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23 pages, 8722 KiB  
Article
16S rRNA Sequencing and Metabolomics to Analyze Correlation Between Fecal Flora and Metabolites of Squabs and Parent Pigeons
by Xiaobin Li, Shengchen Zheng, Haiying Li, Jiajia Liu, Fan Yang, Xiaoyu Zhao and Yafei Liang
Animals 2025, 15(1), 74; https://doi.org/10.3390/ani15010074 (registering DOI) - 1 Jan 2025
Viewed by 146
Abstract
Intestinal microorganisms are essential for maintaining homeostasis, health, and development, playing a critical role in nutrient digestion, growth, and exercise performance in pigeons. In young pigeons, the gut microbiota is primarily acquired through pigeon milk, meaning the microbial composition of parent pigeons directly [...] Read more.
Intestinal microorganisms are essential for maintaining homeostasis, health, and development, playing a critical role in nutrient digestion, growth, and exercise performance in pigeons. In young pigeons, the gut microbiota is primarily acquired through pigeon milk, meaning the microbial composition of parent pigeons directly influences microbial colonization in squabs. However, research on the correlation between the gut microbial diversity of parent pigeons and their offspring remains scarce. This study investigates the fecal microbiota and metabolites of 10 pairs of parent pigeons and 20 squabs raised under a 2 + 2 system. Fecal samples were collected at 15 days of age, and differences in the microbiota and metabolites between the two groups were analyzed using 16S rRNA sequencing and LC-MS/MS. The results showed the following: (1) Squabs exhibited significantly lower α diversity, with a reduction in their Chao1 index and observed OTUs compared to the parent pigeons. (2) Firmicutes dominated the fecal microbiota in both groups, but parent pigeon feces showed a notably higher abundance of Proteobacteria. At the family level, 10 distinct families were identified, with 9 at the genus level and 4 at the species level. (3) A LEfSe analysis identified 16 significantly different bacterial species in the parent pigeons and 7 in the squabs. Functional gene abundance was highest in the metabolism, genetic information processing, and environmental information processing pathways. (4) An LC-MS/MS analysis in cationic mode identified 218 metabolites, with 139 upregulated and 79 downregulated in the squabs relative to the parents. These metabolites were primarily concentrated in five functional categories and enriched in 33 pathways, 2 of which showed significant differences. In conclusion, significant differences in both the α and β diversity of fecal microbiota were observed between squabs and parent pigeons, with similar bacterial species but marked differences in abundance. Metabolite analysis revealed greater richness in the parent pigeon feces. These findings suggest that future gut modulation using beneficial bacteria, such as probiotics, could potentially enhance host health based on microbial and metabolite compositions. Full article
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<p>OTU-based Wayne graph analysis and UPGMA clustering tree. (<b>A</b>): Wayne diagram analysis; (<b>B</b>): UPGMA clustering tree based on OTUs.</p>
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<p>Principal component analysis (PCA) and principal coordinate analysis (PCoA). (<b>A</b>): PCA; (<b>B</b>): PCoA.</p>
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<p>Effects of the relative abundance of the fecal flora. (<b>A</b>,<b>C</b>,<b>E</b>,<b>G</b>): Influence of the relative abundance of the fecal flora at the phylum, family, genus, and species levels; (<b>B</b>,<b>D</b>,<b>F</b>,<b>H</b>): <span class="html-italic">T</span>-test for the effects of the relative abundance of the fecal flora at the phylum, family, genus, and species levels.</p>
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<p>Effects of the relative abundance of the fecal flora. (<b>A</b>,<b>C</b>,<b>E</b>,<b>G</b>): Influence of the relative abundance of the fecal flora at the phylum, family, genus, and species levels; (<b>B</b>,<b>D</b>,<b>F</b>,<b>H</b>): <span class="html-italic">T</span>-test for the effects of the relative abundance of the fecal flora at the phylum, family, genus, and species levels.</p>
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<p>Significant differences were assessed using Linear Discriminant Analysis Effect Size (LEfSe), with a linear discriminant analysis score &gt; 4 and a <span class="html-italic">p</span>-value &lt; 0.05. (<b>A</b>) Histogram of LDA scores showing significant bacterial taxa that differ between the PP (red) and SP (green) groups; (<b>B</b>) Cladogram showing the phylogenetic relationships of significantly different taxa between the PP (red) and SP (green) groups.</p>
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<p>Functional prediction of Tax4Fun in feces. (<b>A</b>): Functional heatmap of fecal flora in SP and PP groups. (<b>B</b>): Functional prediction of fecal flora using Tax4Fun in SP and PP groups. (<b>C</b>): Significant <span class="html-italic">T</span>-test analysis of fecal flora in SP and PP groups.</p>
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<p>Functional prediction of Tax4Fun in feces. (<b>A</b>): Functional heatmap of fecal flora in SP and PP groups. (<b>B</b>): Functional prediction of fecal flora using Tax4Fun in SP and PP groups. (<b>C</b>): Significant <span class="html-italic">T</span>-test analysis of fecal flora in SP and PP groups.</p>
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<p>Correlation analysis and PCA of QC samples. (<b>A</b>): Correlation analysis of QC samples in positive ion mode. (<b>B</b>): Correlation analysis of QC samples in negative ion mode. (<b>C</b>): PCA of samples and QC samples in positive ion mode based on relative metabolite quantification. (<b>D</b>): PCA of samples and QC samples in negative ion mode based on relative metabolite quantification.</p>
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<p>Classification of fecal metabolites. (<b>A</b>): Classification of metabolites in feces in positive ion mode. (<b>B</b>): Classification of metabolites in feces in negative ion mode.</p>
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<p>Volcano plot of the differential metabolites in the feces. (<b>A</b>): Volcano plot of differential metabolites in the feces of the SP and PP groups in positive ion mode. (<b>B</b>): Volcano plot of differential metabolites in the feces of the SP and PP groups in negative ion mode.</p>
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<p>Correlation analysis of the differential metabolites in the feces. (<b>A</b>): Correlation analysis of differential metabolites in the feces of the SP and PP groups in positive ion mode. (<b>B</b>): Correlation analysis of differential metabolites in the feces of the SP and PP groups in negative ion mode.</p>
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<p>KEGG classification of differential metabolites in feces. (<b>A</b>): Pathway annotation for KEGG in positive ion mode. (<b>B</b>): Pathway annotation for KEGG in negative ion mode.</p>
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<p>KEGG enrichment pathway analysis of the differential metabolites in the feces. (<b>A</b>): Enrichment results of KEGG in the feces of the SP and PP groups in positive ion mode. (<b>B</b>): Enrichment results of KEGG in the feces of the SP and PP groups in negative ion mode.</p>
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<p>Correlation analysis of the differential metabolites in the feces. (<b>A</b>): Correlation analysis results of the differential metabolites in the feces of the SP and PP groups in positive ion mode. (<b>B</b>): Correlation analysis results of the differential metabolites in the feces of the SP and PP groups in negative ion mode. An asterisk (*) indicates significant relevance in the correlation analysis.</p>
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22 pages, 6439 KiB  
Article
Role of Increasing Body Mass Index in Gut Barrier Dysfunction, Systemic Inflammation, and Metabolic Dysregulation in Obesity
by Fatima Maqoud, Francesco Maria Calabrese, Giuseppe Celano, Domenica Mallardi, Francesco Goscilo, Benedetta D’Attoma, Antonia Ignazzi, Michele Linsalata, Gabriele Bitetto, Martina Di Chito, Pasqua Letizia Pesole, Arianna Diciolla, Carmen Aurora Apa, Giovanni De Pergola, Gianluigi Giannelli, Maria De Angelis and Francesco Russo
Nutrients 2025, 17(1), 72; https://doi.org/10.3390/nu17010072 (registering DOI) - 28 Dec 2024
Viewed by 382
Abstract
Aims: This study explores the link between body mass index (BMI), intestinal permeability, and associated changes in anthropometric and impedance parameters, lipid profiles, inflammatory markers, fecal metabolites, and gut microbiota taxa composition in participants having excessive body mass. Methods: A cohort of 58 [...] Read more.
