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11 pages, 2448 KiB  
Article
Validation of Recombinase Polymerase Amplification with In-House Lateral Flow Assay for mcr-1 Gene Detection of Colistin Resistant Escherichia coli Isolates
by Naeem Ullah, Nutchaba Suchanta, Umaporn Pimpitak, Pitak Santanirand, Nutthee Am-In and Nuntaree Chaichanawongsaroj
Antibiotics 2024, 13(10), 984; https://doi.org/10.3390/antibiotics13100984 - 17 Oct 2024
Abstract
Background/Objectives: The emergence of the mobilized colistin resistance 1 (mcr-1) gene, which causes colistin resistance, is a serious concern in animal husbandry, particularly in pigs. Although antibiotic regulations in many countries have prohibited the use of colistin in livestock, the persistence [...] Read more.
Background/Objectives: The emergence of the mobilized colistin resistance 1 (mcr-1) gene, which causes colistin resistance, is a serious concern in animal husbandry, particularly in pigs. Although antibiotic regulations in many countries have prohibited the use of colistin in livestock, the persistence and dissemination of this plasmid-mediated gene require effective and rapid monitoring. Therefore, a rapid, sensitive, and specific method combining recombinase polymerase amplification (RPA) with an in-house lateral flow assay (LFA) for the mcr-1 gene detection was developed. Methods: The colistin agar test and broth microdilution were employed to screen 152 E. coli isolates from pig fecal samples of five antibiotic-used farms. The established RPA-in-house LFA was validated with PCR for mcr-1 gene detection. Results: The RPA-in-house LFA was completed within 35 min (20 min of amplification and 5–15 min on LFA detection) at 37 °C. The sensitivity, specificity, and accuracy were entirely 100% in concordance with PCR results. No cross-reactivity was detected with seven common pathogenic bacteria or other mcr gene variants. Conclusions: Therefore, the in-house RPA-LFA serves as a point-of-care testing tool that is rapid, simple, and portable, facilitating effective surveillance of colistin resistance in both veterinary and clinical settings, thereby enhancing health outcomes. Full article
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Figure 1

Figure 1
<p>Phenotypic and genotypic resistance analysis in <span class="html-italic">E. coli</span> isolates from pigs, analysis by (<b>A</b>) colistin agar test, and (<b>B</b>) broth microdilution method.</p>
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<p>In-house LFA optimization (<b>A</b>) anti−biotin conjugated AuNPs concentrations, (<b>B</b>) anti−IgG antibody concentrations, (<b>C</b>) anti−FITC concentrations, and (<b>D</b>) % tween−20 in running buffer.</p>
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<p>Optimization of RPA and LFA for <span class="html-italic">mcr</span>-<span class="html-italic">1</span> detection on AGE and LFA, respectively. Primer concentrations (<b>A</b>,<b>B</b>); temperature (<b>C</b>,<b>D</b>); and incubation time (<b>E</b>,<b>F</b>); RPA product: buffer ratio at 1:4–1:20 (<b>G</b>) and detection time on LFA (<b>H</b>). [M, 100 bp Marker; C−, no template control; C+, positive DNA control].</p>
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<p>LOD and cross-reactivity for <span class="html-italic">mcr</span>-<span class="html-italic">1</span> detection by RPA-LFA. AGE results at different DNA template concentrations (0.1–200 ng) (<b>A</b>) and corresponding LFA (<b>B</b>). AGE results with 7 bacterial strains (<b>C</b>), <span class="html-italic">mcr-1</span> to <span class="html-italic">mcr-10</span> (<b>E</b>) and corresponded with LFA (<b>D</b>,<b>F</b>) detection. [M, 100 bp Marker; C−, no template control; C+, positive].</p>
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15 pages, 3279 KiB  
Article
Genomic Analysis of Aeromonas salmonicida ssp. salmonicida Isolates Collected During Multiple Clinical Outbreaks Supports Association with a Single Epidemiological Unit
by Konrad Wojnarowski, Paulina Cholewińska, Peter Steinbauer, Tobias Lautwein, Wanvisa Hussein, Lisa-Marie Streb and Dušan Palić
Pathogens 2024, 13(10), 908; https://doi.org/10.3390/pathogens13100908 - 17 Oct 2024
Abstract
Outbreaks of furunculosis cause significant losses in salmonid aquaculture worldwide. With a recent rise in antimicrobial resistance, regulatory measures to minimize the use of antibiotics in animal husbandry, including aquaculture, have increased scrutiny and availability of veterinary medical products to control this disease [...] Read more.
Outbreaks of furunculosis cause significant losses in salmonid aquaculture worldwide. With a recent rise in antimicrobial resistance, regulatory measures to minimize the use of antibiotics in animal husbandry, including aquaculture, have increased scrutiny and availability of veterinary medical products to control this disease in production facilities. In such a regulatory environment, the utility of autogenous vaccines to assist with disease prevention and control as a veterinary-guided prophylactic measure is of high interest to the producers and veterinary services alike. However, evolving concepts of epidemiological units and epidemiological links need to be considered during approval and acceptance procedures for the application of autogenous vaccines in multiple aquaculture facilities. Here, we present the results of solid-state nanopore sequencing (Oxford Nanopore Technologies, ONT) performed on 54 isolates of Aeromonas salmonicida ssp. salmonicida sampled during clinical outbreaks of furunculosis in different aquaculture facilities from Bavaria, Germany, from 2017 to 2020. All of the performed analyses (phylogeny, single nucleotide polymorphism and 3D protein modeling for major immunogenic proteins) support a high probability that all studied isolates belong to the same epidemiological unit. Simultaneously, we describe a cost/effective method of whole genome analysis with the usage of ONT as a viable strategy to study outbreaks of other pathogens in the field of aquatic veterinary medicine for the purpose of developing the best autogenous vaccine candidates applicable to multiple aquaculture establishments. Full article
(This article belongs to the Special Issue Molecular Epidemiology of Pathogenic Agents)
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<p>Map presenting watersheds where samplings were conducted in the timespan of 2017–2020.</p>
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<p>Phylogenetic tree of <span class="html-italic">Aeromonas salmonicida</span> ssp. <span class="html-italic">salmonicida</span> isolates. The scale bar indicates the evolutionary distance in substitutions per nucleotide. The tree was visualized using <a href="http://microreact.org" target="_blank">microreact.org</a>.</p>
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<p>Results of the SNP analysis representing differences in the number of single nucleotide polymorphisms in tested samples (other samples = 0 SNP difference). (Analysis was performed using the Galaxy web platform and SNP distance matrix 0.8.2 tool).</p>
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<p>Protein structure analysis between reference and selected sample (290-1) [(<b>A</b>)—ASA_2540; (<b>B</b>)—ASA_ 0509; (<b>C</b>)—ASA_1438; (<b>D</b>)—ASA_1267; (<b>E</b>)—OmpA; (<b>F</b>)—OmpC; (<b>G</b>)—OmpF; (<b>H</b>)—AscC; (<b>I</b>)—AexT; a—sequencing coverage; b—3D structure; 1—sample; 2—reference] (analysis was performed using Neurosnap platform and Alphafold 2 model).</p>
Full article ">Figure 4 Cont.
<p>Protein structure analysis between reference and selected sample (290-1) [(<b>A</b>)—ASA_2540; (<b>B</b>)—ASA_ 0509; (<b>C</b>)—ASA_1438; (<b>D</b>)—ASA_1267; (<b>E</b>)—OmpA; (<b>F</b>)—OmpC; (<b>G</b>)—OmpF; (<b>H</b>)—AscC; (<b>I</b>)—AexT; a—sequencing coverage; b—3D structure; 1—sample; 2—reference] (analysis was performed using Neurosnap platform and Alphafold 2 model).</p>
Full article ">Figure 4 Cont.
<p>Protein structure analysis between reference and selected sample (290-1) [(<b>A</b>)—ASA_2540; (<b>B</b>)—ASA_ 0509; (<b>C</b>)—ASA_1438; (<b>D</b>)—ASA_1267; (<b>E</b>)—OmpA; (<b>F</b>)—OmpC; (<b>G</b>)—OmpF; (<b>H</b>)—AscC; (<b>I</b>)—AexT; a—sequencing coverage; b—3D structure; 1—sample; 2—reference] (analysis was performed using Neurosnap platform and Alphafold 2 model).</p>
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26 pages, 8121 KiB  
Article
Mixed Th1/Th2/Th17 Responses Induced by Plant Oil Adjuvant-Based B. bronchiseptica Vaccine in Mice, with Mechanisms Unraveled by RNA-Seq, 16S rRNA and Metabolomics
by Xuemei Cui, Qiuju Xiang, Yee Huang, Quanan Ji, Zizhe Hu, Tuanyuan Shi, Guolian Bao and Yan Liu
Vaccines 2024, 12(10), 1182; https://doi.org/10.3390/vaccines12101182 - 17 Oct 2024
Abstract
Background/Objectives: The current Bordetella bronchiseptica (Bb) vaccine, when adjuvanted with alum, does not elicit adequate robust cellular immunity or effective antibody defense against Bb attacks. Unfortunately, antibiotic treatment generally represents an ineffective strategy due to the development of resistance against a broad range [...] Read more.
