species of zokors. (<b>C</b>) Test of intergroup differences in the Shannon index of the intestinal bacterial communities of the two species of zokors. (<b>D</b>) Test of intergroup differences in the Pd index of the intestinal bacterial communities of the two species of zokors. (<b>E</b>) Composition of the two zokor species’ intestinal bacterial communities at the genus level. Significance levels are denoted as *** <span class="html-italic">p</span> ≤ 0.001.</p>
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<p>Mantel analyses of food types and gut bacterial communities of the two species of <span class="html-italic">Myospalax</span>. The color of the line indicates the <span class="html-italic">p</span>-value of the correlation between the gut microbial community and the food composition distance matrix (blue: <span class="html-italic">p</span> &lt; 0.05; gray: <span class="html-italic">p</span> ≥ 0.05). The thickness of the line indicates the strength of the correlation between the microbial community β-diversity distance matrix and the food composition distance matrix (wide: r ≥ 0.4; medium: 0.4 ≥ r ≥ 0.2; thin: r &lt; 0.2). The solid lines indicate positive correlations and dashed lines indicate negative correlations. The colors of the boxes reflect the strength and direction of correlation between food types, with blue representing a positive correlation and red representing a negative correlation. * 0.01 &lt; <span class="html-italic">p</span> ≤ 0.05, ** 0.001 &lt; <span class="html-italic">p</span> ≤ 0.01 and *** <span class="html-italic">p</span> ≤ 0.001.</p>
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<p>Abundance and indicative values of bacterial indicator genera in the gut flora of two species of <span class="html-italic">Myospalax</span>. Indicator genera are listed on the <span class="html-italic">y</span>-axis, and their indicator values are represented on the <span class="html-italic">x</span>-axis. The colors of the circles and the background represent the two species of zokors, pink for <span class="html-italic">M. psilurus</span> and blue for <span class="html-italic">M. aspalax</span>, with the size of the “○” indicating the abundance of the indicator genera.</p>
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<p>Functions of metabolism-related intestinal bacterial communities, including differences between the two species of <span class="html-italic">Myospalax</span>. The <span class="html-italic">x</span>-axis of the left bar graph indicates the average absolute abundance of the three-level pathways in different subgroups, and the <span class="html-italic">y</span>-axis lists the names of the pathways. The circles with letters on the right side of the bar graph represent the secondary pathway to which the corresponding functional pathway belongs: A for amino acid metabolism, C for carbohydrate metabolism, and G for glycan biosynthesis and metabolism. The middle area is the confidence interval, and the dots represent the average absolute abundance of the species in the two groups. The bars on the dots are the upper and lower limits of the confidence intervals for the difference, and the right-hand side is the corrected <span class="html-italic">p</span>-value, *** <span class="html-italic">p</span> ≤ 0.001.</p>
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<p>Predicted abundance of cellulose degradation-related genes. The <span class="html-italic">y</span>-axis represents the predicted values of absolute abundance of related genes. *** <span class="html-italic">p</span> ≤ 0.001.</p>
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<p>Estimation of the neutral community model for the gut bacterial communities of the two species of zokors. The <span class="html-italic">x</span>-axis represents the logarithm of the average relative abundance of species, and the <span class="html-italic">y</span>-axis depicts the frequency of occurrence. The solid line represents the fit of the neutral model, and the upper and lower dashed lines represent the 95% confidence of the model prediction. R<sup>2</sup> represents the overall goodness-of-fit of the neutral community model, and the higher R<sup>2</sup> indicates the model is closer to the neutral model, which means that the construction of the community is more affected by stochastic processes, and less by deterministic processes. Nm is the product of metacommunity size (N) and migration rate (m) (Nm = N × m), which is used to assess the degree of dispersal among communities.</p>
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13 pages, 847 KiB  
Article
Oral Prevalence of Selenomonas noxia Differs among Orthodontic Patients Compared to Non-Orthodontic Controls: A Retrospective Biorepository Analysis
by Kyle Hodges, Payton Famuliner, Karl Kingsley and Katherine M. Howard
Pathogens 2024, 13(8), 670; https://doi.org/10.3390/pathogens13080670 - 8 Aug 2024
Viewed by 526
Abstract
The oral microbial flora may be significantly altered by orthodontic therapy and the use of fixed orthodontic brackets. Most orthodontic research has focused on cariogenic pathogens, while some evidence has demonstrated an increase in many known periodontal pathogens. However, little is known about [...] Read more.
The oral microbial flora may be significantly altered by orthodontic therapy and the use of fixed orthodontic brackets. Most orthodontic research has focused on cariogenic pathogens, while some evidence has demonstrated an increase in many known periodontal pathogens. However, little is known about the prevalence of the Gram-negative periodontal pathogen Selenomonas noxia (SN) among these patients. Using an existing saliva biorepository, n = 208 samples from adult and pediatric orthodontic and non-orthodontic patients were identified and screened for the presence of SN using qPCR and validated primers. In the pediatric study sample (n = 89), 36% tested positive for the presence of SN, with orthodontic patients comprising more SN-positive samples (87.5%) than SN-negative samples (78.9%), p = 0.0271. In the adult study sample (n = 119), SN was found in 28.6%, with orthodontic patients comprising 58.8% of positive samples and only 28.2% of negative samples (p < 0.0001). These data demonstrated that both pediatric and adult orthodontic patients exhibited higher prevalence of SN compared with age-matched non-orthodontic controls. As this microorganism is associated not only with periodontal disease but also long-term health issues such as obesity, more research is needed regarding the factors that increase the prevalence of this microbe. Full article
(This article belongs to the Special Issue Oral Microbiome and Human Systemic Health)
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<p>Pediatric sample screening for SN. More than one-third, or 35.9% (<span class="html-italic">n</span> = 32/89), of pediatric samples harbored SN, while 64.1%, or <span class="html-italic">n</span> = 57/89, were SN-negative, and the samples were equally divided among males (53.1% or <span class="html-italic">n</span> = 17/32) and females (46.9% or <span class="html-italic">n</span> = 15/32). Most SN-positive samples were minority patients (62.5% or <span class="html-italic">n</span> = 20/32) compared with White or Caucasian patients (37.5% or <span class="html-italic">n</span> = 12/32), an outcome that was similar to the overall study sample (65.2% or <span class="html-italic">n</span> = 58/89 and 34.8% or <span class="html-italic">n</span> = 31/89, respectively). SN-positive samples were more prevalent among pediatric orthodontic patients (87.5% or <span class="html-italic">n</span> = 28/32) versus non-orthodontic patients (12.5% or <span class="html-italic">n</span> = 4/32), an observation that was different than the overall proportion of orthodontic versus non-orthodontic pediatric study samples (53.9% or <span class="html-italic">n</span> = 48/89 and 46.1% or <span class="html-italic">n</span> = 41/89, respectively), <span class="html-italic">p</span> = 0.0001. NC = negative control, PC = positive control, (qPCR) CT = cycle threshold value, ND = not detected.</p>