Aims: This study explores the link between body mass index (BMI), intestinal permeability, and associated changes in anthropometric and impedance parameters, lipid profiles, inflammatory markers, fecal metabolites, and gut microbiota taxa composition in participants having excessive body mass. Methods: A cohort of 58 obese individuals with comparable diet, age, and height was divided into three groups based on a priori clustering analyses that fit with BMI class ranges: Group I (25–29.9), Group II (30–39.9), and Group III (>40). Anthropometric and clinical parameters were assessed, including plasma C-reactive protein and cytokine profiles as inflammation markers. Intestinal permeability was measured using a multisaccharide assay, with fecal/serum zonulin and serum claudin-5 and claudin-15 levels. Fecal microbiota composition and metabolomic profiles were analyzed using a phylogenetic microarray and GC-MS techniques. Results: The statistical analyses of the clinical parameters were based on the full sample set, whereas a subset composed of 37 randomized patients was inspected for the GC/MS metabolite profiling of fecal specimens. An increase in potentially pro-inflammatory bacterial genera (e.g., Slackia, Dorea, Granulicatella) and a reduction in beneficial genera (e.g., Adlercreutzia, Clostridia UCG-014, Roseburia) were measured. The gas chromatography/mass spectrometry analysis of urine samples evidenced a statistically significant increase in m-cymen-8-ol, 1,3,5-Undecatriene, (E, Z) and a decreased concentration of p-cresol, carvone, p-cresol, and nonane. Conclusions: Together, these data demonstrated how an increased BMI led to significant changes in inflammatory markers, intestinal barrier metabolites, glucose metabolism, endocrine indicators, and fecal metabolomic profiles that can indicate a different metabolite production from gut microbiota. Our findings suggest that targeting intestinal permeability may offer a therapeutic approach to prevent and manage obesity and related metabolic complications, reinforcing the link between gut barrier function and obesity. Full article
(This article belongs to the Section Nutrition and Obesity)
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<p>A priori group stratification resulting from the DAPC analysis run using the clinical/biochemical and anthropometric complete parameter matrix obtained from the 58-patient set. Used eigen values have been colored in dark grey.</p>
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<p>Clinical/anthropometric a posterior sample stratification in the DAPC analysis. The a posterior group assignment was based on BMI grouping, such as overweight, type 1, 2, and type 3 obesity.</p>
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<p>DAPC loading and assignment plot based on the 58-patient sample’s clinical/biochemical and anthropometric parameters. (<b>A</b>) DAPC loading plot reporting the clinical/anthropometric variables that most impacted cluster separation. An arbitrary 0.02 threshold is used to show the above threshold variables. (<b>B</b>) The cell matrix reports the fitting between the “a priori” and the “a posterior” assignments.</p>
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<p>Comparison of glucose metabolism and endocrine indicators based on DAPC BMI stratification in overweight and obese subjects. Data are expressed as the mean ± standard deviation, and statistical analyses were performed using an ANOVA followed by a Tukey’s post hoc test. Statistically significant comparisons (<span class="html-italic">p</span> &lt; 0.05) are highlighted by a bold line and marked with an asterisk. Path coefficients and significance: * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01. Measured parameters include (<b>a</b>) fasting plasma glucose, (<b>b</b>) fasting insulin, (<b>c</b>) HOMA-IR, (<b>d</b>) fasting obestatin, (<b>e</b>) fasting ghrelin.</p>
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<p>Levels of inflammatory markers in the 58 overweight and obese individuals grouped according to BMI categories. Data are expressed as the mean ± standard deviation, and statistical analyses were performed using an ANOVA followed by a Tukey’s post hoc test. Statistically significant comparisons (<span class="html-italic">p</span> &lt; 0.05) are highlighted by a bold line and marked with an asterisk. Path coefficients and significance: * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01. Inflammatory marker sub-panels include (<b>a</b>) PCR, (<b>b</b>) IL-6, (<b>c</b>) IL-8, (<b>d</b>) IL-10, (<b>e</b>) TNF-alpha.</p>
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<p>Levels of biomarkers related to intestinal barrier function and integrity measured in the set composed of 58 patients. Data are expressed as the mean ± standard deviation, and statistical analyses were performed using an ANOVA followed by a Tukey’s post hoc test. Statistically significant comparisons (<span class="html-italic">p</span> &lt; 0.05) are highlighted by a bold line and marked with an asterisk. Path coefficients and significance: * <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; 0.0001. Sub-panels include (<b>a</b>) lac/man ration, IFAB-2 (<b>b</b>), (<b>c</b>) serum claudin 5.</p>
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<p>Linear regression analysis assessing the relationship between BMI and the intestinal permeability marker I-FABP.</p>
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<p>Levels of urinary indole, urinary skatole, and serum lipopolysaccharide (LPS) in the study cohort where the 58 patients have been grouped based on the DAPC BMI clusters. Data are expressed as the mean ± standard deviation, and statistical analyses were performed using an ANOVA followed by a Tukey’s post hoc test. <span class="html-italic">p</span>-values indicating significant differences (<span class="html-italic">p</span> &lt; 0.05) are highlighted by a bold line and marked with an asterisk. Path coefficients and significance: * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01. The cut-off levels indicating dysbiosis were set at 20 mg/L for indican and 20 μg/L for skatole. Sub-panels of urinary markers include (<b>a</b>) indican, (<b>b</b>) skatole and, (<b>c</b>) LPS.</p>
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<p>Statistically significant urinary VOCs detected by metabolomic (GC/MS) analyses on 37 patient samples. Fold change analysis was joined with a Welch’s corrected test (BH multiple correction) based on taxa at the genus level. A dot size increase is representative of lower <span class="html-italic">p</span>-values. Log2(FC) values range from gray (lower) to red (higher). Increased and decreased VOC concentrations are relative to the first comparison member, i.e., Group II versus Group I (<b>A</b>). (<b>B</b>) Pairwise comparison between Group III and Group I samples.</p>
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<p>Statistically significant taxa volcano plot. Fold change analysis was joined with a Welch’s corrected test (BH multiple correction) based on taxa at the genus level. A dot size increase is representative of lower <span class="html-italic">p</span>-values. Log2(FC) values range from gray (lower) to red (higher). Increased and decreased VOC concentrations are relative to the first comparison member, i.e., Group II (<b>A</b>) versus Group I (<b>B</b>) pairwise comparison between Group III and Group II samples. (<b>C</b>) Comparison between Group II and Group I samples.</p>
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<p>Pearson’s correlations among the VOC, taxa, and clinical variables. Statistically significant VOC (black), clinical/anthropometrical (dark orange), and taxa (dark green) variable sets have been correlated via a Pearson’s test. Only inter-group variable correlations with a <span class="html-italic">p</span>-value equal/lower than 0.05 have been shown, and only correlations greater than 0.6 were flagged in bold black font. Positive and negative correlations were reported as red and blue bubbles, respectively. Based on inter- and intra-group variable comparison (taxa, VOC, and clinical variables), bubbles were placed on a light aqua or yellow background.</p>
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26 pages, 18252 KiB  
Article
Amelioration of Inflammation in Rats with Experimentally Induced Asthma by Spenceria ramalana Trimen Polyphenols via the PI3K/Akt Signaling Pathway
by Zhaobin Xia, Xing Zhao, Lu Wang, Lin Huang, Yanwen Yang, Xiangyu Yin, Luyu He, Yuebumo Aga, Ankaer Kahaer, Shiyu Yang, Lili Hao and Chaoxi Chen
Int. J. Mol. Sci. 2025, 26(1), 165; https://doi.org/10.3390/ijms26010165 - 28 Dec 2024
Viewed by 275
Abstract
Asthma is a chronic inflammatory respiratory disease that affects millions globally and poses a serious public health challenge. Current therapeutic strategies, including corticosteroids, are constrained by variable patient responses and adverse effects. In this study, a polyphenolic extract derived from the Tibetan medicinal [...] Read more.
Asthma is a chronic inflammatory respiratory disease that affects millions globally and poses a serious public health challenge. Current therapeutic strategies, including corticosteroids, are constrained by variable patient responses and adverse effects. In this study, a polyphenolic extract derived from the Tibetan medicinal plant Spenceria ramalana Trimen (SRT) was employed and shown to improve experimentally (ovalbumin + cigarette smoke, OVA + CS) induced asthma in rats. Initially, the potential therapeutic mechanism of the polyphenolic components in SRT on OVA + CS-induced asthma was predicated by network pharmacology analysis. Subsequently, in vivo experiments identified that SRT polyphenols exhibit significant anti-asthmatic activities, primarily mediated by lowering inflammatory cell counts such as the WBC (white blood cell), eosinophils, and neutrophils, decreasing the expression of inflammatory cytokines (IL-4, IL-5, IL-13, and TNF-α), alleviating lung histological damage (reduced inflammation, collagen deposition, and mucus secretion), and enhancing the epithelial barrier integrity (upregulation of ZO-1, occludin, and claudin-1). Additionally, SRT polyphenols downregulated the PI3K/Akt (Phosphoinositide 3-kinase/protein kinase B) signaling pathway, improved gut microbiota disruption, and regulated fecal metabolites (glucose-6-glutamate, PS (16:0/0:0), 8-aminocaprylic acid, galactonic acid, Ascr#10, 2,3,4,5,6,7-hexahydroxyheptanoic acid, phosphodimethylethanolamine, muramic acid, 9-oxohexadeca-10e-enoic acid, and sedoheptulose) in asthmatic rats. In conclusion, SRT polyphenols exerted multifaceted protective effects against OVA + CS-induced asthma in rats, highlighting their potential value in preventing asthma via the PI3K/Akt signaling pathway. Full article
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<p>Network pharmacology analysis. (<b>A</b>) Flow chart for screening of potential active components. (<b>B</b>) Asthma-related targets from OMIM, Drugbank, TTD, DisGeNET, and GeneCards. (<b>C</b>) Venn diagram of 143 overlapping targets between SRT polyphenols and asthma. (<b>D</b>) PPI network of 143 overlapping targets. (<b>E</b>) GO function enrichment analysis. (<b>F</b>) KEGG enrichment analysis. (<b>G</b>) Component–target–pathway network (green nodes represent pathways, red nodes represent SRT polyphenols, and blue nodes represent targets). (<b>H</b>) Key components predicted for treatment of asthma.</p>
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<p>The establishment of an OVA + CS-induced asthma model. (<b>A</b>) Experimental design and dosing regimen. (<b>B</b>) Gross lesions in the lungs of asthmatic rats (white areas represent lesions and histological edema). (<b>C</b>) The W/D ratio of the lung. ** <span class="html-italic">p</span> &lt; 0.01 and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>SRT polyphenols attenuated inflammatory responses in OVA + CS-induced asthmatic rats. (<b>A</b>–<b>C</b>) WBC count, percentages of eosinophils, and neutrophils in BALF. (<b>D</b>–<b>G</b>) ELISA-measured cytokine concentrations of IL-4, IL-5, IL-13, and TNF-α in serum. (<b>H</b>–<b>K</b>) Relative mRNA expression of <span class="html-italic">IL-4</span>, <span class="html-italic">IL-5</span>, <span class="html-italic">IL-13</span>, and <span class="html-italic">TNF-α</span> in lung tissues. Data are presented as mean ± SD. (n = 5–6). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>SRT polyphenols promoted lung epithelial barrier repair. (<b>A</b>) EB staining assay. (<b>B</b>) Quantification of dye in lung tissues. (<b>C</b>–<b>F</b>) Representative Western blot images and bar graphs showing relative expressions of ZO-1, occludin, and claudin-1. Data are presented as mean ± SD. (n = 3). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Effects of SRT polyphenols on airway remodeling. (<b>A</b>–<b>D</b>) H&amp;E, Masson, and PAS staining of lung tissues with quantitative evaluation of inflammatory response, collagen deposition, and mucus secretion (black arrows in H&amp;E staining indicate inflammatory cell infiltration; blue areas around airways in Masson staining represent collagen deposition; red arrows in PAS staining indicate mucus). (<b>E</b>–<b>H</b>) Immunohistochemical analysis for MMP9 and <span class="html-italic">α</span>-SMA with quantitative analysis. Data are presented as mean ± SD. (n = 3). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>SRT polyphenols modulated the PI3K/Akt signaling pathways. (<b>A</b>–<b>E</b>) Representative Western blot images and bar graphs showing the relative expression levels of PIK3CA, Akt, and p-Akt. Data are presented as mean ± SD. (n = 3). * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>SRT polyphenols regulated the overall structure of the gut microbiota. (<b>A</b>) Petal plot of the ASV distribution. (<b>B</b>) Phylogenetic tree of the top 50 species. (<b>C</b>) Principal coordinates analysis (PCoA). (<b>D</b>) Hierarchical cluster analysis. Data are presented as mean ± SD. (n = 5). * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>SRT polyphenols altered the relative abundance of the gut microbiota. Microbial community structure at the (<b>A</b>) phylum and <b>(B</b>) genus levels. (<b>C</b>–<b>E</b>) Representative differentially enriched species at the genus level: <span class="html-italic">Prevotella</span>, <span class="html-italic">Romboutsia,</span> and <span class="html-italic">Parabacteroides</span>. Data are presented as mean ± SD. (n = 5). * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>LEfSe analysis. (<b>A</b>) Differential species score chart and (<b>B</b>) differential species annotation branching diagram (n = 5).</p>
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<p>Fecal metabolome analysis. (<b>A</b>–<b>C</b>) PCA analysis, volcano plots of differentially expressed metabolites, and KEGG enrichment analysis for NC vs. MO. (<b>D</b>–<b>F</b>) PCA analysis, volcano plots of differentially expressed metabolites, and KEGG enrichment analysis for MO vs. SRTH. (<b>G</b>,<b>H</b>) Overlapping differentially expressed metabolites and expression levels between NC vs. MO and MO vs. SRTH. (<b>I</b>) Spearman’s correlation analysis of key differentially expressed metabolites with asthma indicators and gut microbiota. Data are presented as mean ± SD. (n = 5). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>The underlying mechanism of the protective effect of SRT polyphenols against OVA + CS-induced asthma.</p>
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18 pages, 3062 KiB  
Article
Dietary Supplementation with Methylsulfonylmethane and Myo-Inosito Supports Hair Quality and Fecal Microbiome in Poodles
by Jie Zhang, Dan Guo, Limeng Zhang, Deping Li and Baichuan Deng
Animals 2024, 14(24), 3643; https://doi.org/10.3390/ani14243643 - 17 Dec 2024
Viewed by 316
Abstract
This study aimed to investigate the effects of dietary supplementation with methylsulfonylmethane (MSM) and myo-inositol (MI) on hair quality, fecal microbiota, and metabolome in poodles. Thirty-two adult poodles categorized based on initial body weight and sex were randomly assigned to four groups. These [...] Read more.