Background/Objectives: The current Bordetella bronchiseptica (Bb) vaccine, when adjuvanted with alum, does not elicit adequate robust cellular immunity or effective antibody defense against Bb attacks. Unfortunately, antibiotic treatment generally represents an ineffective strategy due to the development of resistance against a broad range of antibiotics. Methods: The present study was designed to investigate the immune response, protective capabilities and underlying mechanisms of a plant oil-based adjuvant E515 formulated with inactivated Bb antigen as a potential vaccine candidate against Bordetella bronchiseptica. Results: Immunization studies revealed that a combination of SO, VE and GS (E515) exhibited a good synergistic adjuvant effect. The E515 adjuvanted Bb vaccine was proven to be highly efficacious and induced a mixed Th1/Th2/Th17 immune response in mice, leading to a significant increase in Bb-specific IgG, IgG1 and IgG2a antibodies, proliferative lymphocyte responses and cytokine levels (by lymphocytes and serum) and effectively induced responses by CD4+ TE, TM cells and B cells. The E515 adjuvant significantly enhanced the immune protection provided by the Bb vaccine in a mice model, as indicated by a reduced bacterial burden in the lungs. Multi-omics sequencing analysis revealed that E515 functions as an adjuvant by modulating critical pathways, including cytokine–cytokine receptor interaction, the IL-17 signaling pathway and the chemokine signaling pathway. This modulation also included interactions with beneficial species of bacteria including Alistipes, Odoribacter and Colidextribacter, as well as energy and lipid-related metabolites, thus highlighting its role as an immunomodulatory agent. Conclusion: Collectively, our results demonstrate the huge potential of E515-Bb vaccine candidates, thus highlighting the vegetable oil original adjuvant E515 as a promising agent for the development of new veterinary vaccines. Full article
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Figure 1
<p>Experimental design. (<b>A</b>) Synergistic effects of E515 adjuvant on Bb vaccine. Mice (n = 6/group) were immunized intramuscularly (i.m.) on days 0 and 14. Blood samples were collected 3 and 7 days after boost to measure Bb-specific IgG. (<b>B</b>) Immune effects of the E515-Bb vaccine. Mice (n = 15/group) were i.m. Blood samples were collected 7, 14, 21 and 28 days after boosting to measure Bb-specific IgG, IgG1 and IgG2a. On day 14, spleens were collected to measure lymphocyte proliferation, relative mRNA expression, T/B cell differentiation and cytokine production (spleen and serum). On day 28, spleens and cecal contents were collected for transcriptomic, 16S rRNA sequencing and untargeted metabolomics analyses. (<b>C</b>) Protection effect of E515-Bb vaccine. Mice (n = 9/group) were immunized i.m. on day 0. On day 28 post-boosting, mice were challenged with live Bb (5.2 × 10<sup>9</sup> CFU/mice); seven days later lungs were collected for bacterial burdens quantified.</p>
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<p>Vaccine-induced humoral immune response (Mice, n = 6/group). (<b>A</b>) Blood samples were collected 3 and 7 days after boost to measure Bb-specific IgG. (<b>B</b>) Serum samples were collected 7, 14, 21 and 28 days after boosting to measure Bb-specific IgG. (<b>C</b>) IgG1 and IgG2a were measured 14 days after the boost. Data shown as mean ± SE. Bars with different letters are statistically different (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Vaccine-induced cellular immune response (Mice, n = 6/group). Splenocytes were isolated from spleens 14 days after the boost. (<b>A</b>) SI splenocytes were stimulated by Con A (10 µg/mL), LPS (10 µg/mL) or Bb antigen (20 µg/mL) for 48 h. (<b>B</b>) The expression of GATA-3, T-bet and ROR-γt mRNA splenocytes were stimulated by the Bb antigen (20 µg/mL) for 24 h. (<b>C</b>–<b>F</b>) The concentration of the IFN-γ, IL-6, TGF-β1 and IL-17 splenocytes were stimulated by the Bb antigen (20 µg/mL) for 72 h. Data shown as mean ± SE. Bars with different letters are statistically different (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Cytokine production in serum (Mice, n = 5/group). Serum samples were collected 14 days after the boost and detected by a cytokine array. (<b>A</b>) Heat map of hierarchical clustering of cytokines in Bb vs. alum. (<b>B</b>) Heat map of hierarchical clustering of cytokines in Bb vs. E515. (<b>C</b>) Heat map of hierarchical clustering of cytokines in alum vs. E515. (<b>D</b>) KEGG pathway enrichment of cytokines in Bb vs. alum. (<b>E</b>) KEGG pathway enrichment of cytokines in Bb vs. E515. (<b>F</b>) KEGG pathway enrichment of cytokines in alum vs. E515. Red color on the heatmap indicates cytokines are upregulated and blue indicates cytokines are downregulated. Dot size on the KEGG pathway represents the number of cytokines; the color represents the <span class="html-italic">p</span> value.</p>
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<p>Bb-specific T cell and B cell responses in the spleen (Mice, n = 5/group). Splenocytes were isolated from the spleens 14 days after the boost, and analyzed by flow cytometry. (<b>A</b>,<b>B</b>) Frequencies and quantification of the CD4<sup>+</sup>, CD8<sup>+</sup> T cells and CD4<sup>+</sup>/CD8<sup>+</sup> ratio. (<b>C</b>,<b>D</b>) Frequencies and quantification of the CD4<sup>+</sup> TM (CD44<sup>+</sup> CD62L<sup>+</sup>) and TE (CD44<sup>+</sup> CD62L<sup>−</sup>) cells. (<b>E</b>,<b>F</b>) Frequencies and quantification of the CD8<sup>+</sup> TM and TE cells. (<b>G</b>,<b>H</b>) Frequencies and quantification of the plasmablasts (CD19<sup>+</sup> CD138<sup>+</sup> CD38<sup>+</sup>). (<b>I</b>,<b>J</b>) Frequencies and quantification of the plasma cells (CD19<sup>−</sup> CD138<sup>+</sup> CD38<sup>−</sup>). (<b>K</b>,<b>L</b>) Frequencies and quantification of the GC B cells (CD19<sup>+</sup> Fas<sup>+</sup> CD38<sup>−</sup>). Data shown as mean ± SE. Bars with different letters are statistically different (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>KEGG pathways analysis (n = 3 /group). Comparison of DEGs KEGG modules between Bb-vs-alum (<b>A</b>), Bb-vs-E515 (<b>B</b>) and alum-vs-E515 (<b>C</b>).</p>
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<p>Effect of E515 on the diversity and composition of gut microbiota (n = 6/group). (<b>A</b>) Rank–abundance curve. (<b>B</b>,<b>G</b>–<b>I</b>) β-diversity was detected using PCoA (principal coordinates analysis) and ANOSIM (non-metric multi-dimensional scaling). (<b>C</b>) Observed OTUs (operational taxonomic units). (<b>D</b>–<b>F</b>) α-Diversity was detected using Chao1, Shannon and Simpson indices. (<b>J</b>–<b>L</b>) Composition of gut microbiota was analyzed at the phylum, family and genus levels.</p>
Full article ">Figure 8
<p>Differences in microbial abundances were identified by linear discriminant analysis (LDA, LDA score &gt; 2) and linear discriminant analysis effect size (LEfSe) analyses. (<b>A</b>) Bb-vs-E515. (<b>B</b>) alum-vs-E515.</p>
Full article ">Figure 9
<p>KEGG pathway analysis of differential microbes in the three groups.</p>
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<p>Effect of E515 on the changes of gut metabolic (n = 6 /group). (<b>A</b>) PLS-DA analysis score plots of the metabolic profiles in the negative ion mode. (<b>B</b>) PLS-DA analysis score plots of the metabolic profiles in the positive ion mode. PLS-DA and OPLS-DA analysis score plots of the metabolic profiles in the negative ion mode. Cross-validation results in the negative ion mode. (<b>C</b>) Differential expressed metabolites (DEMs) in every comparison group (VIP &gt; 1 and <span class="html-italic">p</span> &lt; 0.05). (<b>D</b>–<b>F</b>) KEGG pathway analysis of the differential metabolites in every comparison groups.</p>
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<p>Pearson’s correlation analysis among metabolites and microbes. (<b>A</b>) Bb-vs-E515. (<b>B</b>) Alum-vs-E515. Positive correlation marked with red color, negative correlation marked with green color, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001.</p>
Full article ">Figure 12
<p>Protective effect of E515-Bb vaccine on mice post-Bb challenge. Mice (n = 9/group) were s.c. injected with 0.2 mL of inactivated Bb antigen (3 × 10<sup>9</sup> CFU/mL) or Bb antigen adjuvanted with E515 or alum. Then, mice were challenged by an intraperitoneal injection of live Bb (5.2 × 10<sup>9</sup> CFU/mice). Seven days after attack, bacterial loads in lungs were quantified (n = 6). Data are presented as mean ± SE. Bars with different letters are statistically different (<span class="html-italic">p</span> &lt; 0.05).</p>
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17 pages, 5594 KiB  
Article
The Effects of swnH1 Gene Function of Endophytic Fungus Alternaria oxytropis OW 7.8 on Its Swainsonine Biosynthesis
by Dan Li, Xinlei Zhao, Ping Lu and Yu Min
Microorganisms 2024, 12(10), 2081; https://doi.org/10.3390/microorganisms12102081 - 17 Oct 2024
Abstract
The swnH1 gene in the endophytic fungus Alternaria oxytropis OW 7.8 isolated from Oxytropis glabra was identified, and the gene knockout mutant ΔswnH1 was first constructed in this study. Compared with A. oxytropis OW 7.8, the ΔswnH1 mutant exhibited altered colony [...] Read more.
The swnH1 gene in the endophytic fungus Alternaria oxytropis OW 7.8 isolated from Oxytropis glabra was identified, and the gene knockout mutant ΔswnH1 was first constructed in this study. Compared with A. oxytropis OW 7.8, the ΔswnH1 mutant exhibited altered colony and mycelium morphology, slower growth rate, and no swainsonine (SW) in mycelia, indicating that the function of the swnH1 gene promoted SW biosynthesis. Five differential expressed genes (DEGs) closely associated with SW synthesis were identified by transcriptomic analysis of A. oxytropis OW 7.8 and ΔswnH1, with sac, swnR, swnK, swnN, and swnH2 down-regulating. Six differential metabolites (DEMs) closely associated with SW synthesis were identified by metabolomic analysis, with P450, PKS-NRPS, saccharopine, lipopolysaccharide kinase, L-PA, α-aminoadipic, and L-stachydrine down-regulated, while L-proline was up-regulated. The SW biosynthetic pathways in A. oxytropis OW 7.8 were predicted and refined. The results lay the foundation for in-depth exploration of the molecular mechanisms and metabolic pathways of SW synthesis in fungi and provide reference for future control of SW in locoweeds, which would benefit the development of animal husbandry and the sustainable use of grassland ecosystems. Full article
(This article belongs to the Section Molecular Microbiology and Immunology)
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Figure 1
<p>SW biosynthetic pathways in two fungi. (<b>A</b>) <span class="html-italic">R. leguminicola</span> and (<b>B</b>) <span class="html-italic">M. robertsii</span> [<a href="#B27-microorganisms-12-02081" class="html-bibr">27</a>,<a href="#B38-microorganisms-12-02081" class="html-bibr">38</a>].</p>
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<p>Diagram of the <span class="html-italic">swnH1</span> gene knockout vector structure. Amp: Ampicillin; Ori: Origin of replication; <span class="html-italic">swnH1</span> up: the upstream homologous sequences of <span class="html-italic">swnH1</span>; <span class="html-italic">swnH1</span> down: the downstream homologous sequences of <span class="html-italic">swnH1</span>; <span class="html-italic">hpt</span>: hygromycin phosphotransferase gene; <span class="html-italic">lacZ</span>: <span class="html-italic">lacZ</span> gene.</p>
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<p>Identification figure for <span class="html-italic">swnH1</span> gene knockout transformants.</p>
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<p>Bioinformatics analysis of the <span class="html-italic">swnH1</span> gene. (<b>A</b>) Predicted SwnH1 protein structure; (<b>B</b>) the phylogenetic tree of the SwnH1 protein.</p>
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<p>Colonies of <span class="html-italic">A. oxytropis</span> OW 7.8 on PDA media containing different concentrations of Hyg B after 30 days of incubation. (<b>A</b>) 0 μg/mL. (<b>B</b>) 0.4 μg/mL. (<b>C</b>) 0.5 μg/mL. (<b>D</b>) 0.6 μg/mL. (<b>E</b>) 0.7 μg/mL. (<b>F</b>) 0.8 μg/mL. (<b>G</b>) 0.9 μg/mL. (<b>H</b>) 1.0 μg/mL. (<b>I</b>) 2.0 μg/mL. (<b>J</b>) 3.0 μg/mL.</p>
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<p>Electrophoresis analysis of PCR products of transformant DNA. Marker: 1 kb plus DNA ladder. (<b>A</b>) Lanes 1, 2, 3 show bands of the <span class="html-italic">hpt</span> gene, with the expected product being 1388 bp; W: negative control. (<b>B</b>) Lanes 1, 2, 3 show bands of the <span class="html-italic">hpt</span> gene + downstream homologous sequence of the <span class="html-italic">swnH1</span>, with the expected product being 1703 bp; W: negative control. (<b>C</b>) Lanes 1, 2 show bands of the upstream homologous sequence of <span class="html-italic">swnH1</span> + <span class="html-italic">hpt</span> gene, with the expected product being 1998 bp; W: negative control. (<b>D</b>) Lanes 1, 2, 3 show unamplified internal sequence of <span class="html-italic">swnH1</span>; W: positive control, with the expected product being 987 bp.</p>
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<p>Morphology of colonies and mycelia from <span class="html-italic">A. oxytropis</span> OW 7.8 and Δ<span class="html-italic">swnH1.</span> (<b>A</b>) <span class="html-italic">A. oxytropis</span> OW 7.8 colonies. (<b>B</b>) Δ<span class="html-italic">swnH1</span> colonies. (<b>C</b>) <span class="html-italic">A. oxytropis</span> OW 7.8 mycelia magnified 3000×. (<b>D</b>) Δ<span class="html-italic">swnH1</span> mycelia magnified 3000×.</p>
Full article ">Figure 8
<p>SW levels in mycelia from <span class="html-italic">A. oxytropis</span> OW 7.8 and Δ<span class="html-italic">swnH1</span>. Error bars represent the standard error of the mean (<span class="html-italic">n</span> = 3), with (****) <span class="html-italic">p</span> &lt; 0.0001.</p>
Full article ">Figure 9
<p>Transcriptome analysis between <span class="html-italic">A. oxytropis</span> OW 7.8 and Δ<span class="html-italic">swnH1</span>. (<b>A</b>) Volcano plot for differential comparison. (<b>B</b>) KEGG enrichment analysis and (<b>C</b>) GO functional classification annotation. BP: Biological Process; CC: Cellular Component; MF: Molecular Function.</p>
Full article ">Figure 10
<p>Metabolome analysis between <span class="html-italic">A. oxytropis</span> OW 7.8 and Δ<span class="html-italic">swnH1</span>. (<b>A</b>) Principal component analysis in positive ion mode. (<b>B</b>) Principal component analysis in negative ion mode. (<b>C</b>) Pie chart of metabolite classification in positive ion mode. (<b>D</b>) Pie chart of metabolite classification in negative ion mode. (<b>E</b>) differential metabolite volcano plot in positive ion mode. (<b>F</b>) differential metabolite volcano plot in negative ion mode. (<b>G</b>) Scatter plot of KEGG enrichment of differential metabolites in positive ion mode. (<b>H</b>) Scatter plot of KEGG enrichment of differential metabolites in negative ion mode. (<b>I</b>) KEGG enrichment analysis of DEMs.</p>
Full article ">Figure 10 Cont.