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<p>Adult sample screening for SN. Approximately one-third, or 28.6% (<span class="html-italic">n</span> = 34/119), of adult samples harbored SN, while 71.4%, or <span class="html-italic">n</span> = 85/119, were SN-negative, which was divided nearly equally among males (52.9% or <span class="html-italic">n</span> = 18/34) and females (47.1% or <span class="html-italic">n</span> = 16/34). Most SN-positive adult samples were also from non-White or minority patients (73.5% or <span class="html-italic">n</span> = 25/34), which was similar to the overall adult study sample (68.1% or <span class="html-italic">n</span> = 81/119), <span class="html-italic">p</span> = 0.1984. SN-positive adult samples were more prevalent among orthodontic patients (58.8% or <span class="html-italic">n</span> = 20/34) than the overall proportion of adult orthodontic study samples (36.9% or <span class="html-italic">n</span> = 44/119), <span class="html-italic">p</span> = 0.0001. NC = negative control, PC = positive control, (qPCR) CT = cycle threshold value, ND = not detected.</p>
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17 pages, 2918 KiB  
Article
Clostridium butyricum Prevents Diarrhea Incidence in Weaned Piglets Induced by Escherichia coli K88 through Rectal Bacteria–Host Metabolic Cross-Talk
by Jing Liang, Sihu Wang, Shasha Kou, Cheng Chen, Wenju Zhang and Cunxi Nie
Animals 2024, 14(16), 2287; https://doi.org/10.3390/ani14162287 - 6 Aug 2024
Viewed by 570
Abstract
This study aimed to evaluate the effects of Clostridium butyricum (C. butyricum) on the prevention of the diarrhea rates and growth performances of weaned piglets induced by Escherichia coli K88 (E. coli K88). Twenty-four weaned piglets (6.92 ± 0.11 kg) [...] Read more.
This study aimed to evaluate the effects of Clostridium butyricum (C. butyricum) on the prevention of the diarrhea rates and growth performances of weaned piglets induced by Escherichia coli K88 (E. coli K88). Twenty-four weaned piglets (6.92 ± 0.11 kg) were randomly assigned to one of three treatment groups for a period of 21 days. Each group consisted of eight pigs, with each pig being housed in an individual pen. Group I received the control diet along with normal saline, Group II received the control diet along with E. coli K88, and Group III received the control diet supplemented with 5 × 108 CFU/kg of C. butyricum and E. coli K88. We examined alterations in rectal microbiota and metabolites, analyzed the incidence of diarrhea, and investigated the interactions between microbiota and metabolites through the application of Illumina MiSeq sequencing and liquid chromatography–mass spectrometry. The results showed that, from days 14 to 21, the diarrhea incidence in Group III decreased significantly by 83.29% compared to Group II (p < 0.05). Over the entire experimental duration, the average daily feed intake of Group III decreased significantly by 11.13% compared to Group I (p < 0.05), while the diarrhea incidence in Group III decreased by 71.46% compared to Group II (p < 0.05). The predominant microbial flora in the rectum consisted of Firmicutes (57.32%), Bacteroidetes (41.03%), and Proteobacteria (0.66%). Administering E. coli K88 orally can elevate the relative abundance of Megasphaera (p < 0.05). Conversely, the supplementation of C. butyricum in the diet reduced the relative abundance of Megasphaera (p < 0.05), while increasing the relative abundance of unclassified_f_Lachnospiraceae (p < 0.05). Rectal metabolomics analysis revealed that supplementing C. butyricum in the feed significantly altered the amino acids and fatty acids of the piglets infected with E. coli K88 (p < 0.05). The correlation analysis showed that the occurrence of diarrhea was inversely related to adipic acid (p < 0.05) and positively associated with (5-hydroxyindol-3-YL) acetic acid and L-aspartic acid (p < 0.05). Prevotella_1 exhibited a negative correlation with octadecanoic acid (p < 0.05). Prevotellaceae_UCG-005 showed a negative correlation with (5-hydroxyindol-3-YL) acetic acid (p < 0.05). The findings from this research study aid in probiotic development and the enhancement of healthy growth in weaned piglets. Full article
(This article belongs to the Section Animal Nutrition)
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<p>Venn diagram, rank–abundance and rarefaction curves of fecal microorganism in weaned piglets in different treatment groups. (<b>A</b>) Venn diagram; (<b>B</b>) rank–abundance curve; (<b>C</b>) rarefaction curve.</p>
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<p>Non-metric multidimensional scaling analysis (<b>A</b>) and partial least-squares discriminant analysis (<b>B</b>) at the OTU level for three treatment groups.</p>
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<p>Classification of the bacterial community composition across the three different treatment groups. (<b>A</b>) Relative abundance of bacterial phylum level and (<b>B</b>) relative abundance of bacterial genus level.</p>
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<p>Heatmap showing the most relative abundance of dominant bacterial OTUs. Note: the relative values are indicated by color intensity, with the legend indicated at the right corner.</p>
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<p>Two-dimensional score plots of fecal metabolites in three different treatment groups. (<b>A</b>,<b>B</b>) partial least-squares discriminant analysis (PLS-DA) in positive/negative ion modes, (<b>C</b>,<b>D</b>) PLS-DA replacement test in positive/negative ion mode, and (<b>E</b>,<b>F</b>) orthogonal partial least-squares discriminant analysis (OPLS-DA) in positive/negative ion mode (<span class="html-italic">n</span> = 6).</p>
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<p>Pathway enrichment map analysis of differential metabolites in feces between (<b>A</b>–<b>C</b>) using MetaboAnalystR 3.0. Note: the color of the circles from white to yellow to red denotes incremental fold change (−log(<span class="html-italic">p</span>)). The size of the circles from small to large indicates an increment of the impact of the pathway.</p>
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<p>Correlation between the body weight, diarrhea incidence, differential microbiota (at the genera level), and metabolites. (<b>A</b>) Correlation between body weight, diarrhea incidence, and microbiota. (<b>B</b>) Correlation between body weight, diarrhea incidence, and metabolites. (<b>C</b>) Correlation between microbiota and metabolites. Note: the strength (Spearman’s ρ value) and significance of correlations are shown as color in shades (red, positive correlation; blue, negative correlation). The values above/below zero represent positive/negative correlations. Significant correlations are noted by * <span class="html-italic">p</span> ≤ 0.05, ** <span class="html-italic">p</span> ≤ 0.01, *** <span class="html-italic">p</span> ≤ 0.001.</p>
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22 pages, 11424 KiB  
Article
A Comparative Study of Microbial Communities, Biogenic Amines, and Volatile Profiles in the Brewing Process of Rice Wines with Hongqu and Xiaoqu as Fermentation Starters
by Yingyin Yan, Zihua Liang, Yujia Huo, Qi Wu, Li Ni and Xucong Lv
Foods 2024, 13(15), 2452; https://doi.org/10.3390/foods13152452 - 2 Aug 2024
Viewed by 733
Abstract
Rice wine is primarily crafted from grains through saccharification and liquification with the help of Qu. Qu plays an important role in the formation of the flavor quality of rice wine. Hongqu and Xiaoqu represent two prevalent varieties of Qu that are typically [...] Read more.