This study aimed to investigate the effects of dietary supplementation with methylsulfonylmethane (MSM) and myo-inositol (MI) on hair quality, fecal microbiota, and metabolome in poodles. Thirty-two adult poodles categorized based on initial body weight and sex were randomly assigned to four groups. These groups (designated the CON, MSM, MI, and MSM + MI groups) received a basal diet, the same diet supplemented with 0.2% MSM + 0% MI, the same diet supplemented with 0% MSM + 0.2% MI, or the same diet supplemented with 0.2% MSM + 0.2% MI, respectively. The study lasted for 65 days. During the entire study period, body weight, average daily weight gain, feed intake, energy intake, and fecal output were normal in all the animals and did not differ significantly among the treatment groups. Hair scale thickness was lower in the MI and MSM + MI groups than in the CON group on Day 65 (p < 0.05). An amino acid analysis of the hair revealed higher sulfur content in the MI and MSM + MI groups on Day 65 than on Day 0 (p < 0.05). Moreover, the poodles in the MSM, MI, and MSM + MI groups presented significantly lower levels of Proteobacteria_unclassified and Candidatus Phytoplasma than did those in the CON group. The relative abundance of Gammaproteobacteria_unclassified was greater in the MSM and MI groups than in the CON group (p < 0.05). The MSM group presented a greater abundance of Glucerabacter than the CON group (p < 0.05). Compared with those in the CON and MSM + MI groups, the abundances of Paramuribaculum and Hafnia in the MSM group were greater (p < 0.05). The abundances of Enterobacter and Kineothrix were greater (p < 0.05) in the MI group than in the CON and MSM + MI groups. The poodles in the MI group presented significantly greater abundances of Bacteroidales_unclassified, Halanaerobium, Mycobacterium, and Erysipelotrichaceae_unclassified than did poodles in the CON, MSM, and MSM + MI groups. Fecal metabolomics analysis revealed that MSM, MI, and MSM + MI treatment markedly affected carbohydrate metabolism. MSM + MI treatment also influenced lipid metabolism. These findings suggest that dietary supplementation with MSM and MI can improve the hair quality of poodles. Full article
(This article belongs to the Topic Research on Companion Animal Nutrition)
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<p>Hair rating criteria for poodles fed MSM- and MI-supplemented diets. All samples were scored in 5 increments using the following scale: (<b>A</b>) 1 = dull, coarse, dry; (<b>B</b>) 2 = medium soft, medium dry; (<b>C</b>) 3 = very soft, normal dry; (<b>D</b>) 4 = medium knots, medium greasy; and (<b>E</b>) 5 = severe knots, very greasy.</p>
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<p>Scale thickness of the hair of poodles fed MSM- and MI-supplemented diets. The scale thickness of the hair of the poodles in the (<b>A</b>) CON, (<b>B</b>) MSM, (<b>C</b>) MI, and (<b>D</b>) MSM + MI groups on Day 0. (<b>E</b>) The scale thickness of the hair of the poodles in the (<b>F</b>) CON, (<b>G</b>) MSM, (<b>H</b>) MI, and (<b>I</b>) MSM + MI groups on Day 65. (<b>J</b>) The scale thickness of the hair of the poodles in the four groups on Day 0 and Day 65. CON, basal diet; MSM, basal diet supplemented with 0.2% MSM; MI, basal diet supplemented with 0.2% MI; and MSM + MI, basal diet supplemented with 0.2% MSM and 0.2% MI. The red arrows indicate the scale thickness of the hair. The symbol (*) indicates statistically significant differences between two groups (* <span class="html-italic">p</span> &lt; 0.05), and the symbol (#) represents a difference (# 0.05 ≤ <span class="html-italic">p</span> &lt; 0.10). The mean values are based on 8 replicates per treatment group and one dog per replicate.</p>
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<p>Scale height of the hair of poodles fed MSM- and MI-supplemented diets. Scale height of the hair of the animals in the (<b>A</b>) CON, (<b>B</b>) MSM, (<b>C</b>) MI, and (<b>D</b>) MSM + MI groups on Day 0. (<b>E</b>) Scale height of the hair of poodles fed diets containing MSM on Day 65. Scale height of the hair of the animals in the (<b>F</b>) CON, (<b>G</b>) MSM, (<b>H</b>) MI, and (<b>I</b>) MSM + MI groups on Day 65. (<b>J</b>) Scale height of the hair of the poodles in the four groups on Day 0 and Day 65. CON, basal diet; MSM, basal diet supplemented with 0.2% MSM; MI, basal diet supplemented with 0.2% MI; and MSM + MI, basal diet supplemented with 0.2% MSM and 0.2% MI. The red arrows indicate the scale height of the hair. The mean values are based on 8 replicates per treatment group and one dog per replicate.</p>
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<p>Scale diameter of the hair of poodles fed MSM- and MI-supplemented diets. Scale diameter of the hair of the poodles in the (<b>A</b>) CON, (<b>B</b>) MSM, (<b>C</b>) MI, and (<b>D</b>) MSM + MI groups on Day 0. (<b>E</b>) Scale diameter of the hair of poodles fed diets containing MSM on Day 0. Scale height of the hair of the poodles in the (<b>F</b>) CON, (<b>G</b>) MSM, (<b>H</b>) MI, and (<b>I</b>) MSM + MI groups on Day 65. (<b>J</b>) Scale diameter of the hair of the poodles in the four groups on Day 0 and Day 65. CON, basal diet; MSM, basal diet supplemented with 0.2% MSM; MI, basal diet supplemented with 0.2% MI; and MSM + MI, basal diet supplemented with 0.2% MSM and 0.2% MI. The red arrows indicate the scale diameter of the hair. The mean values are based on 8 replicates per treatment group and one dog per replicate.</p>
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<p>Fecal microbiota of poodles fed MSM- and MI-supplemented diets. (<b>A</b>) Principal coordinate analysis (PCoA) based on weighted UniFrac distances. (<b>B</b>) Fecal microbial communities predominant at the phylum level. (<b>C</b>) Fecal microbial communities predominant at the genus level. (<b>D</b>,<b>E</b>) LEfSe analysis. CON, basal diet; MSM, basal diet supplemented with 0.2% MSM; MI, basal diet supplemented with 0.2% MI; and MSM + MI, basal diet supplemented with 0.2% MSM and 0.2% MI.</p>
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<p>Fecal metabolomics of poodles fed MSM − and MI − supplemented diets. (<b>A</b>) Score plots from the PCA model among the three groups. (<b>B</b>) Volcano plot. (<b>C</b>) KEGG metabolic pathway enrichment analysis based on differential fecal metabolites. CON, basal diet; MSM, basal diet supplemented with 0.2% MSM; MI, basal diet supplemented with 0.2% MI; and MSM + MI, basal diet supplemented with 0.2% MSM and 0.2% MI.</p>
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14 pages, 6506 KiB  
Article
Comparison of Fecal Microbiota and Metabolites Between Captive and Grazing Male Reindeer
by Fei Zhao, Quanmin Zhao, Songze Li, Yuhang Zhu, Huazhe Si, Jiang Feng and Zhipeng Li
Animals 2024, 14(24), 3606; https://doi.org/10.3390/ani14243606 - 14 Dec 2024
Viewed by 391
Abstract
The reindeer (Rangifer tarandus) is a circumpolar member of the Cervidae family, and has adapted to a harsh environment. Summer is a critical period for reindeer, with peak digestibility facilitating body fat accumulation. The gut microbiota plays a pivotal role in [...] Read more.