<p>Metabolome analysis between <span class="html-italic">A. oxytropis</span> OW 7.8 and Δ<span class="html-italic">swnH1</span>. (<b>A</b>) Principal component analysis in positive ion mode. (<b>B</b>) Principal component analysis in negative ion mode. (<b>C</b>) Pie chart of metabolite classification in positive ion mode. (<b>D</b>) Pie chart of metabolite classification in negative ion mode. (<b>E</b>) differential metabolite volcano plot in positive ion mode. (<b>F</b>) differential metabolite volcano plot in negative ion mode. (<b>G</b>) Scatter plot of KEGG enrichment of differential metabolites in positive ion mode. (<b>H</b>) Scatter plot of KEGG enrichment of differential metabolites in negative ion mode. (<b>I</b>) KEGG enrichment analysis of DEMs.</p>
Full article ">Figure 11
<p>SW Biosynthesis Pathway in <span class="html-italic">A. oxytropis</span> OW 7.8. Note: LysX, LysZ, LysY, LysJ, and LysK are trypsin enzymes that can specifically cleave peptide bonds at different positions of lysine. SDH, LYS1, and LYS9 are saccharine reductases. lhpD/dpkA: Delta-1-piperideine-2-carboxylate reductase, lhpI: 1-piperideine-2-carboxylate reductase, PIPOX: <span class="html-italic">L</span>-PA oxidase.</p>
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14 pages, 442 KiB  
Article
Correlations and Variations Between the Major Biochemical Parameters of the Blood of Hybrid Swine
by Sergei Yu. Zaitsev, Oksana A. Voronina, Nikita S. Kolesnik, Anastasia A. Savina and Aloyna A. Zelenchenkova
Animals 2024, 14(20), 3002; https://doi.org/10.3390/ani14203002 - 17 Oct 2024
Abstract
In modern animal husbandry, increasing attention is given to mathematical modeling and statistical methods, especially for evaluating commercial hybrids. Our aim was to evaluate the phenotypic and genetic variability of biochemical parameters of blood serum of the 56 hybrid boars (Large White × [...] Read more.
In modern animal husbandry, increasing attention is given to mathematical modeling and statistical methods, especially for evaluating commercial hybrids. Our aim was to evaluate the phenotypic and genetic variability of biochemical parameters of blood serum of the 56 hybrid boars (Large White × Landrace × Duroc) raised in feeding stations (Russia) through mathematical modeling. The particular variances and covariances of traits were calculated using the limited maximum likelihood model and the REMLF90 programs. A narrow range of variability was found for major biochemical parameters in relationship with the “FFG-factor” (“fattening period × final live weight × gain”), including the majority of the metabolites (p ≤ 0.05). The highest values of the genetic correlations were observed for the “total protein” parameter with albumins (0.78), globulins (0.94), creatinine (0.99), and enzymes: AST (0.98), ALT (0.80), etc. Phenotypic and genetic relationships showed fairly high correlation coefficients (0.5–0.8). It is important to emphasize that most of the studied amino acids (alanine, arginine, aspartic acid and asparagine, glutamic acid and glutamine, glycine, isoleucine, leucine, serine, threonine, tyrosine, valine) were significantly associated with the “FFG-factor” (p ≤ 0.05). The proposed approach provides reliable data on metabolite variability and correlations. Full article
(This article belongs to the Section Pigs)
23 pages, 37649 KiB  
Article
Research on Forage–Livestock Balance in the Three-River-Source Region Based on Improved CASA Model
by Chenlu Hu, Yichen Tian, Kai Yin, Huiping Huang, Liping Li and Qiang Chen
Remote Sens. 2024, 16(20), 3857; https://doi.org/10.3390/rs16203857 - 17 Oct 2024
Abstract
As an important ecological barrier and a crucial base for animal husbandry in China, the forage–livestock balance in the Three-River-Source Region (TRSR) directly impacts both the degradation and recovery of grassland. This study examines the forage–livestock balance in the TRSR over the past [...] Read more.
As an important ecological barrier and a crucial base for animal husbandry in China, the forage–livestock balance in the Three-River-Source Region (TRSR) directly impacts both the degradation and recovery of grassland. This study examines the forage–livestock balance in the TRSR over the past 13 years (2010–2022) by calculating both the theoretical and actual livestock carrying capacity, thereby providing a scientific basis for regional animal husbandry policies. Firstly, the Carnegie–Ames–Stanford Approach (CASA) model was improved to fit the specific characteristics of alpine grassland ecosystem in the TRSR. This enhanced model was subsequently used to calculate the net primary productivity (NPP) of the grassland, from which the regional grassland yield and theoretical livestock carrying capacity were derived. Secondly, the actual livestock carrying capacity was calculated and spatialized based on the number of regional year-end livestock. Finally, the livestock carrying pressure index was determined using both the theoretical and actual livestock carrying capacity. The results revealed several key findings: (1) The average grassland NPP in the TRSR was 145.44 gC/m2, the average grassland yield was 922.7 kg/hm2, and the average theoretical livestock carrying capacity was 0.55 SU/hm2 from 2010 to 2022. Notably, all three metrics showed an increasing trend over the past 13 years, which indicates the rise in grassland vegetation activities. (2) The average actual livestock carrying capacity over the 13-year period was 0.46 SU/hm2, showing a decreasing trend on the whole. The spatial distribution displayed a pattern of higher capacity in the east and lower in the west. (3) Throughout the 13 years, the TRSR generally maintained a forage–livestock balance, with an average livestock carrying pressure index of 0.96 (insufficient). However, the trend of livestock carrying pressure is on the rise, with serious overloading observed in the western part of Qumalai County and the northern part of Tongde County. Slight overloading was also noted in Zhiduo, Maduo, and Zeku Counties. Notably, Tanggulashan Town, Zhiduo, Qumalai, and Maduo Counties showed significant increases in livestock carrying pressure, while Zaduo County and the eastern regions experienced significant decreases. In conclusion, this study not only provides feasible technical methods for assessing and managing the forage–livestock balance in the TRSR but also contributes significantly to the sustainable development of the region’s grassland ecosystem and animal husbandry industry. Full article
(This article belongs to the Special Issue Remote Sensing of Mountain and Plateau Vegetation)
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<p>The Three–River–Source Region: (<b>a</b>) digital elevation model and location; (<b>b</b>) grassland vegetation types.</p>
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<p>The spatiotemporal patterns of grassland NPP in the TRSR from 2010 to 2022: (<b>a</b>) mean annual NPP: the inset chart shows the interannual dynamics of NPP from 2010 to 2022, where the red dashed line shows the overall trend of NPP; (<b>b</b>) change trend of NPP: the inset chart shows the area proportion of each; (<b>c</b>) radial accumulation bar chart of NPP in different grassland vegetation types from 2010 to 2022.</p>
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<p>The spatiotemporal patterns of grassland NPP in the TRSR from 2010 to 2022: (<b>a</b>) mean annual NPP: the inset chart shows the interannual dynamics of NPP from 2010 to 2022, where the red dashed line shows the overall trend of NPP; (<b>b</b>) change trend of NPP: the inset chart shows the area proportion of each; (<b>c</b>) radial accumulation bar chart of NPP in different grassland vegetation types from 2010 to 2022.</p>
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<p>The spatiotemporal patterns of grassland yield and theoretical livestock carrying capacity in the TRSR from 2010 to 2022: (<b>a</b>) mean annual grassland yield: the inset chart shows the interannual dynamics of grassland yield from 2010 to 2022, where the red dashed line shows the overall trend of grassland yield; (<b>b</b>) change trend of grassland yield: the inset chart shows the area proportion of each; (<b>c</b>) mean annual theoretical livestock carrying capacity: the inset chart shows the interannual dynamics of theoretical livestock carrying capacity from 2010 to 2022, where the red dashed line shows the overall trend of theoretical livestock carrying capacity; (<b>d</b>) change trend of theoretical livestock carrying capacity: the inset chart shows the area proportion of each.</p>
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<p>Validations of simulated NPP and grassland yield by improved CASA model: (<b>a</b>) the correlation of simulated NPP by improved CASA model and MOD17A3 NPP, where the solid black line represents the fitting curve of simulated NPP and MOD17A3 NPP; (<b>b</b>) the correlation of simulated grassland yield by improved CASA model and observed grassland yield, where the solid black line represents the fitting curve of simulated grassland yield and observed grassland yield.</p>
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<p>The spatiotemporal patterns of actual livestock carrying capacity in the TRSR from 2010 to 2022: (<b>a</b>) mean annual actual livestock carrying capacity: the inset chart shows the interannual dynamics of actual livestock carrying capacity from 2010 to 2022, where the red dashed line shows the overall trend of actual livestock carrying capacity; (<b>b</b>) change trend of actual livestock carrying capacity: the inset chart shows the area proportion of each.</p>
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<p>The spatiotemporal patterns of the livestock carrying pressure in the TRSR from 2010 to 2022: (<b>a</b>) mean annual livestock carrying pressure: the inset chart shows the interannual dynamics of livestock carrying pressure from 2010 to 2022, where the red dashed line shows the overall trend of livestock carrying pressure; (<b>b</b>) change trend of livestock carrying pressure: the inset chart shows the area proportion of each.</p>
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<p>The spatiotemporal patterns of the livestock carrying pressure in the TRSR from 2010 to 2022: (<b>a</b>) mean annual livestock carrying pressure: the inset chart shows the interannual dynamics of livestock carrying pressure from 2010 to 2022, where the red dashed line shows the overall trend of livestock carrying pressure; (<b>b</b>) change trend of livestock carrying pressure: the inset chart shows the area proportion of each.</p>
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<p>Simulated NPP by improved CASA model and unimproved CASA model compared to MOD17A3 NPP product: (<b>a</b>) scatter plot with MOD17A3 NPP; (<b>b</b>) change curve of NPP from 2010 to 2022; (<b>c</b>) histogram of NPP in different grassland vegetation types; (<b>d</b>) radar map of NPP with different elevation grades.</p>
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<p>Livestock carrying pressure in the TRSR from 2010 to 2022: (<b>a</b>) heat map of livestock carrying pressure in each county from 2010 to 2022; (<b>b</b>) changes and average values of livestock carrying pressure in each county for 13 years.</p>
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<p>The spatial distributions of grazing condition and Three–River–Source Nature Reserve (<a href="https://sthjt.qinghai.gov.cn" target="_blank">https://sthjt.qinghai.gov.cn</a>, accessed on 14 July 2024) from 2010 to 2022: (<b>a</b>) mean annual actual livestock carrying capacity; (<b>b</b>) mean annual livestock carrying pressure; (<b>c</b>) mean annual NDVI.</p>
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31 pages, 18130 KiB  
Article
Research on Cattle Behavior Recognition and Multi-Object Tracking Algorithm Based on YOLO-BoT
by Lei Tong, Jiandong Fang, Xiuling Wang and Yudong Zhao
Animals 2024, 14(20), 2993; https://doi.org/10.3390/ani14202993 - 17 Oct 2024
Viewed by 214
Abstract
In smart ranch management, cattle behavior recognition and tracking play a crucial role in evaluating animal welfare. To address the issues of missed and false detections caused by inter-cow occlusions and infrastructure obstructions in the barn environment, this paper proposes a multi-object tracking [...] Read more.