Rice wine is primarily crafted from grains through saccharification and liquification with the help of Qu. Qu plays an important role in the formation of the flavor quality of rice wine. Hongqu and Xiaoqu represent two prevalent varieties of Qu that are typically utilized in the brewing process of rice wine and play a crucial role in its production. In this study, GC, GC-MS, HPLC, and metagenomic sequencing techniques were used to contrast the microbial flora, biogenic amines, and aroma characteristics developed during the fermentation of rice wines, with Hongqu and Xiaoqu being used as initiating agents for the brewing process. The results show that the content of higher alcohols (including n-propanol, isobutanol, 3-methyl-1-butanol, and phenethyl alcohol) in rice wine brewed with Xiaoqu (XQW) was significantly higher than that in rice wine brewed with Hongqu (HQW). Contrarily, the concentration of biogenic amines in HQW surpassed that of XQW by a notable margin, but tyramine was significantly enriched in XQW and not detected in HQW. In addition, a multivariate statistical analysis revealed distinct disparities in the constitution of volatile components between HQW and XQW. Hexanoic acid, ethyl acetate, isoamyl acetate, ethyl caproate, ethyl decanoate, 2-methoxy-4-vinylphenol, etc., were identified as the characteristic aroma-active compounds in HQW and XQW. A microbiome analysis based on metagenomic sequencing showed that HQW and XQW had different dominant microorganisms in the brewing process. Burkholderia, Klebsiella, Leuconostoc, Monascus, and Aspergillus were identified as the primary microbial genera in the HQW fermentation period, while Pediococcus, Enterobacter, Rhizopus, Ascoidea, and Wickerhamomyces were the main microbial genera in the XQW brewing process. A bioinformatics analysis revealed that the concentrations of microbial genes involved in biogenic amines and esters biosynthesis were significantly higher in HQW than those in XQW, while the content of genes relevant to glycolysis, higher alcohol biosynthesis, and fatty acid metabolism was significantly higher in XQW than in HQW, which are the possible reasons for the difference in flavor quality between the two kinds of rice wine from the perspective of microbial functional genes. Full article
(This article belongs to the Section Drinks and Liquid Nutrition)
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Graphical abstract
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<p>The dynamics of physicochemical parameters (including reducing sugar, ethanol, total acid, and amino nitrogen) during the HQW and XQW brewing processes.</p>
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<p>The dynamics of higher alcohols (including n-propanol, isobutanol, 3-methyl-1-butanol, and phenethyl alcohol) during the HQW and XQW brewing processes.</p>
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<p>The dynamic change in biogenic amines (BAs, including putrescine, cadaverine, tyramine, spermine, and spermidine) during the brewing of HQW and XQW.</p>
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<p>The dynamic changes in the volatile flavor components during the brewing of HQW and XQW. (<b>A</b>) A heatmap of the abundances of volatile components. (<b>B</b>) A principal component analysis (PCA) score scatter plot. (<b>C</b>) A PCA loading scatter plot. (<b>D</b>) A hierarchical clustering diagram.</p>
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<p>A stacked histogram of the relative abundance of the predominant bacterial (<b>A</b>) and fungal (<b>B</b>) genera during the HQW and XQW brewing processes.</p>
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<p>A stacked histogram of the relative abundance of the predominant bacterial (<b>A</b>) and fungal (<b>B</b>) species during the HQW and XQW brewing processes.</p>
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<p>A visualization of the differences in the relative abundance of bacterial (<b>A</b>) and fungal (<b>B</b>) species between HQW and XQW. Microbial species with significant differences between HQW and XQW were determined using Welsh’s <span class="html-italic">t</span>-test, and the Benjamini–Hochberg procedure was used to control the false discovery rate due to multiple tests. The corrected <span class="html-italic">p</span> values are shown on the right.</p>
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<p>A correlation analysis between the characteristic volatile components, biogenic amines (BAs), and the predominant microbial phylotypes at the species level during the HQW and XQW brewing processes. (<b>A</b>) Key bacterial species—volatile components/biogenic amines (BAs); (<b>B</b>) key fungal species—volatile components/biogenic amines (BAs).</p>
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<p>A bubble chart of the abundances of microbial genes for enzymes closely related to the metabolism of characteristic volatile components (<b>A</b>) and biogenic amines (<b>B</b>) during the HQW and XQW brewing processes.</p>
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26 pages, 5132 KiB  
Article
Microbial Diversity of Soil in a Mediterranean Biodiversity Hotspot: Parque Nacional La Campana, Chile
by Carolina Quinteros-Urquieta, Jean-Pierre Francois, Polette Aguilar-Muñoz, Roberto Orellana, Rodrigo Villaseñor, Andres Moreira-Muñoz and Verónica Molina
Microorganisms 2024, 12(8), 1569; https://doi.org/10.3390/microorganisms12081569 - 31 Jul 2024
Viewed by 600
Abstract
Parque Nacional La Campana (PNLC) is recognized worldwide for its flora and fauna, rather than for its microbial richness. Our goal was to characterize the structure and composition of microbial communities (bacteria, archaea and fungi) and their relationship with the plant communities typical [...] Read more.