The reindeer (Rangifer tarandus) is a circumpolar member of the Cervidae family, and has adapted to a harsh environment. Summer is a critical period for reindeer, with peak digestibility facilitating body fat accumulation. The gut microbiota plays a pivotal role in nutrient metabolism, and is affected by captivity. However, differences in the composition of the gut microbiota and metabolites between captive and grazing reindeer during summer remain poorly understood. Here, we conducted a comparative study of the fecal microbiota and metabolites between captive (n = 6) and grazing (n = 6) male reindeer, using full-length 16S rRNA gene sequencing and gas chromatography–time-of-flight mass spectrometry, respectively. Our results indicated that Prevotella, Phocaeicola, Papillibacter, Muribaculum, and Bacteroides were the predominant genera in the feces of reindeer. However, microbial diversity was significantly higher in captive reindeer compared to their grazing counterparts. Principal coordinate analysis revealed significant differences in the fecal microbiota between captive and grazing reindeer. In captive reindeer, the relative abundances of the genera Clostridium, Paraprevotella, Alistipes, Paludibacter, Lentimicrobium, Paraclostridium, and Anaerovibrio were significantly higher, while those of the genera Prevotella, Phocaeicola, Pseudoflavonifractor, and Lactonifactor were significantly lower. A comparison of predicted functions indicated that pathways involved in fat digestion and absorption, histidine metabolism, lysine biosynthesis, and secondary bile acid biosynthesis were more abundant in captive reindeer, whereas the pathways of fructose and mannose metabolism and propanoate metabolism were less abundant. An untargeted metabolomic analysis revealed that 624 metabolites (e.g., amino acids, lipids, fatty acids, and bile acids) and 645 metabolites (e.g., carbohydrates and purines) were significantly increased in the feces of captive and grazing reindeer, respectively. In conclusion, we unveiled significant differences in fecal microbiota and metabolites between captive and grazing male reindeer, with the results suggesting a potentially enhanced ability to utilize plant fibers in grazing reindeer. Full article
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<p>Microbial community composition and diversity in the feces of captive and grazing reindeer. Microbial community composition in the feces of the Cap and Gra groups at the phylum (<b>A</b>) and genus (<b>B</b>) levels. (<b>C</b>) A comparison of alpha-diversity indices in feces between the Cap and Gra groups. (<b>D</b>) PCoA illustrating the differences in microbial community membership and structure in reindeer feces between the Cap and Gra groups at the OTU level, based on Bray–Curtis dissimilarity, Unweighted UniFrac distance, and Weighted UniFrac distance. * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>The significantly different genera in the feces of captive and grazing reindeer. (<b>A</b>) A Venn diagram illustrating genera that were common and unique to the Cap and Gra groups. (<b>B</b>) A heatmap depicting the significantly different genera in feces between the Cap and Gra groups. Individuals are shaded from blue to red to represent relative abundances (low to high). * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>A comparison of the potential functions of microbes in the feces of captive and grazing reindeer. (<b>A</b>) PCoA illustrating the variation in microbial functions at KEGG level 3, based on the Bray–Curtis dissimilarity matrix, in feces between the Cap and Gra groups. (<b>B</b>) A heatmap showing the significantly different metabolic pathways of fecal microbiota between the Cap and Gra groups. Individuals are shaded from blue to red to indicate relative abundances (low to high). * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Differences in fecal metabolites between captive and grazing reindeer. (<b>A</b>) A pie chart illustrating the classification of identified metabolites in feces. (<b>B</b>) PCA and PLS-DA plots highlighting the differences in fecal metabolites between the Cap (blue) and Gra (red) groups. (<b>C</b>) A comparison of the total concentrations of lipids, fatty acids, bile acids, carbohydrates, purines, pyrimidines, and amino acids between the Cap and Gra groups. (<b>D</b>) Volcano plots depicting the significantly different metabolites in feces between the Cap and Gra groups. (<b>E</b>) A heatmap showing the significantly different metabolites in reindeer feces when comparing the Gra group to the Cap group. Individuals are shaded from yellow to purple to indicate concentrations (low to high). (<b>F</b>) A lollipop chart displaying the enriched metabolic pathways of significantly different metabolites. * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>The co-occurrence of significantly different microbiota and metabolites in the feces of captive (<b>A</b>) and grazing (<b>B</b>) reindeer. The Spearman correlation coefficient (|rho| &gt; 0.8 and <span class="html-italic">p</span> ≤ 0.05) was calculated from the abundances of microbiota and the concentrations of metabolites. Node colors indicate microbiota and metabolites, with yellow and blue edges representing positive and negative correlations, respectively.</p>
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16 pages, 3768 KiB  
Article
Effect of Hydrolyzed Frozen Meat on Diet Palatability, Apparent Digestibility, Immune Response, Fecal Microbiota, and Metabolome in British Shorthair Cats
by Shibin Ye, Zhihao Cao, Shiyan Jian, Limeng Zhang, Baichuan Deng and Jinping Deng
Pets 2024, 1(3), 427-442; https://doi.org/10.3390/pets1030030 - 10 Dec 2024
Viewed by 783
Abstract
Frozen meat is an important source of protein in pet food, and has attracted much attention in recent years. In this study, we compared the effect of meat meal (MM), frozen meat (FM), and hydrolyzed frozen meat (HFM) as ingredients in extruded pet [...] Read more.
Frozen meat is an important source of protein in pet food, and has attracted much attention in recent years. In this study, we compared the effect of meat meal (MM), frozen meat (FM), and hydrolyzed frozen meat (HFM) as ingredients in extruded pet food on its palatability and apparent digestibility, as well as its effects on the immune response, fecal microbiota, and metabolome of British shorthair cats. A total of 24 British shorthair cats were allocated to the MM, FM, and HFM groups according to body weight and gender. The palatability test lasted 4 days and the feeding test lasted 45 days. The results showed that the FM and HFM diets had better palatability than the MM diet (p < 0.05) and significantly improved dry matter and crude protein digestibility (p < 0.05). The serum IL-10 level was significantly higher in the HFM group compared to the MM and FM groups (p < 0.05). The serum IgM levels were also found to be significantly higher in the FM group compared to the MM and HFM groups (p < 0.05). The blood urea nitrogen/creatinine ratio was significantly lower in the HFM and FM groups than in the MM group (p < 0.05). Cats fed HFM had a higher abundance of fecal Actinobacteria and Bifidobacterium and a lower content of Bacteroidota (p < 0.05). Furthermore, serum metabolomics analysis revealed that the tryptophan (Trp) metabolism and bile acid metabolism were affected by HFM. Overall, FM and HFM were better for the cat’s health than meat meal, but they also have some potential risks. Full article
(This article belongs to the Topic Research on Companion Animal Nutrition)
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<p>Schematic representation of the study design. Group1 was fed MM and FM (<span class="html-italic">n</span> = 8), Group2 was fed MM and HFM (<span class="html-italic">n</span> = 8), and Group3 was fed FM and HFM (<span class="html-italic">n</span> = 8).</p>
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<p>Intake ratio (%) (<b>A</b>) and first consumption choice (%) (<b>B</b>) of cats (MM: <span class="html-italic">n</span> = 8; FM: <span class="html-italic">n</span> = 8; HFM: <span class="html-italic">n</span> = 8) fed experimental diets containing MM, FM, and HFM. The symbol (*) indicates statistically significant differences between two groups (* <span class="html-italic">p</span>&lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Gut microbial composition and structure of cats (MM: <span class="html-italic">n</span> = 6; FM: <span class="html-italic">n</span> = 6; HFM: <span class="html-italic">n</span> = 8) fed experimental diets containing MM, FM, and HFM. Venn diagram of fecal samples in each group (<b>A</b>); alpha diversity (<b>B</b>); principal co-ordinate analysis (PCoA) based on weighted UniFrac distances (<b>C</b>). The symbol (*) indicates statistically significant differences between two groups (* <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span>&lt; 0.01).</p>
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<p>Predominant fecal microbial communities and different bacteria at the phylum (<b>A</b>) and genus levels (<b>B</b>) in cats (MM: <span class="html-italic">n</span> = 6; FM: <span class="html-italic">n</span> = 6; HFM: <span class="html-italic">n</span> = 8) fed experimental diets containing MM, FM, and HFM. The symbol (*) indicates statistically significant differences between two groups (* <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span>&lt; 0.01), and the symbol (<sup>#</sup>) represents the difference tendency (<sup>#</sup> <span class="html-italic">p</span> &lt; 0.10).</p>
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<p>The linear discriminant analysis effect size (LEfSe) analysis identified the most differential microbiota in cats fed experimental diets containing MM, FM, and HFM.</p>
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<p>Multivariate statistical analysis of metabolites in cats fed experimental diets containing MM, FM, and HFM. Score plots from the principal component analysis (PCA) model among the three groups (<b>A</b>). Score plots from the partial least-squares discriminant analysis (PLS-DA) model among the three groups (<b>B</b>).</p>
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<p>Bar charts of the metabolic pathway analysis of differential serum metabolites between the MM and FM groups (<b>A</b>) and between the FM and HFM groups (<b>B</b>).</p>
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14 pages, 8565 KiB  
Article
Role of Milk Intake in Modulating Serum Lipid Profiles and Gut Metabolites
by Ting Xu, Chang Zhang, Yufeng Yang, Liang Huang, Qingyou Liu, Ling Li, Qingkun Zeng and Zhipeng Li
Metabolites 2024, 14(12), 688; https://doi.org/10.3390/metabo14120688 - 7 Dec 2024
Viewed by 575
Abstract
Background/Objectives: Milk is one of the main sources of nutrition in people’s daily diet, but the fat in milk raises health concerns in consumers. Here, we aimed to elucidate the impact of Buffalo milk and Holstein cow milk consumption on blood lipid health [...] Read more.
Background/Objectives: Milk is one of the main sources of nutrition in people’s daily diet, but the fat in milk raises health concerns in consumers. Here, we aimed to elucidate the impact of Buffalo milk and Holstein cow milk consumption on blood lipid health through metabolomics analysis. Methods: Golden hamsters were administered Murrah Buffalo milk (BM) or Holstein cow milk (HM), and the body weight and serum lipid indicators were tested and recorded. The hamsters receiving equal amounts of physiological saline were used as the negative control (NC). Serum and fecal samples were collected, and LC-MS was used to identify the metabolites in the samples. Results: The results showed that both the BM and HM groups exhibited a significant reduction in body weight compared to that of the NC group from day 9, and the serum TG, TC, and LDL-C levels were significantly lower than those of the NC group. Further analysis identified 564 and 567 metabolites in the serum and fecal samples shared in the BM and HM groups and significantly different from those in the NC group, which were mainly enriched in the pathways related to lipid metabolism, such as fatty acid biosynthesis, arachidonic acid metabolism, and primary bile acid biosynthesis. Correlation analysis further suggested that milk intake can increase the levels of Muramic Acid, Oleoyl Ethanolamide, Seratrodast, Chenodeoxycholic Acid, Docosahexaenoic Acid Ethyl Ester, and Deoxycholic Acid in the serum and gut microbiota, which may affect TG, TC, HDL-C, and LDL-C in the serum, and thereby benefit the body’s lipid health. Conclusions: The results further confirmed that milk intake has a beneficial effect on blood lipid health by altering multiple metabolites in the serum and the gut. This study provides novel evidence that milk consumption is beneficial to health and is a reference for guiding people to a healthy diet. Full article
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<p>The effects of Buffalo milk and Holstein cow milk intake on the body weight and blood lipid levels of the golden hamsters. (<b>A</b>) The animal experimental design. (<b>B</b>) The analysis of the composition of Buffalo milk and Holstein cow milk. (<b>C</b>) The body weight of the hamsters. (<b>D</b>) The serum TG levels of the hamsters. (<b>E</b>) The serum TC levels of the hamsters. (<b>F</b>) The serum LDL-c levels of the hamsters. (<b>G</b>) The serum HDL-c levels of the hamsters. (<b>H</b>) The serum LDL-c/HDL-c ratio of the hamsters. Different labels (*, **, ***, ****) indicate significant differences (<span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">p</span> &lt; 0.01, <span class="html-italic">p</span> &lt; 0.001, <span class="html-italic">p</span> &lt; 0.0001), respectively. The different labels (a and b) indicate significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The effects of Buffalo milk and Holstein cow milk intake on the serum metabolites in the golden hamsters. (<b>A</b>) The principal component analysis (PCA) of the metabolites. (<b>B</b>) The OPLS-DA analysis of the metabolites. (<b>C</b>) The OPLS-DA permutation test of the metabolomics. (<b>D</b>) A heatmap of the cluster analysis of the metabolomics. (<b>E</b>) The identification of differential metabolites. (<b>F</b>) A Venn diagram showing the differential metabolites between the BM vs. NC, HM vs. NC, and HM vs. BM groups. (<b>G</b>) The KEGG pathway enrichment analysis of the differential metabolites shared in both the BM and HM groups and significantly different from those in the NC group.</p>
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<p>The effects of Buffalo milk and Holstein cow milk intake on the gut microbial metabolites in the golden hamsters. (<b>A</b>) The principal component analysis (PCA) of the NC, BM, and HM groups. (<b>B</b>) An OPLS-DA plot comparing the three groups. (<b>C</b>) The OPLS-DA permutation test for gut microbiota metabolomics. (<b>D</b>) A heatmap of the cluster analysis among the three groups. (<b>E</b>) The visualization of differential metabolites. (<b>F</b>) A Venn diagram showing the union of differential metabolites between the BM vs. NC, HM vs. NC, and HM vs. BM groups. (<b>G</b>) The KEGG pathway enrichment analysis of the differential metabolites shared both in the BM and HM groups and significantly different from those in the NC group.</p>
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<p>Correlation analysis of shared differential metabolites from serum and gut microbiota with blood lipids between BM and HM groups compared to those of NC group. (<b>A</b>) Venn diagram of shared gut microbial and serum differentia metabolites in BM group compared to NC group. (<b>B</b>) Cluster analysis of shared DEMs in NC, BM, and HM groups. (<b>C</b>) Pearson correlation analysis between shared differential metabolites and serum lipid parameters. (<b>D</b>) Venn diagram of shared gut microbial and serum differential metabolites in HM group compared to NC group. (<b>E</b>) Cluster analysis of shared differential metabolites in NC, BM, and HM groups. (<b>F</b>) Pearson correlation analysis between shared differential metabolites and serum lipid parameters. Different labels (*, **) indicate significant differences (<span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">p</span>&lt; 0.01).</p>
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15 pages, 5598 KiB  
Article
An Integrated Analysis of the Role of Gut Microbiome-Associated Metabolites in the Detection of MASH-Related Cirrhosis
by Feixiang Xiong, Xuejie Zhang, Yuyong Jiang, Peipei Meng, Yang Zhou, Xiaomin Ji, Jialiang Chen, Tong Wu and Yixin Hou
Metabolites 2024, 14(12), 681; https://doi.org/10.3390/metabo14120681 - 4 Dec 2024
Viewed by 610
Abstract
Background and aim: The prevalence and adverse outcomes of metabolic dysfunction associated with steatotic liver disease (MAFLD) are increasing. The changes in the gut microbiota and metabolites associated with metabolic dysfunction-associated steatohepatitis (MASH) are regarded as an essential part of the progression of [...] Read more.