In smart ranch management, cattle behavior recognition and tracking play a crucial role in evaluating animal welfare. To address the issues of missed and false detections caused by inter-cow occlusions and infrastructure obstructions in the barn environment, this paper proposes a multi-object tracking method called YOLO-BoT. Built upon YOLOv8, the method first integrates dynamic convolution (DyConv) to enable adaptive weight adjustments, enhancing detection accuracy in complex environments. The C2f-iRMB structure is then employed to improve feature extraction efficiency, ensuring the capture of essential features even under occlusions or lighting variations. Additionally, the Adown downsampling module is incorporated to strengthen multi-scale information fusion, and a dynamic head (DyHead) is used to improve the robustness of detection boxes, ensuring precise identification of rapidly changing target positions. To further enhance tracking performance, DIoU distance calculation, confidence-based bounding box reclassification, and a virtual trajectory update mechanism are introduced, ensuring accurate matching under occlusion and minimizing identity switches. Experimental results demonstrate that YOLO-BoT achieves a mean average precision (mAP) of 91.7% in cattle detection, with precision and recall increased by 4.4% and 1%, respectively. Moreover, the proposed method improves higher order tracking accuracy (HOTA), multi-object tracking accuracy (MOTA), multi-object tracking precision (MOTP), and IDF1 by 4.4%, 7%, 1.7%, and 4.3%, respectively, while reducing the identity switch rate (IDS) by 30.9%. The tracker operates in real-time at an average speed of 31.2 fps, significantly enhancing multi-object tracking performance in complex scenarios and providing strong support for long-term behavior analysis and contactless automated monitoring. Full article
(This article belongs to the Section Cattle)
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<p>Schematic diagram of the cowshed. Camera 1, positioned near the entrance of the barn, is responsible for collecting behavioral data of the cattle in the blue area. Camera 2, located farther from the entrance, is responsible for collecting behavioral data of the cattle in the red area.</p>
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<p>Examples of cattle data in different activity areas: (<b>a</b>) morning scene, (<b>b</b>) well-lit environment, (<b>c</b>) light interference, (<b>d</b>) night scene, (<b>e</b>) outdoor activity area, and (<b>f</b>) indoor activity area. The time in the top-left corner of the image represents the capture time of the data.</p>
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<p>Analysis of the cattle behavior dataset: (<b>a</b>) analysis of cattle behavior labels, and (<b>b</b>) distribution of cattle count in each image.</p>
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<p>iRMB structure and C2f-iRMB structure.</p>
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<p>ADown downsampling structure.</p>
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<p>DyHead structure.</p>
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<p>Dynamic convolution. The “*” represents element-wise multiplication of each convolution output with its attention weight.</p>
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<p>The improved YOLOv8n network architecture.</p>
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<p>Flowchart for multi-object tracking of cattle.</p>
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<p>Schematic representation of the tracking process leading to object loss due to occlusion: The red solid line denotes the detection frame, while the yellow dashed line represents the predicted frame.</p>
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<p>Ablation experiment results.</p>
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<p>Comparison of algorithm improved cattle instance detection. In scenario 1, standing cattle are mistakenly detected as walking; in scenario 2, some behavioral features of lying cattle are missed and walking behavior is repeatedly detected; and in scenario 3, some features of walking behavior are missed.</p>
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<p>Variation curve of re-identification model accuracy.</p>
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<p>Comparison of the improved results of replacing DIoU, (<b>a</b>,<b>c</b>) denote the tracking results of the original algorithm, and (<b>b</b>,<b>d</b>) denote the tracking results of the improved algorithm. The green circle indicates the part of the target extending beyond the detection box, while the red circle indicates the detection box containing extra background information.</p>
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<p>Comparison between before and after the tracking algorithm improvement at frame 50, frame 652, and frame 916, respectively. The white dotted line in the image indicates the untracked object.</p>
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<p>Comparison between before and after the tracking algorithm improvement at frame 22, frame 915, and frame 1504, respectively. The white dotted line in the image indicates the untracked object.</p>
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<p>Performance comparison of tracking algorithms.</p>
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<p>Tracking results for multiple tracking algorithms. White dashed lines in the image indicate untracked objects, while red dashed lines indicate incorrectly tracked objects. The time in the top-left corner of the image represents the capture time of the data.</p>
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<p>Behavioral duration data from the herd are displayed in one minute, focusing on the incidence of the behavior (<b>a</b>) and the number of individual cattle (<b>b</b>). Expanded to the entire 10 min video (<b>c</b>) to fully demonstrate behavioral changes in the herd over time.</p>
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<p>Time series statistics for each cattle over a one-minute period. Four cattle with both active and quiet behavior were specifically chosen to demonstrate these variations. The numbers 2, 4, 7, and 10 indicate the scaling of the selected cattle IDS assigned by the model in the initial frame.</p>
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21 pages, 3450 KiB  
Article
Field Trial with Vaccine Candidates Against Bovine Tuberculosis Among Likely Infected Cattle in a Natural Transmission Setting
by Ximena Ferrara Muñiz, Elizabeth García, Federico Carlos Blanco, Sergio Garbaccio, Carlos Garro, Martín Zumárraga, Odir Dellagostin, Marcos Trangoni, María Jimena Marfil, Maria Verónica Bianco, Alejandro Abdala, Javier Revelli, Maria Bergamasco, Adriana Soutullo, Rocío Marini, Rosana Valeria Rocha, Amorina Sánchez, Fabiana Bigi, Ana María Canal, María Emilia Eirin and Angel Adrián Cataldiadd Show full author list remove Hide full author list
Vaccines 2024, 12(10), 1173; https://doi.org/10.3390/vaccines12101173 - 17 Oct 2024
Viewed by 298
Abstract
Background/Objectives: Vaccines may improve the control and eradication of bovine tuberculosis. However, the evaluation of experimental candidates requires the assessment of the protection, excretion, transmission and biosafety. A natural transmission trial among likely infected animals was conducted. Methods: Seventy-four male heifers [...] Read more.
Background/Objectives: Vaccines may improve the control and eradication of bovine tuberculosis. However, the evaluation of experimental candidates requires the assessment of the protection, excretion, transmission and biosafety. A natural transmission trial among likely infected animals was conducted. Methods: Seventy-four male heifers were randomly distributed (five groups) and vaccinated subcutaneously with attenuated strains (M. bovis Δmce2 or M. bovis Δmce2-phoP), a recombinant M. bovis BCG Pasteur (BCGr) or M. bovis BCG Pasteur. Then, they cohoused with a naturally infected bTB cohort under field conditions exposed to the infection. Results: A 23% of transmission of wild-type strains was confirmed (non-vaccinated group). Strikingly, first vaccination did not induce immune response (caudal fold test and IFN-gamma release assay). However, after 74 days of exposure to bTB, animals were re-vaccinated. Although their sensitization increased throughout the trial, the vaccines did not confer significant protection, when compared to the non-vaccinated group, as demonstrated by pathology progression of lesions and confirmatory tools. Besides, the likelihood of acquiring the infection was similar in all groups compared to the non-vaccinated group (p > 0.076). Respiratory and digestive excretion of viable vaccine candidates was undetectable. To note, the group vaccinated with M. bovis Δmce2-phoP exhibited the highest proportion of animals without macroscopic lesions, compared to the one vaccinated with BCG, although this was not statistically supported. Conclusions: This highlights that further evaluation of these vaccines would not guarantee better protection. The limitations detected during the trial are discussed regarding the transmission rate of M. bovis wild-type, the imperfect test for studying sensitization, the need for a DIVA diagnosis and management conditions of the trials performed under routine husbandry conditions. Re-vaccination of likely infected bovines did not highlight a conclusive result, even suggesting a detrimental effect on those vaccinated with M. bovis BCG. Full article
(This article belongs to the Section Veterinary Vaccines)
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<p>Timeline of the trial. A schematic timeline illustrating the most relevant intervention and sampling points, types of samples and techniques used to monitor the animals prior to the necropsy. mpv: months pre-vaccination. dpv: days post-vaccination. dprv: days post re-vaccination. TST: tuberculin skin test, IGRA: Interferon-Gamma release assay, MAP: <span class="html-italic">Mycobacterium avium</span> subsp. <span class="html-italic">Paratuberculosis</span>, ELISA: Enzyme-Linked Immunosorbent assay, CFT: caudal fold test, PCR: Polymerase Chain Reaction, bTB: bovine tuberculosis.</p>
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<p>(<b>A</b>). Prevalence of animals positive to the caudal fold test (CFT) in each group at the different sampling times. (<b>B</b>). Percentage of positive animals to the interferon-gamma release assay (IGRA). Kruskal–Wallis test and Dunn’s post test. * Prevalence that differed significantly between the studied groups, <span class="html-italic">p</span> &lt; 0.05. The dotted vertical line and gray arrow indicate the day of the re-vaccination. (<b>C</b>,<b>D</b>). Incidence of positivity for both CFT and IGRA, respectively, represented by the Kaplan–Meier analysis. TST: Tuberculin skin test. Dpv: days post vaccination. Dprv: days post re-vaccination. IFNg: Interferon gamma.</p>
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<p>Scatter plot showing the OD readouts for Interferon Gamma Release Assay (IGRA) detected in cattle from the different groups under study at 75 days post vaccination (dpv), 42, 144 and 249 days post re-vaccination (dprv) when stimulated with PPDA (<b>A</b>), PPDB (<b>B</b>) and FP (<b>C</b>). The dotted horizontal line represents the cut-off of 0.1, above which is considered a positive OD value and below which is negative. Kruskal–Wallis Test and Dunn’s post test. *, <span class="html-italic">p</span> &lt; 0.05; **, <span class="html-italic">p</span> &lt; 0.1; *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Scatter plot showing the OD readouts for Interferon Gamma Release Assay (IGRA) detected in cattle from the different groups under study at 75 days post vaccination (dpv), 42, 144 and 249 days post re-vaccination (dprv) when stimulated with PPDA (<b>A</b>), PPDB (<b>B</b>) and FP (<b>C</b>). The dotted horizontal line represents the cut-off of 0.1, above which is considered a positive OD value and below which is negative. Kruskal–Wallis Test and Dunn’s post test. *, <span class="html-italic">p</span> &lt; 0.05; **, <span class="html-italic">p</span> &lt; 0.1; *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>(<b>A</b>). Macroscopic lesion score. (<b>B</b>). Microscopic lesion score of each animal from the different groups.</p>
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36 pages, 3237 KiB  
Article
Spatial and Bioaccumulation of Heavy Metals in a Sheep-Based Food System: Implications for Human Health
by Florin-Ioan Fechete, Maria Popescu, Sorin-Marian Mârza, Loredana-Elena Olar, Ionel Papuc, Florin-Ioan Beteg, Robert-Cristian Purdoiu, Andrei Răzvan Codea, Caroline-Maria Lăcătuș, Ileana-Rodica Matei, Radu Lăcătuș, Adela Hoble, Ioan Valentin Petrescu-Mag and Florin-Dumitru Bora
Toxics 2024, 12(10), 752; https://doi.org/10.3390/toxics12100752 - 16 Oct 2024
Viewed by 253
Abstract
Heavy metal contamination in agricultural soils presents serious environmental and health risks. This study assessed the bioaccumulation and spatial distribution of nickel, cadmium, zinc, lead, and copper within a sheep-based food chain in the Baia Mare region, Romania, which includes soil, green grass, [...] Read more.