Parque Nacional La Campana (PNLC) is recognized worldwide for its flora and fauna, rather than for its microbial richness. Our goal was to characterize the structure and composition of microbial communities (bacteria, archaea and fungi) and their relationship with the plant communities typical of PNLC, such as sclerophyllous forest, xerophytic shrubland, hygrophilous forest and dry sclerophyllous forest, distributed along topoclimatic variables, namely, exposure, elevation and slope. The plant ecosystems, the physical and chemical properties of organic matter and the soil microbial composition were characterized by massive sequencing (iTag-16S rRNA, V4 and ITS1-5F) from the DNA extracted from the soil surface (5 cm, n = 16). A contribution of environmental variables, particularly related to each location, is observed. Proteobacteria (35.43%), Actinobacteria (32.86%), Acidobacteria (10.07%), Ascomycota (76.11%) and Basidiomycota (15.62%) were the dominant phyla. The beta diversity (~80% in its axes) indicates that bacteria and archaea are linked to their plant categories, where the xerophytic shrub stands out with the most particular microbial community. More specifically, Crenarchaeote, Humicola and Mortierella were dominant in the sclerophyllous forest; Chloroflexi, Cyanobacteria and Alternaria in the xerophytic shrubland; Solicoccozyma in the dry sclerophyllous forest; and Cladophialophora in the hygrophilous forest. In conclusion, the structure and composition of the microbial consortia is characteristic of PNLC’s vegetation, related to its topoclimatic variables, which suggests a strong association within the soil microbiome. Full article
(This article belongs to the Special Issue Advances in Soil Microbial Ecology)
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<p>Map that shows PNLC sampling locations (1, 2, 3, 4). Vegetation map, adapted from Hauck et al. [<a href="#B38-microorganisms-12-01569" class="html-bibr">38</a>].</p>
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<p>Graphical representation of the four sample sites characterized by their exposure and elevation.</p>
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<p>Physicochemical variables of the soil samples: (<b>a</b>) C/N. Significant C/N differences were observed between the hygrophilous forest and the xerophytic shrubland, and between the dry sclerophyllous forest and the xerophytic shrubland. (<b>b</b>) pH. No significant differences between locations were observed. (<b>c</b>) Dry density (g/cm<sup>3</sup>). Significant differences were observed between the hygrophilous forest and the xerophytic shrubland (<b>d</b>) d15N and d13C isotope content.</p>
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<p>Redundancy analysis (RDA) that shows the variability of the environmental conditions associated with the plant communities. (For further information, see <a href="#app1-microorganisms-12-01569" class="html-app">Supplementary Table S3</a>.)</p>
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<p>(<b>a</b>) Prokaryote and (<b>b</b>) fungi soil microorganism’s alpha diversity in PNLC plant communities. Grey dots correspond to replicate outliers.</p>
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<p>PCoA (PERMANOVA <span class="html-italic">p</span>-value &lt; 0.05) that illustrates the microbial variability of the soil of the plant communities at the phylum level of (<b>a</b>) prokaryotes and (<b>b</b>) fungi. The blue arrows represent the environmental parameters that significantly correlate with the microbial communities (envfit, <span class="html-italic">p</span>-value &lt; 0.05).</p>
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<p>PCoA (PERMANOVA <span class="html-italic">p</span>-value &lt; 0.05) that illustrates the microbial variability of the soil of the plant communities at the phylum level of (<b>a</b>) prokaryotes and (<b>b</b>) fungi. The blue arrows represent the environmental parameters that significantly correlate with the microbial communities (envfit, <span class="html-italic">p</span>-value &lt; 0.05).</p>
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<p>Venn diagram that shows the comparison of ASVs richness associated with (<b>a</b>) prokaryotes and (<b>b</b>) fungi, grouped on the basis of the soil of the plant communities. Sclerophilous forest—purple area, Xerophytic shrubland—yellow area, Hygrophilous forest—green area, Dry sclerophilous forest—orange area.</p>
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<p>The box plot shows the relative abundance of bacterial and archaeal phyla in the PNLC soil categorized by the different plant communities.</p>
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<p>The box plots show the relative abundance of the fungal phyla in the PNLC soil categorized by different plant communities.</p>
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<p>Heatmap of the 18 most abundant genera of prokaryotes in the soil (<span class="html-italic">n</span> = 16) analyzed in PNLC, categorized by plant communities.</p>
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<p>Heatmap for the 20 genera of fungi in the soil (<span class="html-italic">n</span> = 16) analyzed in PNLC categorized by plant communities.</p>
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<p>Volcano plot that shows the ASVs of microorganisms (bacteria, archaea and fungi) when comparing the xerophytic shrubland vs. sclerophyllous forest plant communities in PNLC. The red line indicates the significantly different ASVs (<span class="html-italic">p</span>-adj). Complete data in <a href="#app1-microorganisms-12-01569" class="html-app">Supplementary Table S4</a>.</p>
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<p>Volcano plot that shows the ASVs of microorganism when comparing the hygrophilous forest and dry sclerophyllous forest plant communities in PNLC. The red line indicates the significantly different ASVs (<span class="html-italic">p</span>-adj). Complete data in <a href="#app1-microorganisms-12-01569" class="html-app">Supplementary Table S5</a>.</p>
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<p>Predicted potential microbial function profile by comparison of the 4 study sites. (<b>a</b>) Prokaryotes, (<b>b</b>) fungi.</p>
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15 pages, 5588 KiB  
Article
Multi-Omics Analysis Reveals the Regulatory Mechanism of Different Probiotics on Growth Performance and Intestinal Health of Salmo trutta (S. trutta)
by Mengjuan Chen, Zhitong Wang, Hui He, Wenjia He, Zihao Zhang, Shuaijie Sun and Wanliang Wang
Microorganisms 2024, 12(7), 1410; https://doi.org/10.3390/microorganisms12071410 - 12 Jul 2024
Viewed by 626
Abstract
Probiotics play an important role in animal production, providing health benefits to the host by improving intestinal microbial balance. In this study, we added three different probiotics, Saccharomyces cerevisiae (SC), Bacillus licheniformis (BL), and lactic acid bacteria (LAB), and compared them with the [...] Read more.