Background and aim: The prevalence and adverse outcomes of metabolic dysfunction associated with steatotic liver disease (MAFLD) are increasing. The changes in the gut microbiota and metabolites associated with metabolic dysfunction-associated steatohepatitis (MASH) are regarded as an essential part of the progression of MAFLD. This study aimed to identify the gut microbiota and metabolites involved in the development of MAFLD in patients. Method: This study enrolled 90 patients (healthy controls, HC: n = 30; MASH: n = 30; MASH-related cirrhosis, MC: n = 30), and their fecal samples were collected for 16S rRNA sequencing and non-targeted LC–MS/MS metabolomics analysis. Data preprocessing and statistical analyses were performed using QIIME2 software, Pynast, QIIME2 package, Progenesis QI, and R program. Results: The abundance of Prevotellaceae at the family level and Prevotella at the genus level was lower in the MASH and NC samples than in the HC samples. Both Prevotellaceae and Prevotella showed the strongest correlation with MASH progression via random forest analysis. Untargeted metabolomics was used to quantitatively screen for discrepant metabolites in the stool samples from the three groups. Linolenic acid (LA)-related metabolite levels were significantly lower in MASH and NC samples. Associations between Prevotella- or LA-related metabolites and liver function were discovered. A high abundance of Prevotella was associated with LA-related metabolites and MASH. Conclusion: This study identified that gut microbiota and metabolites are associated with MASH-related metabolic dysfunction. LA and Prevotella are depleted during MASH progression, and additional supplementation with Prevotella may be a potential strategy for the future treatment of MAFLD. Full article
(This article belongs to the Section Lipid Metabolism)
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<p>(<b>A</b>) ACE index boxplot in three groups; (<b>B</b>) Chao1 index boxplot in three groups; (<b>C</b>) Shannon index boxplot in three groups; (<b>D</b>) Simpson index boxplot in three groups; (<b>E</b>) Principal Coordinates Analysis (PCoA) between MASH and HC patients; (<b>F</b>) PCoA analysis between NC and HC patients; (<b>G</b>) the abundance of the top 15 gut microbiota in three groups at family level; (<b>H</b>) the abundance of the top 15 gut microbiota in three groups at genus level; (<b>I</b>) the importance of decreased gut microbiota in three groups at the family level via RF analysis; (<b>J</b>) the importance of decreased gut microbiota in three groups at the genus level via RF analysis. *, <span class="html-italic">p</span> &lt; 0.05; ns, <span class="html-italic">p</span> &gt; 0.05. Abbreviations: HC, healthy control; MASH, nonalcoholic steatohepatitis; NC, nonalcoholic steatohepatitis cirrhosis; RF, random forest.</p>
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<p>(<b>A</b>) OPLS-DA analysis between HC and MASH patients; (<b>B</b>) OPLS-DA analysis between HC and NC patients; (<b>C</b>) filtering out the metabolites identified through both univariate and multivariate statistical analyses via volcano plots in MASH patients (VIP &gt; 2, <span class="html-italic">p</span> &lt; 0.05, |log2FC| &gt; 1); (<b>D</b>) filtering out the metabolites identified through both univariate and multivariate statistical analyses via volcano plots in NC patients (VIP &gt; 2, <span class="html-italic">p</span> &lt; 0.05, |log2FC| &gt; 1); (<b>E</b>) the main gut metabolites between HC and MASH patients; (<b>F</b>) the main gut metabolites between HC and NC patients. Abbreviations: HC, healthy control; MASH, non-alcoholic steatohepatitis; NC, non-alcoholic steatohepatitis cirrhosis; VIP, variable importance in projection; FC, fold change.</p>
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<p>(<b>A</b>) Upregulation and downregulation of lipid gut metabolites in MASH patients; (<b>B</b>) upregulation and downregulation of lipid gut metabolites in NC patients; (<b>C</b>) the KEGG enrichment via lipid gut metabolites in MASH patients; (<b>D</b>) the KEGG enrichment via lipid gut metabolites in NC patients; (<b>E</b>–<b>I</b>) LA-related metabolites level in three groups. Abbreviations: HC, healthy control; MASH, nonalcoholic steatohepatitis; NC, nonalcoholic steatohepatitis cirrhosis; LA, linolenic acid. *, <span class="html-italic">p</span> &lt; 0.05; **, <span class="html-italic">p</span> &lt; 0.01; ***, <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>(<b>A</b>) The heatmap of the correlation between reduced gut microbiota and liver function in MASH patients; (<b>B</b>) the heatmap of the correlation between reduced gut microbiota and liver function in NC patients; (<b>C</b>) the heatmap of the correlation between lipid gut microbiota and liver function in MASH patients; (<b>D</b>) the heatmap of the correlation between lipid gut microbiota and liver function in NC patients. Abbreviations: HC, healthy control; MASH, non-alcoholic steatohepatitis; NC, non-alcoholic steatohepatitis cirrhosis. *, <span class="html-italic">p</span> &lt; 0.05; **, <span class="html-italic">p</span> &lt; 0.01; ***, <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>(<b>A</b>) Incidence of MASH and cirrhosis in the high- and low-abundance groups of <span class="html-italic">Prevotella</span> in three groups; (<b>B</b>) LA-related gut microbiota level of three groups in the low-abundance group of <span class="html-italic">Prevotella</span>; (<b>C</b>) LA-related gut microbiota level of three groups in the high-abundance group of <span class="html-italic">Prevotella</span>; (<b>D</b>) the bubble diagram of the correlation between LA-related gut microbiota and a low abundance of <span class="html-italic">Prevotellaa</span>; (<b>E</b>) the bubble diagram of the correlation between LA-related gut microbiota and a high abundance of <span class="html-italic">Prevotella.</span> Abbreviations: HC, healthy control; MASH, non-alcoholic steatohepatitis; NC, non-alcoholic steatohepatitis cirrhosis; LA, linolenic acid. *, <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. ns, <span class="html-italic">p</span> &gt; 0.05.</p>
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23 pages, 523 KiB  
Systematic Review
The Efficacy of Fecal Microbiota Transplantation in Mouse Models Infected with Clostridioides difficile from the Perspective of Metabolic Profiling: A Systematic Review
by Anna Voziki, Olga Deda and Melania Kachrimanidou
Metabolites 2024, 14(12), 677; https://doi.org/10.3390/metabo14120677 - 3 Dec 2024
Viewed by 606
Abstract
Objectives: This systematic review evaluates the effectiveness of fecal microbiota transplantation (FMT) in treating Clostridioides difficile infection (CDI) in mouse models using a metabolomics-based approach. Methods: A comprehensive search was conducted in three databases (PubMed, Scopus, Google Scholar) from 10 April [...] Read more.
Objectives: This systematic review evaluates the effectiveness of fecal microbiota transplantation (FMT) in treating Clostridioides difficile infection (CDI) in mouse models using a metabolomics-based approach. Methods: A comprehensive search was conducted in three databases (PubMed, Scopus, Google Scholar) from 10 April 2024 to 17 June 2024. Out of the 460 research studies reviewed and subjected to exclusion criteria, only 5 studies met all the inclusion criteria and were analyzed. Results: These studies consistently showed that FMT effectively restored gut microbiota and altered metabolic profiles, particularly increasing short-chain fatty acids (SCFAs) and secondary bile acids, which inhibited C. difficile growth. FMT proved superior to antibiotic and probiotic treatments in re-establishing a healthy gut microbiome, as evidenced by significant changes in the amino acid and carbohydrate levels. Despite its promise, variability in the outcomes—due to factors such as immune status, treatment protocols, and donor microbiome differences—underscores the need for standardization. Rather than pursuing immediate standardization, the documentation of factors such as donor and recipient microbiome profiles, preparation methods, and administration details could help identify optimal configurations for specific contexts and patient needs. In all the studies, FMT was successful in restoring the metabolic profile in mice. Conclusions: These findings align with the clinical data from CDI patients, suggesting that FMT holds potential as a therapeutic strategy for gut health restoration and CDI management. Further studies could pave the way for adoption in clinical practice. Full article
(This article belongs to the Special Issue Preclinical and Clinical Application of Metabolomics in Medicine)
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<p>PRISMA flow chart for identification of studies via databases and registers.</p>
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20 pages, 5798 KiB  
Article
Pasteurized Akkermansia muciniphila Ameliorates Preeclampsia in Mice by Enhancing Gut Barrier Integrity, Improving Endothelial Function, and Modulating Gut Metabolic Dysregulation
by Linyu Peng, Qinlan Yin, Xinwen Wang, Yawen Zhong, Yu Wang, Wanting Cai, Ruisi Zhou, Ying Chen, Yu Hu, Zhixing Cheng, Wenqian Jiang, Xiaojing Yue and Liping Huang
Microorganisms 2024, 12(12), 2483; https://doi.org/10.3390/microorganisms12122483 - 2 Dec 2024
Viewed by 762
Abstract
Preeclampsia (PE) is a serious complication of pregnancy linked to endothelial dysfunction and an imbalance in the gut microbiota. While Akkermansia muciniphila (AKK) has shown promise in alleviating PE symptoms, the use of live bacteria raises safety concerns. This study explored the potential [...] Read more.