Heavy metal contamination in agricultural soils presents serious environmental and health risks. This study assessed the bioaccumulation and spatial distribution of nickel, cadmium, zinc, lead, and copper within a sheep-based food chain in the Baia Mare region, Romania, which includes soil, green grass, sheep serum, and dairy products. Using inductively coupled plasma mass spectrometry (ICP-MS), we analyzed the concentrations of these metals and calculated bioconcentration factors (BCFs) to evaluate their transfer through trophic levels. Spatial analysis revealed that copper (up to 2528.20 mg/kg) and zinc (up to 1821.40 mg/kg) exceeded permissible limits, particularly near former mining sites. Elevated lead (807.59 mg/kg) and cadmium (2.94 mg/kg) were observed in industrial areas, while nickel and cobalt showed lower concentrations, but with localized peaks. Zinc was the most abundant metal in grass, while cadmium transferred efficiently to milk and cheese, raising potential health concerns. The results underscore the complex interplay between soil properties, contamination sources, and biological processes in heavy metal accumulation. These findings highlight the importance of continuous monitoring, risk assessment, and mitigation strategies to protect public health from potential exposure through contaminated dairy products. Full article
(This article belongs to the Section Agrochemicals and Food Toxicology)
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<p>The spatial distribution of detectable elements in sheep milk and cheese samples; comparing mean concentration across different collection areas.</p>
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<p>The spatial distribution of detectable elements in sheep serum samples; comparing mean concentration across different collection areas.</p>
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19 pages, 5406 KiB  
Article
An Automatic Movement Monitoring Method for Group-Housed Pigs
by Ziyuan Liang, Aijun Xu, Junhua Ye, Suyin Zhou, Xiaoxing Weng and Sian Bao
Animals 2024, 14(20), 2985; https://doi.org/10.3390/ani14202985 - 16 Oct 2024
Viewed by 248
Abstract
Continuous movement monitoring helps quickly identify pig abnormalities, enabling immediate action to enhance pig welfare. However, continuous and precise monitoring of daily pig movement on farms remains challenging. We present an approach to automatically and precisely monitor the movement of group-housed pigs. The [...] Read more.
Continuous movement monitoring helps quickly identify pig abnormalities, enabling immediate action to enhance pig welfare. However, continuous and precise monitoring of daily pig movement on farms remains challenging. We present an approach to automatically and precisely monitor the movement of group-housed pigs. The instance segmentation model YOLOv8m-seg was applied to detect the presence of pigs. We then applied a spatial moment algorithm to quantitatively summarize each detected pig’s contour as a corresponding center point. The agglomerative clustering (AC) algorithm was subsequently used to gather the pig center points of a single frame into one point representing the group-housed pigs’ position, and the movement volume was obtained by calculating the displacements of the clustered group-housed pigs’ center points of consecutive frames. We employed the method to monitor the movement of group-housed pigs from April to July 2023; more than 1500 h of top-down pig videos were recorded by a surveillance camera. The F1 scores of the trained YOLOv8m-seg model during training were greater than 90% across most confidence levels, and the model achieved an mAP50-95 of 0.96. The AC algorithm performs with an average extraction time of less than 1 millisecond; this method can run efficiently on commodity hardware. Full article
(This article belongs to the Section Pigs)
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<p>Experimental conditions: (<b>a</b>) draft of the pigpen; (<b>b</b>) installation position of the dual sensor surveillance camera; (<b>c</b>) top-down camera view of the pigsty floor.</p>
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<p>Designed workflow of the pig movement monitoring method.</p>
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<p>Model structure of YOLOv8-seg: the segmentation and detection tasks begin with the (<b>a</b>) original image and output an (<b>b</b>) image with a bounding box and segmentation contour.</p>
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<p>Distinguishing the center point of a predicted pig contour. The images in the columns are described as follows: (1) prediction image, (2) mean coordinate, (3) least squares, (4) signed area, and (5) spatial moment. The different pig behavior patterns depicted in each row are as follows: (<b>a</b>) lying, (<b>b</b>) sitting, and (<b>c</b>) standing.</p>
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<p>Running times of different algorithms based on the test video (1166 frames): (<b>a</b>) Time spent on each frame. (<b>b</b>) Total time spent in progress (average of 30 repetitions).</p>
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<p>Complete distribution information of group pig positions. The information was obtained from 13 May to 8 July 2023, and every pig position was drawn at a given point.</p>
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<p>Changes in the position information over time: (<b>a</b>) Pigs positioned during two periods from 13 May to 9 June 2023 and 10 June to 8 July 2023. (<b>b</b>) The statistical variation in the number of pigs appearing in different regions during the two periods.</p>
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<p>Daily summed movement distances of group-housed pigs from 13 May 2023 to 8 July 2023.</p>
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<p>Movement characteristics of pigs in terms of days with the longest, shortest, and median movement distances; every subfigure starts at 0 a.m. and ends at 12 p.m.</p>
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<p>Various distribution locations of group-housed pigs: (<b>a</b>) pigs lying close to the corner; (<b>b</b>) pigs congregating near the door; (<b>c</b>) herd of pigs eating.</p>
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14 pages, 4919 KiB  
Article
Phylogenetic Relations and High-Altitude Adaptation in Wild Boar (Sus scrofa), Identified Using Genome-Wide Data
by Shiyong Fang, Haoyuan Zhang, Haoyuan Long, Dongjie Zhang, Hongyue Chen, Xiuqin Yang, Hongmei Pan, Xiao Pan, Di Liu and Guangxin E
Animals 2024, 14(20), 2984; https://doi.org/10.3390/ani14202984 - 16 Oct 2024
Viewed by 254
Abstract
The Qinghai–Tibet Plateau (QTP) wild boar is an excellent model for investigating high-altitude adaptation. In this study, we analyzed genome-wide data from 93 wild boars compiled from various studies worldwide, including the QTP, southern and northern regions of China, Europe, Northeast Asia, and [...] Read more.
The Qinghai–Tibet Plateau (QTP) wild boar is an excellent model for investigating high-altitude adaptation. In this study, we analyzed genome-wide data from 93 wild boars compiled from various studies worldwide, including the QTP, southern and northern regions of China, Europe, Northeast Asia, and Southeast Asia, to explore their phylogenetic patterns and high-altitude adaptation based on genome-wide selection signal analysis and run of homozygosity (ROH) estimation. The findings demonstrate the alignment between the phylogenetic associations among wild boars and their geographical location. An ADMIXTURE analysis indicated a relatively close genetic relationship between QTP and southern Chinese wild boars. Analyses of the fixation index and cross-population extended haplotype homozygosity between populations revealed 295 candidate genes (CDGs) associated with high-altitude adaptation, such as TSC2, TELO2, SLC5A1, and SLC5A4. These CDGs were significantly overrepresented in pathways such as the mammalian target of rapamycin signaling and Fanconi anemia pathways. In addition, 39 ROH islands and numerous selective CDGs (e.g., SLC5A1, SLC5A4, and VCP), which are implicated in glucose metabolism and mitochondrial function, were discovered in QTP wild boars. This study not only assessed the phylogenetic history of QTP wild boars but also advanced our comprehension of the genetic mechanisms underlying the adaptation of wild boars to high altitudes. Full article
(This article belongs to the Section Animal Genetics and Genomics)
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<p>Phylogenetic analysis and population structure of worldwide wild boars. (<b>A</b>) Genome-wide phylogenetic trees of wild boar populations. Each color represents the wild boar population in a different region, including the SCN wild boar population (SCN), the NCN wild boar population (NCN), the EU wild boar population (EU), the NEA wild boar population (NEA), the QTP wild boar population (QTP), and the SEA wild boar population (SEA). (<b>B</b>) Principal component analysis, based on all available data, divided into six groups by region. (<b>C</b>) Neighbor-net graph of worldwide wild boar populations using the pairwise difference (Fs<sub>T</sub>). (<b>D</b>) Analysis of the population structure of each wild boar population. The <span class="html-italic">K</span> value is the number of assumed ancestral populations, which was 2 to 5. #: The most reliable <span class="html-italic">K</span> value was 4, which had the minimum CV error. (<b>E</b>) Cross-validation error for each <span class="html-italic">K</span> value (<span class="html-italic">K</span> = 1–10).</p>
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<p>Population LD decay and demographic history inference analysis of wild boar populations. (<b>A</b>) LD decay of wild boar populations, including the southern Chinese wild boar population (SCN), northern Chinese wild boar population (NCN), European wild boar population (EU), Northeast Asian wild boar population (NEA), and Qinghai–Tibet Plateau wild boar population (QTP). (<b>B</b>) Effective population sizes of different wild boar populations, inferred from autosomes.</p>
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<p>Genome-wide selective signal analysis of worldwide wild boars to identify the high-altitude adaptability-related genes in Qinghai–Tibet Plateau wild boars. (<b>A</b>) Manhattan map of F<sub>ST</sub> between groups. (<b>B</b>) Manhattan map of XP-EHH between groups.</p>
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<p>ROH proportions among the populations. The width of the bar chart in the figure is 20KB, and green marks the regions of the genome with ROH population frequencies greater than 25%. (<b>A</b>) ROH proportions in the Qinghai–Tibet Plateau wild boar population. (<b>B</b>) ROH proportions in wild boar populations not distributed on the Qinghai–Tibet Plateau, including the northern Chinese wild boar population, southern Chinese wild boar population, European wild boar population, and Northeast Asian wild boar population.</p>
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21 pages, 3449 KiB  
Article
Effects of Different Additives on the Chemical Composition, Fermentation Quality, Bacterial Community and Gene Function Prediction of Caragana korshinskii Kom. Silage
by Yuxiang Wang, Manlin Wei, Fuyu Yang, Haiying Zheng, Junjie Gao, Wen Peng, Ming Xiao, Runze Zhang and Yongjie Zheng
Agronomy 2024, 14(10), 2386; https://doi.org/10.3390/agronomy14102386 - 15 Oct 2024
Viewed by 296
Abstract
The aim of this study was to investigate the effects of Lentilactobacillus plantarum (LP), cellulase (CE), and xylanase (XE) supplementation on the fermentation quality, chemical composition, and bacterial community of Caragana korshinskii Kom. silage. Four groups were designed for the study. No additives [...] Read more.