Probiotics play an important role in animal production, providing health benefits to the host by improving intestinal microbial balance. In this study, we added three different probiotics, Saccharomyces cerevisiae (SC), Bacillus licheniformis (BL), and lactic acid bacteria (LAB), and compared them with the control group (CON), to investigate the effects of probiotic supplementation on growth performance, gut microbiology, and gut flora of S. trutta. Our results showed that feeding probiotics improved the survival, growth, development, and fattening of S. trutta. Additionally, probiotic treatment causes changes in the gut probiotic community, and the gut flora microorganisms that cause significant changes vary among the probiotic treatments. However, in all three groups, the abundance of Pseudomonas, Acinetobacter, and Rhizophagus bacterial genera was similar to that in the top three comparative controls. Furthermore, differences in the composition of intestinal microbiota among feed types were directly associated with significant changes in the metabolomic landscape, including lipids and lipid-like molecules, organic acids and derivatives, and organoheterocyclic compounds. The probiotic treatment altered the gut microbiome, gut metabolome, and growth performance of S. trutta. Using a multi-omics approach, we discovered that the addition of probiotics altered the composition of gut microbiota, potentially leading to modifications in gut function and host phenotype. Overall, our results highlight the importance of probiotics as a key factor in animal health and productivity, enabling us to better evaluate the functional potential of probiotics. Full article
(This article belongs to the Section Veterinary Microbiology)
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<p>Map of macrogenomic analysis of the gut of <span class="html-italic">S. trutta</span> between control and probiotic groups. (<b>A</b>) Gut bacterial alpha diversity, including Shannon index, ACE index, Simpson index, and Chao1 index. * <span class="html-italic">p</span> &lt; 0.01, ** <span class="html-italic">p</span>  &lt; 0.01. (<b>B</b>) NMDS based on Bray–Curtis distance. (<b>C</b>) PCoA based on Bray–Curtis distance. (<b>D</b>) Differences in microbial community composition at the genus level. (<b>E</b>) Differences in microbial community composition at the species level. (<b>F</b>) The top 30 most important bacterial communities identified by random forest classification in control and probiotic groups. BL: <span class="html-italic">Bacillus licheniformis</span>, SC: <span class="html-italic">Saccharomyces cerevisiae</span>, LAB: <span class="html-italic">lactic acid bacteria</span>.</p>
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<p>Functional enrichment analysis of differential metabolic flora. (<b>A</b>) Histogram of functional enrichment analysis of differential flora in CON and BL groups. (<b>B</b>) Histogram of functional enrichment analysis of differential flora in CON and SC groups. (<b>C</b>) Histogram of functional enrichment analysis of differential flora in CON and LAB groups. BL: <span class="html-italic">Bacillus licheniformis</span>, SC: <span class="html-italic">Saccharomyces cerevisiae</span>, LAB: <span class="html-italic">lactic acid bacteria</span>.</p>
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<p>Quality control of the metabolomics data. (<b>A</b>) Principal coordinates analysis (PCoA) plots between the control and probiotics groups. (<b>B</b>) Orthogonal partial least squares discriminant analysis (OPLS-DA) score plot between CON group and SC group. (<b>C</b>) Orthogonal partial least squares discriminant analysis (OPLS-DA) score plot between CON group and BL group. (<b>D</b>) Orthogonal partial least squares discriminant analysis (OPLS-DA) score plot between CON group and LAB group. The blue and red dots represent the R<sub>2</sub>Y and Q<sub>2</sub>Y of the post-replacement model, respectively, and the two dashed lines are regression lines fitted to R<sub>2</sub>Y and Q<sub>2</sub>Y. BL: <span class="html-italic">Bacillus licheniformis</span>, SC: <span class="html-italic">Saccharomyces cerevisiae</span>, LAB: <span class="html-italic">lactic acid bacteria</span>.</p>
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<p>Different metabolites and metabolic pathways between the control group and the probiotics-treated group. Differential metabolite volcano plots of the BL (<b>A</b>), SC (<b>C</b>), and LAB (<b>E</b>) groups, respectively, versus the CON group. Each point represents a metabolite, the points highlighted in blue are downgrade metabolites while those in red are upgrade metabolites, and the size of the point represents the variable importance in projection. Differential metabolic pathways of the BL (<b>B</b>), SC (<b>D</b>), and LAB (<b>F</b>) groups, respectively, versus the CON group. The length of the line segment indicates the absolute value of DA score, and the size of the dots at the endpoints of the line segment indicates the number of differential metabolites in the pathway. The color of the line segments and dots reflects the size of the <span class="html-italic">p</span>-value, with a more red color indicating a smaller <span class="html-italic">p</span>-value and a more blue color indicating a larger <span class="html-italic">p</span>-value. BL: <span class="html-italic">Bacillus licheniformis</span>, SC: <span class="html-italic">Saccharomyces cerevisiae</span>, LAB: <span class="html-italic">lactic acid bacteria</span>.</p>
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<p>Spearman’s correlation analysis of microbiome and metabolism. Correlation analyses were performed between the control group and the BL group (<b>A</b>), SC group (<b>B</b>) and LAB group (<b>C</b>), respectively. Bacteria were deduced from the top 20 genera, which accounted for more than 80% of the microbial sequence reads, and metabolites were deduced from the different metabolic pathways by stages. The color represents the correlation coefficient. Only correlation coefficient beyond 0.6 and <span class="html-italic">p</span>-value below 0.05 were considered significant; in figures, ***, ** and * denote, respectively, <span class="html-italic">p</span>-values below 0.001, 0.01 and 0.05. BL: <span class="html-italic">Bacillus licheniformis</span>, SC: <span class="html-italic">Saccharomyces cerevisiae</span>, LAB: <span class="html-italic">lactic acid bacteria</span>.</p>
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16 pages, 1854 KiB  
Article
Effects of Ginger Straw Silage with Enzymes on Growth Performance, Digestion and Metabolism, Meat Quality and Rumen Microflora Diversity of Laiwu Black Goat
by Shuyue Pan, Di Wang, Yingting Lin, Ming Cheng, Fenghua Zhu and Yixuan Guo
Animals 2024, 14(14), 2040; https://doi.org/10.3390/ani14142040 - 12 Jul 2024
Viewed by 654
Abstract
Laiwu black goats comprise an excellent local germplasm resource; however, a shortage of feed resources has led to the application of unconventional feed. Ginger straw feed has good physiological effects, but research on this feed source for ruminant animals is lacking. The aim [...] Read more.