Preeclampsia (PE) is a serious complication of pregnancy linked to endothelial dysfunction and an imbalance in the gut microbiota. While Akkermansia muciniphila (AKK) has shown promise in alleviating PE symptoms, the use of live bacteria raises safety concerns. This study explored the potential of pasteurized A. muciniphila (pAKK) as a safer alternative for treating PE, focusing on its effects on endothelial function and metabolic regulation. A PE mouse model was induced via the nitric oxide synthase inhibitor L-NAME, followed by treatment with either pAKK or live AKK. Fecal metabolomic profiling was performed via liquid chromatography–tandem mass spectrometry (LC-MS/MS), and in vivo and in vitro experiments were used to assess the effects of pAKK on endothelial function and metabolic pathways. pAKK exhibited therapeutic effects comparable to those of live AKK in improving L-NAME-induced PE-like phenotypes in mice, including enhanced gut barrier function and reduced endotoxemia. pAKK also promoted placental angiogenesis by restoring endothelial nitric oxide synthase (eNOS) activity and nitric oxide (NO) production. The in vitro experiments further confirmed that pAKK alleviated L-NAME-induced NO reduction and endothelial dysfunction in human umbilical vein endothelial cells (HUVECs). Metabolomic analysis revealed that both pAKK and live AKK reversed metabolic disturbances in PE by modulating key metabolites and pathways related to unsaturated fatty acid biosynthesis, folate, and linoleic acid metabolism. As a postbiotic, pAKK may support existing treatments for preeclampsia by improving gut barrier function, restoring endothelial function, and regulating metabolic dysregulation, offering a safer alternative to live bacteria. These findings highlight the potential clinical value of pAKK as an adjunctive therapy in managing PE. Full article
(This article belongs to the Special Issue Microbiota in Human Health and Disease)
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<p>Pasteurized <span class="html-italic">Akkermansia muciniphila</span> alleviates hypertension and fetal resorption in PE mouse models. (<b>A</b>) Experimental design. To determine whether pasteurized <span class="html-italic">Akkermansia muciniphila</span> (pAKK) has the same beneficial effects as live <span class="html-italic">A. muciniphila</span> (AKK) in alleviating PE symptoms, the mice were pretreated with PBS, live AKK, or PAKK. At E8.5, the mice were divided into CTRL, PLN, AmLN, and pAmLN groups (n = 6/group) depending on whether they received L-NAME by gavage. (<b>B</b>) Systolic blood pressure (SBP). (<b>C</b>) Urinary albumin. (<b>D</b>) Gross morphology of dissected uteruses showing fetal resorption sites across groups. (<b>E</b>) Fetal absorption rate per litter. Data are presented as the mean ± SEM. Significant differences based on one-way ANOVA followed by Bonferroni’s post hoc test: * <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001, and CTRL group compared to the PLN group; +++ <span class="html-italic">p</span> &lt; 0.001, and AmLN and pAmLN groups compared to the PLN group.</p>
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<p>Pasteurized <span class="html-italic">Akkermansia muciniphila</span> improves placental and fetal development in PE mouse models. (<b>A</b>) Gross placental and fetal morphology across the experimental groups. (<b>B</b>) Fetal weight. (<b>C</b>) Fetal crown-rump length. (<b>D</b>) fetal/placental weight ratios. (<b>E</b>) Effects of pAKK and live AKK on placental morphology. Representative H&amp;E-stained placental images are shown, with the labyrinth layer and junctional zone marked by dashed lines. Scale bars: 500 µm (<b>left</b>) and 50 µm (<b>right</b>). (<b>F</b>) Ratio of the labyrinth layer to the junctional zone in the placenta. The data are presented as the mean ± SEM (n = 6/group). ** <span class="html-italic">p</span> &lt; 0.01, **** <span class="html-italic">p</span> &lt; 0.0001, as determined using a one-way ANOVA following Bonferroni’s post hoc test. LZ, labyrinth zone; JZ, junctional zone.</p>
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<p>Pasteurized <span class="html-italic">Akkermansia muciniphila</span> improves intestinal barrier damage in PE mice stimulated by L-NAME. The mice were treated with PBS (CTRL), L-NAME (PLN), live AKK (AmLN), or pAKK(pAmLN) according to the animal experimental protocol described in the Methods section. (<b>A</b>) Digital images of colon tissues resected from the cecum to the rectum. (<b>B</b>) Average colon length (<b>left</b>), average colon weight (<b>middle</b>), and length/weight ratio of the colon (<b>right</b>) after treatment. (<b>C</b>) Average length (<b>left</b>), weight (<b>middle</b>), and length/weight ratio (<b>right</b>) of the small intestine after treatment. (<b>D</b>) Representative H&amp;E-stained images of the proximal colon. The upper scale bar represents 500 µm, and the lower scale bar represents 100 µm. (<b>E</b>) Histological injury scores of the colon. (<b>F</b>) Portal plasma LPS levels (ng/mL). (<b>G</b>) Representative AB-PAS-stained images of the proximal colon. The upper scale bar represents 500 µm, and the lower scale bar represents 100 µm. Red arrows indicate acidic mucin and green arrows indicate mixed mucin. A total of 17–23 crypts (4–6 per colonic section) were randomly selected per animal to determine the number of goblet cells per crypt (<b>H</b>) as well as the depth of the crypt (<b>I</b>). The data are presented as the mean ± SEM (n = 6/group). * <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, **** <span class="html-italic">p</span> &lt; 0.0001, ns, no significance, as determined using a one-way ANOVA following Bonferroni’s post hoc test.</p>
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<p>Pasteurized <span class="html-italic">Akkermansia muciniphila</span> enhances intestinal barrier integrity by regulating tight junction protein expression. (<b>A</b>–<b>C</b>) Relative mRNA expression of gut barrier function markers in the jejunum, ileum, and colon. Tight junction proteins: (<b>A</b>) <span class="html-italic">Tjp1</span>, (<b>B</b>) <span class="html-italic">Ocln</span>, and (<b>C</b>) <span class="html-italic">Cldn2</span>. (<b>D</b>) Representative image of occludin immunohistochemistry staining in colon tissues. Scale bar represents 50 µm. (<b>E</b>) Quantitative comparison of occludin expression levels. The data are presented as the mean ± SEM (n = 6/group). * <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, as determined using a one-way ANOVA following Bonferroni’s post hoc test.</p>
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<p>Pasteurized <span class="html-italic">Akkermansia muciniphila</span> improves placental angiogenesis and NO synthesis in PE mice stimulated by <span class="html-small-caps">L</span>-NAME. The mice were treated with PBS (CTRL), L-NAME (PLN), live AKK (AmLN), or pAKK(pAmLN) according to the animal experimental protocol described in the Methods section. (<b>A</b>,<b>B</b>) Immunofluorescence (IF) staining of CD31 in the placenta across the four groups. (<b>A</b>) Representative images are shown. (<b>B</b>) Quantification of IF intensity analyzed via ImageJ. Scale bar = 100 µm. (<b>C</b>) Serum sFlt-1 levels. (<b>D</b>) Serum PlGF levels. (<b>E</b>) sFlt-1/PlGF ratio. (<b>F</b>) Serum NO levels. (<b>G</b>) Serum tetrahydrobiopterin (BH4, a key cofactor of eNOS) levels. (<b>H</b>) Placental eNOS expression was detected by Western blotting. The data are presented as the mean ± SEM (n = 6/group). * <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, **** <span class="html-italic">p</span> &lt; 0.0001, as determined using a one-way ANOVA followed by Bonferroni’s post hoc test.</p>
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<p>Pasteurized <span class="html-italic">Akkermansia muciniphila</span> ameliorates angiogenic potential and NO production in HUVECs exposed to L-NAME in vitro. Human umbilical vein endothelial cells (HUVECs) were incubated with pAKK for 24 h before L-NAME treatment (300 μM). (<b>A</b>,<b>B</b>) Gene expression levels of the antiangiogenic factor <span class="html-italic">sFlt-1</span> (<b>A</b>) and the proangiogenic factor <span class="html-italic">VEGFA</span> (<b>B</b>) in different HUVEC groups. (<b>C</b>,<b>D</b>) Intracellular NO levels in HUVECs were measured via the DAF-FM DA probe. (<b>C</b>) Flow cytometry analysis of intracellular NO levels in HUVECs. (<b>D</b>) Fluorescence imaging of intracellular NO levels in HUVECs, with representative images captured at 200× magnification (left panel) and quantification of fluorescence intensity (right panel). (<b>E</b>–<b>I</b>) pAKK enhances the angiogenic capacity of HUVECs stimulated with L-NAME. (<b>E</b>) Tube formation of HUVECs treated with CTRL (control), L-NAME, or L-NAME + pAKK. The total tubule length (<b>F</b>), number of meshes (<b>G</b>), number of junctions (<b>H</b>), and percentage of mesh area (<b>I</b>) were measured via quantitative analysis at 6 h postinduction. (<b>J</b>) eNOS expression in different HUVEC groups, with eNOS and GAPDH levels detected by Western blotting. The data are presented as the mean ± SEM (n = 4). * <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, **** <span class="html-italic">p</span> &lt; 0.0001, as determined using a one-way ANOVA following Bonfferoni’s post hoc test.</p>
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<p>Pasteurized <span class="html-italic">Akkermansia muciniphila</span> reverses fecal metabolomics disorders in PE mice. (<b>A</b>) PCA results: The x-axis represents the first principal component (PC1), and the y-axis represents the second principal component (PC2). The ellipses indicate the 95% confidence intervals. Each dot represents a sample, with different colors corresponding to different groups. (<b>B</b>–<b>D</b>) PLS-DA results: the <span class="html-italic">x</span>-axis represents the first principal component, and the <span class="html-italic">y</span>-axis represents the second principal component. The numbers in parentheses indicate the percentage of total variance explained by the corresponding principal component. (<b>E</b>–<b>G</b>) Volcano plot analysis: the <span class="html-italic">x</span>-axis represents the log2(fold change), and the <span class="html-italic">y</span>-axis represents the −log10(<span class="html-italic">p</span>-value). Each point represents a metabolite. Significantly upregulated metabolites are shown as red dots and significantly downregulated metabolites are shown as blue dots. The size of each dot indicates the VIP value. (<b>H</b>) Venn diagram: overlapping and unique differentially abundant metabolites among the PLN vs. CTRL, AmLN vs. PLN, and pAmLN vs. PLN comparisons.</p>
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<p>Metabolic pathway enrichment analysis of differential metabolites. (<b>A</b>–<b>C</b>) The <span class="html-italic">x</span>-axis represents the enrichment factor, with a higher value indicating a greater ratio of differentially abundant metabolites annotated to the pathway. The color of the dots represents the <span class="html-italic">p</span>-value from the hypergeometric test, with smaller values indicating greater statistical significance. The size of the dots indicates the number of differentially abundant metabolites annotated to each pathway.</p>
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20 pages, 3836 KiB  
Article
Taurine Supplementation Alleviates Blood Pressure via Gut–Brain Communication in Spontaneously Hypertensive Rats
by Qing Su, Xiong-Feng Pan, Hong-Bao Li, Ling-Xiao Xiong, Juan Bai, Xiao-Min Wang, Xiao-Ying Qu, Ning-Rui Zhang, Guo-Quan Zou, Yang Shen, Lu Li, Li-Li Huang, Huan Zhang and Meng-Lu Xu
Biomedicines 2024, 12(12), 2711; https://doi.org/10.3390/biomedicines12122711 - 27 Nov 2024
Viewed by 970
Abstract
Objects: Taurine exhibits protective effects in the context of cardiovascular pathophysiology. A range of evidence suggests that hypertension activates inflammatory responses and oxidative stress in the paraventricular nucleus (PVN), elevating the arterial tone and sympathetic activity, while it induces gut–brain axis dysfunction in [...] Read more.