The aim of this study was to investigate the effects of Lentilactobacillus plantarum (LP), cellulase (CE), and xylanase (XE) supplementation on the fermentation quality, chemical composition, and bacterial community of Caragana korshinskii Kom. silage. Four groups were designed for the study. No additives were used in the control group (CK), and LP (1 × 106 cfu/g), CE (1 × 104 IU/g) and XE (2 × 105 IU/g) were added to the experimental groups on a fresh matter basis, with three replicates per group. To promote fermentation, 5% molasses was added to all of the groups. On days 15 and 60, fermentation quality, chemical composition and the bacterial community were analysed. The pH of groups CE and XE was lower than that of the CK group at 60 days. During ensiling, the lactic acid (LA) content in the experimental groups and the acetic acid (AA) content in the CK and LP groups increased. At 60 days, the dominant genera in the CK and LP groups was Weissella and the dominant genera in the CE and XE groups was Lentilactobacillus. At different times during silage, nucleotide metabolism was enhanced, whereas the metabolism of carbohydrate, amino acids, energy, cofactors and vitamins was inhibited in the LP group. However, the metabolism of amino acids, energy, cofactors and vitamins in the CE and XE groups was increased, whereas the metabolism of nucleotides was inhibited. In conclusion, LP, CE and XE could exert a positive effect on the fermentation quality of C. korshinskii Kom. silage by shifting the bacterial community composition. Full article
(This article belongs to the Section Agricultural Biosystem and Biological Engineering)
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<p>Venn diagram of the bacterial species. CK, control; LP, <span class="html-italic">Lentilactobacillus plantarum</span>; CE, cellulase; XE, xylanase. The numbers behind CK, LP, CE, and XE represent the days of ensiling.</p>
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<p>PCoA of the bacterial species diversity in <span class="html-italic">C. korshinskii</span> Kom. silage at 15 days (<b>A</b>) and 60 days (<b>B</b>). CK, control; LP, <span class="html-italic">Lentilactobacillus plantarum</span>; CE, cellulase; XE, xylanase. The numbers behind CK, LP, CE, and XE represent the days of ensiling.</p>
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<p>Distribution of bacterial communities at the phylum (<b>A</b>) and genus (<b>B</b>) levels at days 15 and 60 in <span class="html-italic">C. korshinskii</span> Kom. silage. Small populations with abundances less than 0.01 were combined as others. CK, control; LP, <span class="html-italic">Lentilactobacillus plantarum</span>; CE, cellulase; XE, xylanase. The numbers behind CK, LP, CE, and XE represent the days of ensiling.</p>
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<p>Species differences in bacterial genera (LDA = 3) between 15 days (<b>A</b>) and 60 days (<b>B</b>) of ensiling. CK, control; LP, <span class="html-italic">Lentilactobacillus plantarum</span>; CE, cellulase; XE, xylanase. The numbers behind CK, LP, CE, and XE represent the days of ensiling.</p>
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<p>Species differences in bacterial genera (LDA = 3) between 15 days (<b>A</b>) and 60 days (<b>B</b>) of ensiling. CK, control; LP, <span class="html-italic">Lentilactobacillus plantarum</span>; CE, cellulase; XE, xylanase. The numbers behind CK, LP, CE, and XE represent the days of ensiling.</p>
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<p>Heatmap of the Spearman correlation coefficients of chemical composition, fermentation quality and bacterial genera of <span class="html-italic">C. korshinskii</span> Kom. silage at 15 (<b>A</b>) and 60 (<b>B</b>) days. The colour of the heatmap indicates the Spearman correlation coefficient “R” (−1 to 1). R &gt; 0 indicates a positive correlation, and R &lt; 0 indicates a negative correlation. *, 0.01 &lt; <span class="html-italic">p</span> &lt; 0.05; **, 0.001 &lt; <span class="html-italic">p</span> &lt; 0.01; ***, <span class="html-italic">p</span> &lt; 0.001. CK, control; LP, <span class="html-italic">Lentilactobacillus plantarum</span>; CE, cellulase; XE, xylanase. The numbers behind CK, LP, CE, and XE represent the days of ensiling.</p>
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<p>Predicted pathways of the bacterial community in <span class="html-italic">C. korshinskii</span> Kom. at 15 days and 60 days of ensiling. (<b>A</b>) the first metabolic pathway at 15 days of <span class="html-italic">C. korshinskii</span> Kom. Silage. (<b>B</b>) the first metabolic pathway at 60 days of <span class="html-italic">C. korshinskii</span> Kom. Silage. (<b>C</b>) the second metabolic pathway at 15 days of <span class="html-italic">C. korshinskii</span> Kom. Silage. (<b>D</b>) the second metabolic pathway at 60 days of <span class="html-italic">C. korshinskii</span> Kom. Silage. (<b>E</b>) carbohydrate metabolism of the third pathway level at 15 days of <span class="html-italic">C. korshinskii</span> Kom. Silage. (<b>F</b>) carbohydrate metabolism of the third pathway level at 60 days of <span class="html-italic">C. korshinskii</span> Kom. Silage. CK, control; LP, <span class="html-italic">Lentilactobacillus plantarum</span>; CE, cellulase; XE, xylanase. The numbers behind CK, LP, CE, and XE represent the days of ensiling.</p>
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21 pages, 6105 KiB  
Article
Guanethidine Restores Tetracycline Sensitivity in Multidrug-Resistant Escherichia coli Carrying tetA Gene
by Xiaoou Zhao, Mengna Zhang, Zhendu Zhang, Lei Wang, Yu Wang, Lizai Liu, Duojia Wang, Xin Zhang, Luobing Zhao, Yunhui Zhao, Xiangshu Jin, Xiaoxiao Liu and Hongxia Ma
Antibiotics 2024, 13(10), 973; https://doi.org/10.3390/antibiotics13100973 - 15 Oct 2024
Viewed by 426
Abstract
The worrying issue of antibiotic resistance in pathogenic bacteria is aggravated by the scarcity of novel therapeutic agents. Antibiotic adjuvants offer a promising solution due to their cost-effectiveness and high efficacy in addressing this issue, such as the β-lactamase inhibitor sulbactam (a β-lactam [...] Read more.
The worrying issue of antibiotic resistance in pathogenic bacteria is aggravated by the scarcity of novel therapeutic agents. Antibiotic adjuvants offer a promising solution due to their cost-effectiveness and high efficacy in addressing this issue, such as the β-lactamase inhibitor sulbactam (a β-lactam adjuvant) and the dihydrofolate reductase inhibitor trimethoprim (a sulfonamide adjuvant). This study aimed to discover potential adjuvants for tetracyclines from a list of previously approved drugs to restore susceptibility to Escherichia coli carrying the tetA gene. We have screened guanethidine, a compound from the Chinese pharmacopoeia, which effectively potentiates the activity of tetracyclines by reversing resistance in tetA-positive Escherichia coli, enhancing its antibacterial potency, and retarding the development of resistance. Guanethidine functions via the inhibition of the TetA efflux pump, thereby increasing the intracellular concentration of tetracyclines. Our findings suggest that guanethidine holds promise as an antibiotic adjuvant. Full article
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<p>The antibacterial activity of guanethidine in combination with tetracyclines against <span class="html-italic">tetA</span>-positive <span class="html-italic">E. coli</span> C3. (<b>A</b>) The FIC index of guanethidine and tetracyclines. The FIC index (fractional inhibitory concentration index) is commonly used to define the interactions between two bioactive compounds. The FIC index is commonly used to assess the synergistic or antagonistic effects of antibiotics when used in combination with other antibiotics or antibiotic adjuvants against microorganisms. Synergy is defined as an FIC index of ≤0.5. (<b>B</b>) The MICs of tetracyclines with or without guanethidine (2.5 mg/mL), the fold of decrease in MICs, and whether sensitivity can be restored. MIC ≤ 4 μg/mL is sensitive, refer to CILS 2020. All experiments were conducted with four biological replicates.</p>
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<p>The antimicrobial efficiency of guanethidine in combination with tetracyclines against <span class="html-italic">E. coli</span> C3. (<b>A</b>) Time–Survivor Curves of guanethidine in combination with tetracyclines within 24 h. (<b>B</b>) Growth curves in 24 h. All experiments were conducted with three biological replicates.</p>
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<p>The FIC indices of guanethidine and tetracycline against various sources of <span class="html-italic">E. coli</span> with or without <span class="html-italic">tetA</span>. Synergy was defined as an FIC index of ≤0.5. All experiments were conducted with four biological replicates.</p>
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<p>The MICs of tetracyclines against <span class="html-italic">E. coli</span> in the DMEM or MH medium, with or without guanethidine (2.5 mg/mL). MIC ≤ 4 μg/mL is sensitive, refer to CILS 2020. All experiments were conducted with four biological replicates.</p>
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<p>Emergence in <span class="html-italic">E. coli</span> ATCC25922 of resistance to tetracycline (<b>A</b>), doxycycline (<b>B</b>), and minocycline (<b>C</b>) with or without guanidine (2.5 mg/mL) after successive passages for 30 days.</p>
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<p>Effects of guanethidine (0–2.5 mg/mL) on the permeability of <span class="html-italic">E. coli</span> C3 cell membrane: (<b>A</b>) outer membrane and (<b>B</b>) inner membrane. The significance of the differences was analyzed by one-way ANOVA: ns, <span class="html-italic">p</span> &gt; 0.05; *, <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. All experiments were performed with five biological replicates.</p>
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<p>Effects of guanethidine (0–2.5 mg/mL) on the PMF of <span class="html-italic">E. coli</span> C3: (<b>A</b>) the transmembrane potential gradient (ΔΨ) and (<b>B</b>) pH gradient (ΔpH). The significance of the differences was analyzed by one-way ANOVA: ns, <span class="html-italic">p</span> &gt; 0.05; *, <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. All experiments were performed with three biological replicates.</p>
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<p>Effects of guanethidine (0–2.5 mg/mL) on the intracellular ATP and ROS of <span class="html-italic">E. coli</span> C3: (<b>A</b>) effects on intracellular ATP and (<b>B</b>) effects on intracellular ROS levels. The significance of the differences was analyzed by one-way ANOVA: ns, <span class="html-italic">p</span> &gt; 0.05; *, <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. All experiments were performed with three biological replicates.</p>
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<p>An increased intracellular accumulation of tetracycline in <span class="html-italic">E. coli</span> caused by guanethidine. (<b>A</b>) An intracellular accumulation of tetracycline with guanethidine (0–2.5 mg/mL) in <span class="html-italic">E. coli</span> C3. (<b>B</b>) An intracellular accumulation of tetracycline with guanethidine (0–2.5 mg/mL) in <span class="html-italic">E. coli</span> ATCC25922. (<b>C</b>) An intracellular accumulation of guanethidine in <span class="html-italic">E. coli</span> C3 or <span class="html-italic">E. coli</span> ATCC25922. The significance of the differences was analyzed by one-way ANOVA: ns, <span class="html-italic">p</span> &gt; 0.05; *, <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. All experiments were performed with three biological replicates.</p>
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<p>The effect of guanethidine (0–2.5 mg/mL) on the expression of the tetracycline-efflux pump gene <span class="html-italic">tetA</span>. The significance of the differences was analyzed by one-way ANOVA: ns, <span class="html-italic">p</span> &gt; 0.05; *, <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. All experiments were performed with three biological replicates.</p>
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<p>(<b>A</b>) The screening procedure of <span class="html-italic">E. coli</span> DH5α-ZMN1 (<b>B</b>) The FIC index of guanethidine and tetracyclines. Synergy is defined as an FIC index of ≤0.5. (<b>C</b>) The MICs of tetracyclines with or without guanethidine (2.5 mg/mL), folds decrease in MICs are shown in red. All experiments were performed with four biological replicates.</p>
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<p>The predicted binding mode of protein TetA with the compound guanidine by molecular docking. The protein framework is tubular and stained bright blue, guanethidine is depicted in gray, and the yellow dashed line indicates hydrogen bond distances.</p>
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<p>Checkerboard microdilution method to test the antimicrobial effect of tetracycline in combination with guanethidine and its derivatives. (<b>A</b>) The effect of tetracycline in combination with guanethidine. (<b>B</b>) The effect of tetracycline in combination with guanidine. (<b>C</b>) The effect of tetracycline in combination with azacyclooctane. (<b>D</b>) The effect of tetracycline in combination with guanethidine with the addition of additional subinhibitory concentrations of guanidine (1.25 mg/mL). (<b>E</b>) The effect of tetracycline in combination with guanethidine with the addition of additional subinhibitory concentrations of azacyclooctane (1.25 mg/mL). (<b>F</b>) The chemical formulas of guanethidine and its derivatives guanidine and azacyclooctane. All experiments were performed with three biological replicates.</p>
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<p>(<b>A</b>) The cytotoxicity of different concentrations of guanethidine in combination with tetracycline, and (<b>B</b>) the hemolysis of different concentrations of guanethidine in combination with tetracycline. (<b>A</b>) and (<b>B</b>) are illustrated using the same colour markings. All experiments were performed with three biological replicates.</p>
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<p>(<b>A</b>) The survival rate of Galleria mellonella larvae infected with <span class="html-italic">E. coli</span> C3 (10<sup>6</sup>CFU/mL). Each group was treated with tetracycline (35 mg/kg) or guanethidine (10 mg/kg) alone or in combination (35 mg/kg +10 mg/kg) (<span class="html-italic">n</span> = 8 per group). (<b>B</b>) The survival of mice infected with <span class="html-italic">E. coli</span> C3 (10<sup>8</sup>CFU/mL). Each group was treated with tetracycline (35 mg/kg) or guanethidine (10 mg/kg) alone or in combination (35 mg/kg +10 mg/kg) (<span class="html-italic">n</span> = 8 per group). <span class="html-italic">p</span> values were tested using the Mantel–Cox test, with <span class="html-italic">p</span> &lt; 0.05 indicating a significant change. (<b>C</b>) <span class="html-italic">E. coli</span> C3 (10<sup>6</sup> CFU/mL) infected mice were treated with tetracycline (35 mg/kg) or guanidine combined with tetracycline (35 mg/kg + 10 mg/kg) to determine the bacterial load in the heart, liver, spleen, lung, and kidney (<span class="html-italic">n</span> = 6 per group). In the <span class="html-italic">t</span>-test, * indicates <span class="html-italic">p</span> &lt; 0.05, a significant difference, and ** indicates <span class="html-italic">p</span> &lt; 0.01, indicating a highly significant difference.</p>
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16 pages, 7009 KiB  
Article
Cow Placenta Extract Ameliorates Cyclophosphamide-Induced Intestinal Damage by Enhancing the Intestinal Barrier, Improving Immune Function, and Restoring Intestinal Microbiota
by Yuquan Zhao, Zeru Zhang, Anguo Tang, Zhi Zeng, Weijian Zheng, Yuxin Luo, Yixin Huang, Xinyi Dai, Wei Lu, Lei Fan and Liuhong Shen
Vet. Sci. 2024, 11(10), 505; https://doi.org/10.3390/vetsci11100505 (registering DOI) - 14 Oct 2024
Viewed by 277
Abstract
Immunosuppression undermines intestinal barrier integrity. Cow placenta extract (CPE) primarily consists of active peptides with immunomodulatory and antioxidant effects. This study aimed to examine the preventive effect of CPE against intestinal damage induced by cyclophosphamide (Cy) in immunosuppressed mice. Thirty-six mice were randomly [...] Read more.