Laiwu black goats comprise an excellent local germplasm resource; however, a shortage of feed resources has led to the application of unconventional feed. Ginger straw feed has good physiological effects, but research on this feed source for ruminant animals is lacking. The aim of this study was to determine the effects of enzymatic silage ginger straw on Laiwu black goat performance. The experiment used an independent sample t-test analysis method; 24 healthy Laiwu black goats with a body weight of 20.05 ± 1.15 kg and age of 5.67 ± 0.25 months were randomly divided into two groups with three replicates (bars) per group and four goats per replicate. The experimental diet was composed of mixed concentrate, silage, and garlic peel at a 2:7:1 ratio. The silage used in the two groups was whole corn silage (CON group) and 60% whole corn silage plus 40% enzymatic silage ginger straw (SG group), and the other components were identical. Daily feed intake/daily gain (F/G) was significantly higher in the SG group than in the CON group (p < 0.05), but there were no significant differences in dry matter (DM), crude protein (CP), neutral detergent fiber (NDF), and acid detergent fiber (ADF) digestibility between the groups. The shear force, cooking loss, centrifugal loss, and pressure loss of the longissimus dorsi muscle group were significantly lower in the SG than in the CON group (p < 0.05). Compared with those in the CON group, the serum and liver total antioxidant capacity was significantly increased in the SG group, and in the liver, the O2·, malondialdehyde, and OH· contents were significantly decreased. Collectively, the rumen fluid microbial diversity was changed in the SG group. It was concluded that enzymatic silage ginger straw usage instead of 40% whole silage corn as feed for Laiwu black goats can significantly improve the muscle quality, antioxidant capacity, and intestinal flora, with no adverse effects on production performance. In conclusion, our study provides a basis for ginger straw processing and storage and its rational application in the Laiwu black goat diet. Full article
(This article belongs to the Section Animal Nutrition)
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<p>Description of microbial composition and changes in rumen fluid. The silage used for the two groups was whole corn silage (CON group) and 60% whole corn silage plus 40% enzymatic silage ginger straw (SG group). (<b>A</b>) Circos plot of operational taxonomic units (OTUs) in the rumen fluid samples. (<b>B</b>) Alpha diversity of microbiota in the rumen fluid of black goats after different treatments. The horizontal bars in the box represent the average value. The top and bottom of the box represent the upper and lower quartiles, respectively. Partial Least Squares Discriminant Analysis (PCA) picture (<b>C</b>) and Partial Least Squares Discriminant Analysis (PLS-DA) picture (<b>D</b>). Histogram of the species composition at phylum (<b>E</b>) and genus (<b>F</b>) levels. Different colors denote different phyla and genera.</p>
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<p>Relative abundances of rumen microorganisms, ranked among the top 9 species at the phylum level (<b>A</b>) and the top 16 species at the genus level (<b>B</b>). The silage used for the two groups was whole corn silage (CON group) and 60% whole corn silage plus 40% enzymatic silage ginger straw (SG group). The horizontal bars in the box represent average values. The top and bottom of the box represent the upper and lower quartiles, respectively. * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Relative abundances of functional taxa at the primary (<b>A</b>), secondary (<b>B</b>), and tertiary (<b>C</b>) levels. The silage used for the two groups was whole corn silage (CON group) and 60% whole corn silage plus 40% enzymatic silage ginger straw (SG group).</p>
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17 pages, 4271 KiB  
Article
Effects of Transglutaminase-Induced β-Conglycinin Gels on Intestinal Morphology and Intestinal Flora in Mice at Different High-Intensity Ultrasound Pretreatment Time
by Jixin Zhang, Lan Zhang, Huiqing Xu and Jun Wang
Foods 2024, 13(14), 2192; https://doi.org/10.3390/foods13142192 - 11 Jul 2024
Viewed by 610
Abstract
TGase-7S gels prepared after different HIU pretreatment times were used to intervene in healthy mice to analyze their effects on growth characteristics and intestinal morphology, and 16S rRNA high-throughput sequencing was applied to fecal samples to investigate the effects of the gel on [...] Read more.
TGase-7S gels prepared after different HIU pretreatment times were used to intervene in healthy mice to analyze their effects on growth characteristics and intestinal morphology, and 16S rRNA high-throughput sequencing was applied to fecal samples to investigate the effects of the gel on the structure and diversity of intestinal flora in mice. The results showed that the intestinal tissues of mice in different treatment groups showed better integrity, and the intake of gel increased the length of small intestinal villi in mice, among which the 30-gel group had the highest value of villi length (599.27 ± 44.28) μm (p < 0.05) and showed the neatest and tightest arrangement, indicating that the intake of gel did not have adverse effects on the intestinal tract. The effect of gel ingestion on the diversity of the intestinal microbial community structure was more significant, positively promoting the growth of beneficial bacteria such as Desferriobacterium, Synechococcus, and Bifidobacterium. In addition, the ingestion of the gel improved the intestinal health of mice by altering the physiological functions of the intestinal flora and modulating their participation in various metabolic pathways. The above findings provide some theoretical value for the safety of 7S gel in food applications. Full article
(This article belongs to the Section Food Nutrition)
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<p>Analysis of morphological changes in the small intestine of mice. (<b>A</b>) NC, normal control group; (<b>B</b>) 0-gel, HIU pretreated 0 min gel group; (<b>C</b>) 15-gel, HIU pretreated 15 min gel group; (<b>D</b>) 30-gel, HIU pretreated 30 min gel group; (<b>E</b>) 45-gel, HIU pretreated 45 min gel group; (<b>F</b>) 60-gel, HIU pretreated 60 min gel group.</p>
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<p>Quality identification of mice intestinal microbiota samples. (<b>a</b>) Sob dilution curves; (<b>b</b>) Rank–Abundance curves. A, NC group; B, 0-gel group; C, 15-gel group; D, 30-gel group; E, 45-gel group; F, 60-gel group.</p>
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<p>Intergroup analysis of PCoA based on Weighted Unifrac distance (<b>a</b>) and distribution dispersion on the PC1 axis (<b>b</b>). A, NC group; B, 0-gel group; C, 15-gel group; D, 30-gel group; E, 45-gel group; F, 60-gel group.</p>
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<p>Intergroup analysis of PCoA based on Unweighted Unifrac distance (<b>a</b>) and distribution dispersion on the PC1 axis (<b>b</b>). A, NC group; B, 0-gel group; C, 15-gel group; D, 30-gel group; E, 45-gel group; F, 60-gel group.</p>
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<p>Venn diagram of OTU. A, NC group; B, 0-gel group; C, 15-gel group; D, 30-gel group; E, 45-gel group; F, 60-gel group.</p>
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<p>Histogram of community structure distribution of mice gut microbiota at phylum (<b>a</b>), class (<b>b</b>), order (<b>c</b>), family (<b>d</b>), genus (<b>e</b>), and species (<b>f</b>) levels. A, NC group; B, 0-gel group; C, 15-gel group; D, 30-gel group; E, 45-gel group; F, 60-gel group.</p>
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<p>Functional relative abundance bar chart based on COG analysis. A, NC group; B, 0-gel group; C, 15-gel group; D, 30-gel group; E, 45-gel group; F, 60-gel group.</p>
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<p>Functional relative abundance bar chart based on first level annotation of KEGG analysis. M: Metabolism; GIP: Genetic Information Processing; EIP: Environmental Information Processing; HD: Human Diseases; CP: Cellular Processes; OS: Organismal Systems. A, NC group; B, 0-gel group; C, 15-gel group; D, 30-gel group; E, 45-gel group; F, 60-gel group.</p>
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<p>KEGG heat map. A, NC group; B, 0-gel group; C, 15-gel group; D, 30-gel group; E, 45-gel group; F, 60-gel group.</p>
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12 pages, 1501 KiB  
Article
Bioremediation of Cd-Contaminated Soil around Bauxite with Stimulants and Microorganisms
by Luxuan Feng, Xiaofeng Chen, Jinghua Yao, Lei Xiao, Xiujuan Feng and Shengmin Wu
Water 2024, 16(13), 1910; https://doi.org/10.3390/w16131910 - 4 Jul 2024
Viewed by 672
Abstract
Heavy metal pollution in the soil around bauxite mines, especially cadmium pollution, is becoming more and more severe due to this mining becoming more frequent. Therefore, it is urgent to develop green and safe remediation technology. Biostimulants have been studied extensively, but their [...] Read more.