Objects: Taurine exhibits protective effects in the context of cardiovascular pathophysiology. A range of evidence suggests that hypertension activates inflammatory responses and oxidative stress in the paraventricular nucleus (PVN), elevating the arterial tone and sympathetic activity, while it induces gut–brain axis dysfunction in the context of hypertension. However, the mechanism underlying taurine’s anti-hypertensive effects via the gut–brain axis remains unclear. Method: Male spontaneously hypertensive rats (SHRs) were administered 3% taurine in their drinking water for eight weeks, with their arterial pressure measured weekly. Molecular techniques were employed to investigate taurine’s effects on the hypertensive gut and PVN. Additionally, 16S rRNA gene sequencing was used to analyze the gut microbiota composition, and untargeted metabolomics was applied to assess the fecal metabolites following taurine supplementation. Results: Taurine supplementation not only reduced the blood pressure, sympathetic activity, and inflammatory and oxidative stress in the PVN but also improved the cardiac pathology and microbiota composition while alleviating gut inflammation in hypertensive rats. The untargeted metabolite analysis indicated that the primary effect of the taurine intervention in SHRs was exerted on tryptophan metabolism. The levels of serum metabolites such as kynurenine, L-tryptophan, serotonin (5-HT), and 5-hydroxyindole-3-acetic acid (5-HIAA) were altered in hypertensive rats following taurine treatment. Conclusions: Taurine supplementation restored the microbiota balance, strengthened the mucosal barrier, reduced intestinal inflammation, and stimulated tryptophan metabolism. The metabolites derived from the gut microbiota likely crossed the brain barrier and reached the paraventricular nucleus, thereby reducing the inflammatory responses and oxidative stress in the PVN via gut–brain communication, leading to decreased sympathetic nerve activity and blood pressure in the studied hypertensive rats. Full article
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Graphical abstract
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<p>Effects of taurine on blood pressure and cardiac hypertrophy in SHR and WKY rats: (<b>a</b>) Systolic BP (SBP) in 8 weeks; (<b>b</b>) mean arterial blood pressure (MAP); (<b>c</b>) noradrenaline (NE) in plasma; (<b>d</b>) the images of H&amp;E and Masson’s trichrome staining for heart; (<b>e</b>) histogram of cross-sectional area of cardiomyocytes; (<b>f</b>) histogram of perivascular fibrosis. ● represents WKY group data, ■ represents SHR group data, <tt>▲</tt> represents SHR+Taurine group data. Values are expressed as the mean ± SEM. One-way ANOVA with Tukey’s multiple comparison tests (n = 6). * <span class="html-italic">p</span> &lt; 0.05 versus WKY groups; <span class="html-italic"><sup>#</sup> p</span> &lt; 0.05 SHR vs. SHR+Taurine.</p>
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<p>Effects of taurine on gut microbial composition in hypertensive rats: (<b>a</b>) Principal Co-ordinates Analysis (PCoA) of microbiota, PCA plots of the first two principal components for three groups. Ellipses around points represent a 95% confidence interval. (<b>b</b>) Chao 1 richness; (<b>c</b>) Shannon diversity; (<b>d</b>) linear discriminant analysis effect size (LefSe) analysis, and each circle from the inside to out represents Phylum, Class, Order, Family, Genus, Species, but the yellow dots represent those species have no significant with others; (<b>e</b>) the cladograms of intestinal flora; (<b>f</b>) relative abundance of <span class="html-italic">Clostridia</span>, <span class="html-italic">Bacilli</span>, <span class="html-italic">Oscillospirales</span>, <span class="html-italic">Lachnospirales</span>, <span class="html-italic">Lactobacillales</span>, <span class="html-italic">Ruminococcaceae</span>, <span class="html-italic">Oscillospiraceae, Lachnospiraceae</span>, <span class="html-italic">Lactobacillaceae</span>, and the more data distribution, the bigger the violin area. ● represents WKY group data, ■ represents SHR group data, <tt>▲</tt> represents SHR+Taurine group data. Values are expressed as the mean ± SEM. One-way ANOVA with Tukey’s multiple comparison tests (n = 6 in WKY and SHR group. n = 5 in SHR+Taurine group). * <span class="html-italic">p</span> &lt; 0.05 versus WKY groups; <span class="html-italic"><sup>#</sup> p</span> &lt; 0.05 SHR vs. SHR+Taurine.</p>
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<p>Histopathological analysis of the effects of taurine in hypertensive rat colon: (<b>a</b>) Masson’s trichrome staining for collagen deposition and (<b>b</b>) histogram of Fibroticare area %; (<b>c</b>) H&amp;E staining for the muscles layer thickness and (<b>d</b>) analysis of muscle layer thickness; (<b>e</b>) H&amp;E staining for goblet cells and (<b>f</b>) histogram of goblet cells area %; (<b>g</b>) H&amp;E staining for crypt depth and (<b>h</b>) analysis of crypt depth. ● represents WKY group data, ■ represents SHR group data, <tt>▲</tt> represents SHR+Taurine group data. Values are expressed as the mean ± SEM. One-way ANOVA with Tukey’s multiple comparison tests (n = 6). * <span class="html-italic">p</span> &lt; 0.05 versus WKY groups; <span class="html-italic"><sup>#</sup> p</span> &lt; 0.05 SHR vs. SHR+Taurine.</p>
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<p>Effects of taurine on ZO-1 and Occludin in hypertensive rat colon: (<b>a</b>) Immunohistochemical staining for ZO-1 (red) and Occludin (green) in the colon, DAPI is blue; (<b>b</b>) histogram of ZO-1/DAPI fluorescence ratio and (<b>c</b>) Occludin fluorescence ratio; Western blotting for ZO-1 and (<b>d</b>–<b>f</b>) Occludin; (<b>g</b>) Violin of protein/β-actin ratio (n = 5), and the more data distribution, the bigger the violin area. ● represents WKY group data, ■ represents SHR group data, <tt>▲</tt> represents SHR+Taurine group data. Values are expressed as the mean ± SEM. One-way ANOVA with Tukey’s multiple comparison tests (n = 5/6). * <span class="html-italic">p</span> &lt; 0.05 versus WKY groups; <span class="html-italic"><sup>#</sup> p</span> &lt; 0.05 SHR vs. SHR+Taurine.</p>
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<p>Effects of taurine on neuroinflammation in the PVN of a hypertensive rat: (<b>a</b>) immunofluorescence for IL-1β (red), DAPI is blue and immunohistochemical staining TNF-α (brown) in the PVN; (<b>b</b>) IL-1β in the PVN histogram of the number of positive neurons and (<b>c</b>) TNF-α in the PVN histogram of positive staining area %; (<b>d</b>–<b>f</b>) Western blotting for IL-1β, TNF-α, and IL-10 in the PVN; (<b>g</b>) Violin of protein/β-actin ratio (n = 5). ● represents WKY group data, ■ represents SHR group data, <tt>▲</tt> represents SHR+Taurine group data. Values are expressed as the mean ± SEM. One-way ANOVA with Tukey’s multiple comparison tests (n = 5/6). * <span class="html-italic">p</span> &lt; 0.05 versus WKY groups; <span class="html-italic"><sup>#</sup> p</span> &lt; 0.05 SHR vs. SHR+Taurine.</p>
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<p>Effects of taurine on oxidative stress in the PVN of hypertensive rat: (<b>a</b>) immunohistochemical staining p47<sup>phox</sup> (brown) in the PVN and superoxide anion staining (red) DHE; (<b>b</b>) histogram of positive staining area % and (<b>c</b>) fluorescence intensity %; (<b>d</b>,<b>e</b>) Western blotting for gp91<sup>phox</sup>; (<b>f</b>) Violin of protein/β-actin ratio (n = 5), and the more data distribution, the bigger the violin area. ● represents WKY group data, ■ represents SHR group data, <tt>▲</tt> represents SHR+Taurine group data. Values are expressed as the mean ± SEM. One-way ANOVA with Tukey’s multiple comparison tests (n = 5/6). * <span class="html-italic">p</span> &lt; 0.05 versus WKY groups; <span class="html-italic"><sup>#</sup> p</span> &lt; 0.05 SHR vs. SHR+Taurine.</p>
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<p>Effects of taurine on inflammation in the PVN and colon: ELISA analysis of inflammation in the PVN and colon. Histogram of (<b>a</b>) NF-κB activity, (<b>b</b>) IL-1β level, (<b>c</b>) TNF-α level, and (<b>d</b>) IL-10 level in the PVN. Histogram of (<b>e</b>) NF-κB activity, (<b>f</b>) IL-1β level, (<b>g</b>) TNF-α level, and (<b>h</b>) IL-10 level in colon. ● represents WKY group data, ■ represents SHR group data, <tt>▲</tt> represents SHR+Taurine group data. Values are expressed as the mean ± SEM. One-way ANOVA with Tukey’s multiple comparison tests (n = 6). * <span class="html-italic">p</span> &lt; 0.05 versus WKY groups; <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05 SHR vs. SHR+Taurine.</p>
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<p>Multivariate analysis of metabolic profiles in gut microbiota: (<b>a</b>) PCA-3D. (<b>b</b>) Scores OPLS-DA Plot: X—first principal component; Y—the second principal component. Ellipses around points represent a 95% confidence interval. (<b>c</b>) OPLS-Permutation, Q<sup>2</sup> &gt; 0.9 means a reliable model, blue represents the Perm Q<sup>2</sup>, orange represents the Perm R<sup>2</sup>Y. R<sup>2</sup>X = 0.487; Q<sup>2</sup> = 0.869 (<span class="html-italic">p</span> &lt; 0.005), R<sup>2</sup>Y = 0.99 (<span class="html-italic">p</span> &lt; 0.005). (<b>d</b>) Volcano plots: Above −Log<sub>10</sub>P-Value = 1.30, the dots (on the right side of Log<sub>2</sub>FC = 1 and on the left side of Log<sub>2</sub>FC = −1) are marked as significant differences metabolites between SHR group and SHR+Taurine group. The dot is greater than 2 times (on the right side of Log<sub>2</sub>FC= 1) as red, and less than −2 (on the left side of Log<sub>2</sub>FC = −1) as green. Red—up-regulated metabolites; green—down-regulated metabolites. (<b>e</b>) Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of feces samples collected from the SHR group and SHR+Taurine group based on LC-MS/MS in positive ion modes. Dot size: impact value; dot color: <span class="html-italic">p</span>-value. (n = 6 in WKY and SHR group. n = 5 in SHR+Taurine group).</p>
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<p>Effects of taurine on Tryptophan metabolism in hypertensive rat serum: ELISA analysis of tryptophan-related indicators in serum. Histogram of (<b>a</b>) Kynurenine, (<b>b</b>) L-Tryptophan, (<b>c</b>) Kyn/Try ratio, (<b>d</b>) serotonin (5-TH), and (<b>e</b>) 5-Hydroxyindole-3-acetic acid (5-HIAA). ● represents WKY group data, ■ represents SHR group data, <tt>▲</tt> represents SHR+Taurine group data. Values are expressed as the mean ± SEM. One-way ANOVA with Tukey’s multiple comparison tests (n = 6). * <span class="html-italic">p</span> &lt; 0.05 versus WKY groups; <span class="html-italic"><sup>#</sup> p</span> &lt; 0.05 SHR vs. SHR+Taurine.</p>
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19 pages, 6575 KiB  
Article
Chronic Sleep Deprivation Causes Anxiety, Depression and Impaired Gut Barrier in Female Mice—Correlation Analysis from Fecal Microbiome and Metabolome
by Lingyue Li, Zilin Meng, Yuebing Huang, Luyao Xu, Qianling Chen, Dongfang Qiao and Xia Yue
Biomedicines 2024, 12(12), 2654; https://doi.org/10.3390/biomedicines12122654 - 21 Nov 2024
Viewed by 485
Abstract
Background: Chronic sleep deprivation (CSD) plays an important role in mood disorders. However, the changes in the gut microbiota and metabolites associated with CSD-induced anxiety/depression-like behavior in female mice have not been determined. Due to the influence of endogenous hormone levels, females are [...] Read more.