Immunosuppression undermines intestinal barrier integrity. Cow placenta extract (CPE) primarily consists of active peptides with immunomodulatory and antioxidant effects. This study aimed to examine the preventive effect of CPE against intestinal damage induced by cyclophosphamide (Cy) in immunosuppressed mice. Thirty-six mice were randomly allocated into three groups: control group (C), model group (M), and treatment group (CPE). The mice in the CPE group were provided with 1500 mg/kg/day of CPE via gavage. In the last 3 days, mice in the groups M and CPE received intraperitoneal injections of 80 mg/kg/day of Cy. The results showed that CPE improved intestinal barrier function by decreasing serum d-Lactate (D-LA) levels and diamine oxidase (DAO) activity, while elevating the relative expression of Occludin, zonula occludens-1 (ZO-1), and mucin-2 (MUC-2) mRNA. Additionally, CPE improved the immune organ index and elevated the levels of secretory immunoglobulin A (sIgA), superoxide dismutase (SOD), interleukin-1beta (IL-1β), interleukin-4 (IL-4), interleukin-10 (IL-10), and tumor necrosis factor-α (TNF-α) in the intestine, thereby enhancing intestinal mucosal immune function. Furthermore, CPE improved the diversity of intestinal microbiota and increased the abundance of Candidatus_Saccharimonas, Psychrobacter, and Enterorhabdus, which promoted the proper functioning of the intestines. These findings suggest that CPE effectively ameliorates Cy-induced intestinal damage by enhancing the intestinal barrier, improving immune function, and restoring intestinal microbiota. Full article
(This article belongs to the Special Issue Nutraceuticals to Mitigate the Secret Killers in Animals)
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<p>Test results of body weight and immune organ index in experimental mice. (<b>A</b>) Mouse body weight on the 28th day; (<b>B</b>) thymic index in each group; (<b>C</b>) spleen index in each group. ** indicates that group M exhibited statistical significance compared to group C. ** indicates <span class="html-italic">p</span> &lt; 0.01; # indicates that group CPE exhibited statistical significance compared to group M. # indicates <span class="html-italic">p</span> &lt; 0.05, ## indicates <span class="html-italic">p</span> &lt; 0.01 (<span class="html-italic">n</span> = 12).</p>
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<p>Detection results of intestinal permeability index in each group of experimental mice. (<b>A</b>) Diamine oxidase detection results in each group. (<b>B</b>) D-lactate results in each group. ** indicates that group M exhibited statistical significance compared to group C. ** indicates <span class="html-italic">p</span> &lt; 0.01; # indicates that group CPE exhibited statistical significance compared to group M. # indicates <span class="html-italic">p</span> &lt; 0.05, ## indicates <span class="html-italic">p</span> &lt; 0.01 (<span class="html-italic">n</span> = 12).</p>
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<p>Results of intestinal sections of mice in each group. (<b>A</b>) The effect of CPE on the morphological characteristics of the duodenum in each group. (<b>B</b>) The influence of CPE on the morphological characteristics of the jejunum in each group. (<b>C</b>) The influence of CPE on the morphological characteristics of the ileum in each group; scale: 100 µm, magnification: 100×. (<b>D</b>) The effect of CPE on the height of small intestinal villi in each group. (<b>E</b>) Depth of small intestinal villous crypt in each group. (<b>F</b>) The ratio of small intestinal villus height to crypt depth in each group. * indicates that group M exhibited statistical significance compared to group C. * indicates <span class="html-italic">p</span> &lt; 0.05, ** indicates <span class="html-italic">p</span> &lt; 0.01. # indicates that group CPE exhibited statistical significance compared to group M. # indicates <span class="html-italic">p</span> &lt; 0.05, ## indicates <span class="html-italic">p</span> &lt; 0.01 (<span class="html-italic">n</span> = 10).</p>
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<p>Detection of sIgA, β-DF, and cytokines in intestinal tissue of mice in each group. (<b>A</b>) Detection results of sIgA in each group. (<b>B</b>) Detection results of β-DF in each group. (<b>C</b>) Detection results of SOD in each group. (<b>D</b>) Detection results of MDA in each group. (<b>E</b>) Detection results of TNF-α in each group. (<b>F</b>) Detection results of IL-1β in each group. (<b>G</b>) Detection results of IL-4 in each group. (<b>H</b>) Detection results of IL-10 in each group. ** indicates that group M exhibited statistical significance compared to group C. ** indicates <span class="html-italic">p</span> &lt; 0.01. # indicates that group CPE exhibited statistical significance compared to group M. # indicates <span class="html-italic">p</span> &lt; 0.05, ## indicates <span class="html-italic">p</span> &lt; 0.01 (<span class="html-italic">n</span> = 12).</p>
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<p>Detection results of relative expression levels of intestinal barrier genes and mucin 2 (<span class="html-italic">MUC2</span>) mRNA in mice from various experimental groups. ** indicates that group M exhibited statistical significance compared to group C. ** indicates <span class="html-italic">p</span> &lt; 0.01. # indicates that group CPE exhibited statistical significance compared to group M. # indicates <span class="html-italic">p</span> &lt; 0.05, ## indicates <span class="html-italic">p</span> &lt; 0.01 (<span class="html-italic">n</span> = 6).</p>
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<p>Beta diversity index. (<b>A</b>) Principal coordinates analysis (PCoA). (<b>B</b>) Nonmetric multidimensional scaling (NMDS). (<span class="html-italic">n</span> = 8).</p>
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<p>Detection results of microbial community composition in each group of experimental mice. (<b>A</b>) Relative abundance of microbes at the phylum level. (<b>B</b>) Relative abundance of microbes at the genus level. * indicates that group M exhibited statistical significance compared to group C. * indicates <span class="html-italic">p</span> &lt; 0.05, ** indicates <span class="html-italic">p</span> &lt; 0.01. # indicates that group CPE exhibited statistical significance compared to group M. # indicates <span class="html-italic">p</span> &lt; 0.05, ## indicates <span class="html-italic">p</span> &lt; 0.01 (<span class="html-italic">n</span> = 8).</p>
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<p>Linear discriminant analysis score (<span class="html-italic">n</span> = 8).</p>
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<p>Cow placenta extract ameliorates cyclophosphamide-induced intestinal damage by enhancing the intestinal barrier and immune function and restoring intestinal microbes. Note: The green arrows indicate the effect of CPE treatment, and the red arrows indicate the effect of Cy administration on mice. ↑: upregulation; ↓: downregulation. The figure was created using BioRender.</p>
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21 pages, 16172 KiB  
Article
Taurine Protects against Silica Nanoparticle-Induced Apoptosis and Inflammatory Response via Inhibition of Oxidative Stress in Porcine Ovarian Granulosa Cells
by Fenglei Chen, Jiarong Sun, Rongrong Ye, Tuba Latif Virk, Qi Liu, Yuguo Yuan and Xianyu Xu
Animals 2024, 14(20), 2959; https://doi.org/10.3390/ani14202959 - 14 Oct 2024
Viewed by 245
Abstract
Silica nanoparticles (SNPs) induce reproductive toxicity through ROS production, which significantly limits their application. The protective effects of taurine (Tau) against SNP-induced reproductive toxicity remain unexplored. So this study aims to investigate the impact of Tau on SNP-induced porcine ovarian granulosa cell toxicity. [...] Read more.
Silica nanoparticles (SNPs) induce reproductive toxicity through ROS production, which significantly limits their application. The protective effects of taurine (Tau) against SNP-induced reproductive toxicity remain unexplored. So this study aims to investigate the impact of Tau on SNP-induced porcine ovarian granulosa cell toxicity. In vitro, granulosa cells were exposed to SNPs combined with Tau. The localization of SNPs was determined by TEM. Cell viability was examined by CCK-8 assay. ROS levels were measured by CLSM and FCM. SOD and CAT levels were evaluated using ELISA and qPCR. Cell apoptosis was detected by FCM, and pro-inflammatory cytokine transcription levels were measured by qPCR. The results showed that SNPs significantly decreased cell viability, while increased cell apoptosis and ROS levels. Moreover, SOD and CAT were decreased, while IFN-α, IFN-β, IL-1β, and IL-6 were increased after SNP exposures. Tau significantly decreased intracellular ROS, while it increased SOD and CAT compared to SNPs alone. Additionally, Tau exhibited anti-inflammatory effects and inhibited cell apoptosis. On the whole, these findings suggest that Tau mitigates SNP-induced cytotoxicity by reducing oxidative stress, inflammatory response, and cell apoptosis. Tau may be an effective strategy to alleviate SNP-induced toxicity and holds promising application prospects in the animal husbandry and veterinary industry. Full article
(This article belongs to the Special Issue Developmental and Reproductive Toxicity of Nanoparticles in Animals)
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<p>Characteristics and cytotoxicity of SNPs: (<b>A</b>) Representative TEM image of SNPs. (<b>B</b>) Size distribution of SNPs. (<b>C</b>) CCK-8 assay. (<b>D</b>) LDH leakage assay. ** <span class="html-italic">p</span> &lt; 0.01, and **** <span class="html-italic">p</span> &lt; 0.0001 vs. Control.</p>
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<p>Cellular uptake and distribution of SNPs in porcine ovarian granulosa cells: (<b>A</b>) Representative TEM image in the control group. The cells were not exposed to SNPs. (<b>B</b>) Zoomed-in image of the white box in (<b>A</b>). (<b>C</b>) Zoomed-in image of the black box in (<b>B</b>). (<b>D</b>) Representative TEM image in the SNP-exposed group. The cells were exposed to 400 μg/mL SNPs for 48 h. (<b>E</b>) Zoomed-in image of the white box in (<b>D</b>). (<b>F</b>) Zoomed-in image of the black box in (<b>E</b>). Black arrows indicate vesicles in the cytoplasm and white arrows indicate SNPs. Scale bar, 5 μm (<b>A</b>,<b>D</b>), 2 μm (<b>B</b>,<b>E</b>), and 1 μm (<b>C</b>,<b>F</b>).</p>
Full article ">Figure 3
<p>SNP-induced oxidative stress in porcine ovarian granulosa cells. (<b>A</b>) Representative images of ROS staining by CLSM. Ovarian granulosa cells were exposed to control (<b>a</b>), 200 (<b>b</b>), 400 (<b>c</b>), and 800 (<b>d</b>) μg/mL SNPs for 48 h. Green indicates fluorescence of ROS and blue indicates the nucleus of ovarian granulosa cells. Scale bar, 30 μm. (<b>B</b>) Quantitative analysis of the intracellular ROS levels by FCM. (<b>C</b>) Corresponding analysis of fluorescence intensity in Figure (<b>B</b>). (<b>D</b>) Quantitative analysis of CAT mRNA levels by qPCR. (<b>E</b>) Quantitative analysis of SOD mRNA levels by qPCR. (<b>F</b>) Quantitative analysis of CAT enzyme activity by ELISA. (<b>G</b>) Quantitative analysis of SOD enzyme activity by ELISA. * <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 vs. Control.</p>
Full article ">Figure 3 Cont.