Heavy metal pollution in the soil around bauxite mines, especially cadmium pollution, is becoming more and more severe due to this mining becoming more frequent. Therefore, it is urgent to develop green and safe remediation technology. Biostimulants have been studied extensively, but their practical application is still challenging. In this study, the effects of humic acid (HA), glucose (GLU), and tetrasodium glutamate diacetate (GLDA), as well as their synergistic complex bacterial flora, on Cd-contaminated soil were analyzed. It has been shown that applying these three types of stimulants, individually or with complex bacterial flora, can enhance soil environment and quality. Nevertheless, the remediation efficacy of stimulants in combination with microbial communities surpasses that achieved through the use of stimulants alone. Among them, 1%GLU combined with complex bacterial flora had the best passivation effect on Cd, reducing the available Cd by 25%, followed by 0.5% GLU combined with complex bacterial flora and 0.5%HA combined with complex bacterial flora, which reduced the available Cd by 21.92% and 19.17%, respectively. The synergistic remediation method using stimulants and microorganisms can reduce the harm caused to the environment by conventional remediation methods and improve the effectiveness of soil remediation. It has broad application prospects in the field of bauxite-contaminated soil remediation. Full article
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<p>Changes in soil pH (<b>a</b>), electrical conductivity (<b>b</b>), and organic matter (<b>c</b>) with single stimulating agent treatment.</p>
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<p>The chemical partitioning of Cd in soil with single stimulant treatment.</p>
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<p>Changes in soil urease (<b>a</b>), sucrase (<b>b</b>), and catalase (<b>c</b>) activities with single stimulating agent treatment.</p>
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<p>Changes in the number of bacteria (<b>a</b>) and fungi (<b>b</b>) in soil with single stimulator treatment.</p>
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<p>Changes in soil pH (<b>a</b>), electrical conductivity (<b>b</b>), and organic matter (<b>c</b>) after treatment with different stimulating agents and complex bacteria (the dashed line represents CK) (The difference is not significant if the same marked letter, and the difference is significant if the different marked letter).</p>
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<p>The chemical partitioning of Cd in soil after treatment with different stimulating agents and complex bacteria.</p>
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<p>Changes in soil urease (<b>a</b>), sucrase (<b>b</b>), and catalase (<b>c</b>) activities after treatment with different stimulating agents and complex bacteria (the dashed line represents CK) (The difference is not significant if the same marked letter, and the difference is significant if the different marked letter).</p>
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22 pages, 3815 KiB  
Article
Effects of Salinity on Physicochemical Properties, Flavor Compounds, and Bacterial Communities in Broad Bean Paste-Meju Fermentation
by Qingyan Guo, Jiabao Peng, Jingjing Zhao, Jie Lei, Yukun Huang and Bing Shao
Foods 2024, 13(13), 2108; https://doi.org/10.3390/foods13132108 - 2 Jul 2024
Viewed by 841
Abstract
Broad bean paste (BBP) is a traditional fermented soy food, and its high salt content not only prolongs the fermentation time but also threatens human health. In this study, three BBP-meju with different salt concentrations were prepared, and the effects of varying salinity [...] Read more.
Broad bean paste (BBP) is a traditional fermented soy food, and its high salt content not only prolongs the fermentation time but also threatens human health. In this study, three BBP-meju with different salt concentrations were prepared, and the effects of varying salinity on fermentation were comprehensively compared. The results showed that salt-reduced fermentation contributed to the accumulation of amino acid nitrogen, reducing sugars, free amino acids, and organic acids. Alcohols, esters, aldehydes, and acids were the main volatile flavor compounds in BBP-meju, and the highest total volatile flavor compounds were found in medium-salt meju. Bacillus, Staphylococcus, Aspergillus, and Mortierella were the dominant microbial communities during fermentation, and there were also three opportunistic pathogens, Enterobacter, Pantoea, and Brevundimonas, respectively. According to Spearman correlation analysis, Wickerhamomyces, Bacillus, Staphylococcus, and Mortierella all showed highly significant positive correlations with ≥3 key flavor compounds, which may be the core functional flora. Furthermore, the dominant microbial genera worked synergistically to promote the formation of high-quality flavor compounds and inhibit the production of off-flavors during salt-reduced fermentation. This study provides a theoretical reference for the quality and safety control of low-salt fermented soy foods. Full article
(This article belongs to the Section Food Physics and (Bio)Chemistry)
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Graphical abstract

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<p>Physicochemical properties during fermentation of BBP-meju with different salt concentrations: (<b>a</b>) moisture contents; (<b>b</b>) pH; (<b>c</b>) salinity; (<b>d</b>) total acid contents; (<b>e</b>) AAN contents; (<b>f</b>) reducing sugar contents.</p>
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<p>FAAs during fermentation of BBP-meju with different salt concentrations: (<b>a</b>) content of amino acids; (<b>b</b>) relative abundance of amino acids; (<b>c</b>) average content of amino acids in BBP-meju samples. Asterisks indicate a statistically significant difference from the control group (* <span class="html-italic">p</span> ≤ 0.05, ** <span class="html-italic">p</span> ≤ 0.01).</p>
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<p>OAs during fermentation of BBP-meju with different salt concentrations: (<b>a</b>) content of OAs; (<b>b</b>) relative abundance of OAs; (<b>c</b>) average content of OAs in BBP-meju samples with different salt concentrations. Where letters a, b, and c indicated the significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The volatile compounds of BBP-meju with different salt concentrations based on E-nose data: (<b>a</b>) PCA scores and (<b>b</b>) radar chart.</p>
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<p>Content distribution of volatile flavor compounds during fermentation of BBP-meju with different salt concentrations: (<b>A</b>) relative abundance of different types of volatile flavor compounds; (<b>B</b>) content of different types of volatile flavor compounds; (<b>C</b>) variation in the content of different types of volatile flavor compounds across samples.</p>
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<p>The α-diversity of bacterial (<b>a</b>–<b>e</b>) and fungal (<b>f</b>–<b>j</b>) communities during fermentation of BBP-meju with different salt concentrations.</p>
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<p>Structural analysis of bacterial and fungal communities in BBP-meju samples: (<b>a</b>,<b>b</b>) comparison of OTUs of bacteria (<b>a</b>) and fungi (<b>b</b>); (<b>c</b>,<b>d</b>) relative abundance of bacteria (<b>c</b>) and fungi (<b>d</b>) at the genus level.</p>
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<p>Correlation clustering heat map (<b>a</b>) and correlation network diagram (<b>b</b>) between dominant microbial genera and key volatiles flavor compounds (VIP &gt; 1) during BBP-meju fermentation with different salt concentrations. (<b>a</b>) The red and blue indicate positive and negative correlation, respectively, and the darker the color, the stronger the correlation; asterisks represent significance, * is <span class="html-italic">p</span> &lt; 0.05, and ** is <span class="html-italic">p</span> &lt; 0.01. (<b>b</b>): blue squares represent key volatile flavor compounds, red circles represent dominant microbial genera, and the yellow solid line and gray dotted line represent positive and negative correlation, respectively, and the thicker the lines, the stronger the correlation.</p>
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15 pages, 4051 KiB  
Article
Effect of Trace Element Selenium on the Intestinal Microbial Community in Nude Mice with Colorectal Cancer
by Yintong Su, Xiaohua Cai, Xingxing Fan, Jiayu Ning and Mei Shen
Microorganisms 2024, 12(7), 1336; https://doi.org/10.3390/microorganisms12071336 - 29 Jun 2024
Viewed by 818
Abstract
Colorectal cancer (CRC) is the third most common cancer worldwide. The role of intestinal microbiota in carcinogenesis has also become an important research topic, and CRC is closely related to the intestinal microbiota. Selenium-containing compounds have attracted more attention as anticancer drugs as [...] Read more.
Colorectal cancer (CRC) is the third most common cancer worldwide. The role of intestinal microbiota in carcinogenesis has also become an important research topic, and CRC is closely related to the intestinal microbiota. Selenium-containing compounds have attracted more attention as anticancer drugs as they can have minimal side effects. The purpose of this study was to determine and compare the effect of sodium selenite and selenomethionine on the microbial communities of nude mice with CRC. A CRC ectopic tumorigenesis model was established by subcutaneously injecting HCT116 cells into nude mice. The mice were then intraperitoneally injected with sodium selenite and selenomethionine for 24 days to regulate their intestinal microbiota. Compared with sodium selenite, selenomethionine resulted in a greater reduction in the richness and diversity of intestinal microbiota in nude mice with CRC, and the richness and diversity were closer to healthy levels. Selenomethionine also regulated a wider variety of flora. Additionally, sodium selenite and selenomethionine produced different microorganisms, changed function and metabolic pathways in the intestinal microbiota. Both sodium selenite and selenomethionine have certain effects on restoring the intestinal microbial diversity in nude mice with CRC, and the effect of selenomethionine is better than that of sodium selenite. Full article
(This article belongs to the Section Medical Microbiology)
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<p>Chemical structures of sodium selenite and selenomethionine.</p>
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<p>Schematic diagram of the experimental design.</p>
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<p>Inhibitory effects of sodium selenite and selenomethionine on nude mice with CRC. (<b>A</b>) Trend chart of body weight in each group of mice. (<b>B</b>) Tumor weight of mice in Control group, SSe group and SeMet group. (<b>C</b>) H&amp;E staining results of tumor tissues (×200) and the expression of Ki67 in tumor tissues analyzed by IHC staining (×400) of mice in Control group, SSe group and SeMet group. (* indicating <span class="html-italic">p</span> &lt; 0.05, ** indicating <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Alpha diversity analysis. (<b>A</b>) Rarefaction curves were constructed using Sobs indices. (<b>B</b>) Rank–abundance curves on OTU level. (<b>C</b>) Measures of richness using Sobs index. (<b>D</b>) Measures of diversity using Shannon index. (* indicating <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Partial least squares discriminant analysis (PLS-DA) analysis on the OTU level.</p>
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<p>The effect of sodium selenite and selenomethionine on intestinal microbiota composition. (<b>A</b>) Relative abundance at the phylum level in fecal microbiota of each group. (<b>B</b>) Relative abundance at the genus level in fecal microbiota of each group. (<b>C</b>) Difference analysis between Blank group and Control group. (<b>D</b>) Difference analysis between Control group and SSe group. (<b>E</b>) Difference analysis between Control group and SeMet group. (* indicating <span class="html-italic">p</span> &lt; 0.05, ** indicating <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Cladogram of significant difference between groups. (<b>A</b>) Cladogram constructed using the linear discriminant analysis effect size (LefSe) method to indicate the phylogenetic distribution of bacteria that were remarkably enriched between each group. Yellow circles indicate species with no significant differences. (<b>B</b>) Linear discriminant analysis (LDA) scores represent the gut bacteria which were of important biological significance in each group.</p>
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<p>Bar chart of the Clusters of Orthologous Genes (COG) function classification in (<b>A</b>) Blank group, (<b>B</b>) Control group, (<b>C</b>) SSe group and (<b>D</b>) SeMet group. (<b>E</b>) Bar chart and (<b>F</b>) box plot of COG functions with differences in each group. N: cell motility; I: lipid transport and metabolism; K: transcription; J: translation, ribosomal structure and biogenesis. (* indicating <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Functional analysis of (<b>A</b>) enzyme and (<b>B</b>) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway level3 based on KEGG database.</p>
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