<p>SNP-induced oxidative stress in porcine ovarian granulosa cells. (<b>A</b>) Representative images of ROS staining by CLSM. Ovarian granulosa cells were exposed to control (<b>a</b>), 200 (<b>b</b>), 400 (<b>c</b>), and 800 (<b>d</b>) μg/mL SNPs for 48 h. Green indicates fluorescence of ROS and blue indicates the nucleus of ovarian granulosa cells. Scale bar, 30 μm. (<b>B</b>) Quantitative analysis of the intracellular ROS levels by FCM. (<b>C</b>) Corresponding analysis of fluorescence intensity in Figure (<b>B</b>). (<b>D</b>) Quantitative analysis of CAT mRNA levels by qPCR. (<b>E</b>) Quantitative analysis of SOD mRNA levels by qPCR. (<b>F</b>) Quantitative analysis of CAT enzyme activity by ELISA. (<b>G</b>) Quantitative analysis of SOD enzyme activity by ELISA. * <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 vs. Control.</p>
Full article ">Figure 4
<p>SNP-activated inflammatory response in porcine ovarian granulosa cells: (<b>A</b>–<b>D</b>) Quantitative analysis of the mRNA levels for IFN-α (<b>A</b>), IFN-β (<b>B</b>), IL-1β (<b>C</b>), and IL-6 (<b>D</b>) by qPCR. * <span class="html-italic">p</span> &lt; 0.05 and *** <span class="html-italic">p</span> &lt; 0.001 vs. Control.</p>
Full article ">Figure 5
<p>Cell apoptosis was activated in porcine ovarian granulosa cells after SNP exposures: (<b>A</b>) The apoptotic rate was determined by FCM. Q1-UL quadrant represents cell death caused by mechanical damage or necrotic cells, Q1-UR quadrant represents late apoptotic cells, Q1-LL quadrant represents the normal cells, and Q1-LR quadrant represents early apoptotic cells. (<b>B</b>) Quantification of the apoptotic rate. (<b>C</b>–<b>F</b>) Quantitative analysis of the mRNA levels for BCL-2 (<b>C</b>), BAX (<b>D</b>), Caspase-3 (<b>E</b>), and PARP (<b>F</b>) by qPCR. (<b>G</b>) Detection of BCL-2, BAX, cleaved Caspase-3, and cleaved PARP expressions by Western Blot. (<b>H</b>) Quantitative analysis of the band intensity for BCL-2, BAX, cleaved Caspase-3, and cleaved PARP. * <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 vs. Control.</p>
Full article ">Figure 5 Cont.
<p>Cell apoptosis was activated in porcine ovarian granulosa cells after SNP exposures: (<b>A</b>) The apoptotic rate was determined by FCM. Q1-UL quadrant represents cell death caused by mechanical damage or necrotic cells, Q1-UR quadrant represents late apoptotic cells, Q1-LL quadrant represents the normal cells, and Q1-LR quadrant represents early apoptotic cells. (<b>B</b>) Quantification of the apoptotic rate. (<b>C</b>–<b>F</b>) Quantitative analysis of the mRNA levels for BCL-2 (<b>C</b>), BAX (<b>D</b>), Caspase-3 (<b>E</b>), and PARP (<b>F</b>) by qPCR. (<b>G</b>) Detection of BCL-2, BAX, cleaved Caspase-3, and cleaved PARP expressions by Western Blot. (<b>H</b>) Quantitative analysis of the band intensity for BCL-2, BAX, cleaved Caspase-3, and cleaved PARP. * <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 vs. Control.</p>
Full article ">Figure 6
<p>Tau inhibited SNP-induced oxidative stress in porcine ovarian granulosa cells. Ovarian granulosa cells were exposed to SNPs in the absence or presence of 10 mM Tau for 48 h: (<b>A</b>) Representative images of ROS staining by CLSM. Ovarian granulosa cells were exposed to control (<b>a</b>), 200 μg/mL SNP group (<b>b</b>), 400 μg/mL SNP (<b>c</b>), 800 μg/mL SNP (<b>d</b>), 10 mM Tau (<b>e</b>), 200 μg/mL SNP combined with 10 mM Tau (<b>f</b>), 400 μg/mL SNP combined with 10 mM Tau (<b>g</b>), and 800 μg/mL SNP combined with 10 mM Tau (<b>h</b>). Green indicates fluorescence of ROS and blue indicates the nucleus of ovarian granulosa cells. Scale bar, 30 μm. (<b>B</b>) Quantitative analysis of the intracellular ROS levels by FACS. (<b>C</b>) Corresponding analysis of the fluorescence intensity in Figure (<b>B</b>). (<b>D</b>) Quantitative analysis of CAT mRNA levels by qPCR. (<b>E</b>) Quantitative analysis of SOD mRNA levels by qPCR. (<b>F</b>) Quantitative analysis of CAT enzyme activity by ELISA. (<b>G</b>) Quantitative analysis of SOD enzyme activity by ELISA. * <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 vs. Control. <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05 and <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 vs. SNP-exposed group.</p>
Full article ">Figure 6 Cont.
<p>Tau inhibited SNP-induced oxidative stress in porcine ovarian granulosa cells. Ovarian granulosa cells were exposed to SNPs in the absence or presence of 10 mM Tau for 48 h: (<b>A</b>) Representative images of ROS staining by CLSM. Ovarian granulosa cells were exposed to control (<b>a</b>), 200 μg/mL SNP group (<b>b</b>), 400 μg/mL SNP (<b>c</b>), 800 μg/mL SNP (<b>d</b>), 10 mM Tau (<b>e</b>), 200 μg/mL SNP combined with 10 mM Tau (<b>f</b>), 400 μg/mL SNP combined with 10 mM Tau (<b>g</b>), and 800 μg/mL SNP combined with 10 mM Tau (<b>h</b>). Green indicates fluorescence of ROS and blue indicates the nucleus of ovarian granulosa cells. Scale bar, 30 μm. (<b>B</b>) Quantitative analysis of the intracellular ROS levels by FACS. (<b>C</b>) Corresponding analysis of the fluorescence intensity in Figure (<b>B</b>). (<b>D</b>) Quantitative analysis of CAT mRNA levels by qPCR. (<b>E</b>) Quantitative analysis of SOD mRNA levels by qPCR. (<b>F</b>) Quantitative analysis of CAT enzyme activity by ELISA. (<b>G</b>) Quantitative analysis of SOD enzyme activity by ELISA. * <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 vs. Control. <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05 and <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 vs. SNP-exposed group.</p>
Full article ">Figure 7
<p>Tau inhibited SNP-activated inflammatory response in porcine ovarian granulosa cells. Ovarian granulosa cells were exposed to SNPs in the absence or presence of 10 mM Tau for 48 h: (<b>A</b>–<b>D</b>) Quantitative analysis of the mRNA levels for IFN-α (<b>A</b>), IFN-β (<b>B</b>), IL-1β (<b>C</b>), and IL-6 (<b>D</b>) by qPCR. * <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 vs. Control. <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01, and <sup>###</sup> <span class="html-italic">p</span> &lt; 0.001 vs. SNP-exposed group.</p>
Full article ">Figure 7 Cont.
<p>Tau inhibited SNP-activated inflammatory response in porcine ovarian granulosa cells. Ovarian granulosa cells were exposed to SNPs in the absence or presence of 10 mM Tau for 48 h: (<b>A</b>–<b>D</b>) Quantitative analysis of the mRNA levels for IFN-α (<b>A</b>), IFN-β (<b>B</b>), IL-1β (<b>C</b>), and IL-6 (<b>D</b>) by qPCR. * <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 vs. Control. <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01, and <sup>###</sup> <span class="html-italic">p</span> &lt; 0.001 vs. SNP-exposed group.</p>
Full article ">Figure 8
<p>Tau inhibited SNP-induced cell apoptosis in porcine ovarian granulosa cells. Ovarian granulosa cells were exposed to SNPs in the absence or presence of 10 mM Tau for 48 h: (<b>A</b>) The apoptotic rate was determined by FCM. Q1-UL quadrant represents cell death caused by mechanical damage or necrotic cells, Q1-UR quadrant represents late apoptotic cells, Q1-LL quadrant represents the normal cells, and Q1-LR quadrant represents early apoptotic cells. (<b>B</b>) Quantification of the apoptotic rate. (<b>B</b>) Quantification of the apoptotic rate. (<b>C</b>–<b>F</b>) Quantitative analysis of the mRNA levels for BCL-2 (<b>C</b>), BAX (<b>D</b>), Caspase-3 (<b>E</b>), and PARP (F) by qPCR. (<b>G</b>) Detection of BCL-2, BAX, cleaved Caspase-3, and cleaved PARP expressions by Western Blot. (<b>H</b>) Quantitative analysis of the band intensity for BCL-2, BAX, cleaved Caspase-3, and cleaved PARP. * <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 vs. Control. <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01, and <sup>###</sup> <span class="html-italic">p</span> &lt; 0.001 vs. SNP-exposed group.</p>
Full article ">Figure 8 Cont.
<p>Tau inhibited SNP-induced cell apoptosis in porcine ovarian granulosa cells. Ovarian granulosa cells were exposed to SNPs in the absence or presence of 10 mM Tau for 48 h: (<b>A</b>) The apoptotic rate was determined by FCM. Q1-UL quadrant represents cell death caused by mechanical damage or necrotic cells, Q1-UR quadrant represents late apoptotic cells, Q1-LL quadrant represents the normal cells, and Q1-LR quadrant represents early apoptotic cells. (<b>B</b>) Quantification of the apoptotic rate. (<b>B</b>) Quantification of the apoptotic rate. (<b>C</b>–<b>F</b>) Quantitative analysis of the mRNA levels for BCL-2 (<b>C</b>), BAX (<b>D</b>), Caspase-3 (<b>E</b>), and PARP (F) by qPCR. (<b>G</b>) Detection of BCL-2, BAX, cleaved Caspase-3, and cleaved PARP expressions by Western Blot. (<b>H</b>) Quantitative analysis of the band intensity for BCL-2, BAX, cleaved Caspase-3, and cleaved PARP. * <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 vs. Control. <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01, and <sup>###</sup> <span class="html-italic">p</span> &lt; 0.001 vs. SNP-exposed group.</p>
Full article ">
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