Background: Chronic sleep deprivation (CSD) plays an important role in mood disorders. However, the changes in the gut microbiota and metabolites associated with CSD-induced anxiety/depression-like behavior in female mice have not been determined. Due to the influence of endogenous hormone levels, females are more susceptible than males to negative emotions caused by sleep deprivation. Here, we aim to investigate how CSD changes the gut microbiota and behavior and uncover the relationship between CSD and gut microbiota and its metabolites in female mice. Methods: We used a 48-day sleep deprivation (SD) model using the modified multiple platform method (MMPM) to induce anxiety/depression-like behavior in female C57BL/6J mice and verified our results using the open field test, elevated plus maze, novel object recognition test, forced swim test, and tail suspension test. We collected fecal samples of mice for 16S rDNA sequencing and untargeted metabolomic analysis and colons for histopathological observation. We used Spearmen analysis to find the correlations between differential bacterial taxa, fecal metabolites, and behaviors. Results: Our study demonstrates that CSD induced anxiety/depressive-like behaviors in female mice. The results of 16S rDNA sequencing suggested that the relative abundance of the harmful bacteria g_ Rothia, g_ Streptococcus, g_ Pantoea, and g_ Klebsiella were significantly increased, while the beneficial bacteria g_ Rikenella, g_ Eubacterium]-xylanophilum-group, and g_ Eisenbergiella were significantly decreased after SD. Glycerophospholipid metabolism and glutathione metabolism were identified as key pathways in the fecal metabolism related to oxidative stress and inflammatory states of the intestine. Histological observation showed hyperplasia of epithelial cells, a decrease in goblet cells, and glandular atrophy of the colon in SD mice. There were correlations between some of the differential bacterial taxa, fecal metabolites, and behaviors. Conclusion: In summary, we found that CSD induced anxiety/depression-like behavior, caused gut microbiota dysbiosis, altered fecal metabolism, and damaged the colon barrier in female mice. Full article
(This article belongs to the Section Endocrinology and Metabolism Research)
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<p>SD procedure and behavior test. (<b>A</b>) Schematic design of 48-d SD procedure and behavior test. (<b>B</b>) Diagram of the MMPM. (<b>C</b>) Representative tracking plot from the OFT. (<b>D</b>–<b>F</b>) Total distance (unpaired <span class="html-italic">t</span>-test, <span class="html-italic">n</span> = 4/5 per group), central square duration (unpaired <span class="html-italic">t</span>-test, <span class="html-italic">n</span> = 4 per group), and the number of entries in the center (unpaired <span class="html-italic">t</span>-test, <span class="html-italic">n</span> = 5 per group) during the OFT. (<b>G</b>) Representative track plot of the EPM test. (<b>H</b>–<b>J</b>) Time spent in the open arms (unpaired <span class="html-italic">t</span>-test), the number of entries in the open arms (unpaired <span class="html-italic">t</span>-test), and the anxiety index during the EPM test (unpaired <span class="html-italic">t</span>-test, <span class="html-italic">n</span> = 5 per group). (<b>K</b>) Diagram of the NORT. The green polyhedron represents familiar object, the red cube represents the novel object. (<b>L</b>) Recognition index of NORT (unpaired <span class="html-italic">t</span>-test, <span class="html-italic">n</span> = 4 per group). (<b>M</b>) Diagram of the FST. (<b>N</b>) Immobility time during FST (unpaired <span class="html-italic">t</span>-test, <span class="html-italic">n</span> = 5 per group). (<b>O</b>) Diagram of the TST. (<b>P</b>) Immobility time during TST (unpaired <span class="html-italic">t</span>-test, <span class="html-italic">n</span> = 5 per group). All data are presented as mean ± SEM. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Colon pathological analysis. (<b>A</b>) Hematoxylin and eosin (H&amp;E) staining. Bar = 300 μm and 60 μm. (<b>B</b>) Scores of histological changes in H&amp;E staining (<span class="html-italic">n</span> = 3). (<b>C</b>) Alcian Blue Periodic Acid Schiff (AB-PAS) Staining. Bar = 300 μm and 200 μm. (<b>D</b>) Goblet cell counting of AB-PAS staining (<span class="html-italic">n</span> = 3). All data are presented as mean ± SEM. ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Fecal microbiome data analysis after SD. (<b>A</b>) Venn diagram. (<b>B</b>,<b>C</b>) In representative diagrams of alpha diversity, all alpha diversity indicators have no statistically significant differences. (<b>D</b>) Principal coordinates analysis (PcoA) plot using Bray–Curtis distance. (<b>E</b>) The ratio of relative abundances of phylum level. (<b>F</b>) The ratio of relative abundances of family level. (<b>G</b>) The ratio of relative abundances of genus level. (<b>H</b>) The top 10 species with a <span class="html-italic">p</span>-value less than 0.05 at the phylum level. (<b>I</b>) The top 10 species with a <span class="html-italic">p</span>-value less than 0.05 at the family level. (<b>J</b>) The top 10 species with a <span class="html-italic">p</span>-value less than 0.05 at the genus level.</p>
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<p>Fecal metabolomics after SD. (<b>A</b>) Score plot of PLS-DA model in positive ion model. (<b>B</b>) Permutation plot in positive ion model. (<b>C</b>) Heatmap graph of differential metabolites in positive ion model, the metabolites are clustered according to the similarity of the metabolite expression profiles. (<b>D</b>) Volcano plot in positive ion model, showing the distribution of differential metabolites. (<b>E</b>–<b>H</b>) Score plot of PLS-DA, permutation plot, heatmap graph, and volcano plot in negative ion model. (<b>I</b>) Differential metabolite statistics. (<b>J</b>,<b>K</b>) KEGG enrichment analysis of differential metabolites in positive ion model and negative ion model.</p>
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<p>Correlation analysis between differential genera, metabolites, and behavioral indicators. (<b>A</b>,<b>B</b>) Correlation heatmap and correlation network between the top 10 genera with <span class="html-italic">p</span>-values less than 0.05 and metabolites in glycerophospholipid metabolism pathway and glutathione metabolism. (<b>C</b>) Correlation heatmap between the top 10 genera with <span class="html-italic">p</span>-values less than 0.05 and behavioral indicators. (<b>D</b>) Correlation heatmap between metabolites in glycerophospholipid metabolism pathway and glutathione metabolism and behavioral indicators. All data are presented as mean ± SEM. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p>
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17 pages, 2020 KiB  
Article
Probiotic Therapy of Gastrointestinal Symptoms During COVID-19 Infection: A Randomized, Double-Blind, Placebo-Controlled, Remote Study
by Angela Horvath, Rosa Haller, Nicole Feldbacher, Hansjörg Habisch, Kristina Žukauskaitė, Tobias Madl and Vanessa Stadlbauer
Nutrients 2024, 16(22), 3970; https://doi.org/10.3390/nu16223970 - 20 Nov 2024
Viewed by 1000
Abstract
Background: The novel coronavirus (SARS-CoV-2) led to gastrointestinal manifestations in up to 50% of cases, with diarrhea being common, and probiotics have been suggested as a potential treatment. Aim: This study aimed to assess changes in the microbiome and the effects of a [...] Read more.
Background: The novel coronavirus (SARS-CoV-2) led to gastrointestinal manifestations in up to 50% of cases, with diarrhea being common, and probiotics have been suggested as a potential treatment. Aim: This study aimed to assess changes in the microbiome and the effects of a multispecies probiotic in patients with COVID-19 in home quarantine through a fully remote telemedical approach. Methods: Thirty patients were randomized to receive either the Ecologic AAD probiotic (Winclove Probiotics, Amsterdam, The Netherlands), on the market as OMNi-BiOTiC 10 (Allergosan, Austria), or a placebo for 30 days in a 2:1 ratio. Respiratory and gastrointestinal symptoms were monitored in 2–10-day intervals via online surveys, and five stool samples were collected during the 30-day study period for microbiome and metabolomics analyses. Twenty-four healthy volunteers served as controls. Results: Of the 30 patients, 26 completed this study (10 placebo, 16 probiotic). Patients reported respiratory symptoms and a diminished gastrointestinal quality of life, both of which improved significantly during the study period, irrespective of the intervention. Compared to controls, infected patients showed significant alterations in the fecal microbiome (p = 0.002), including an increase in Bacteroidetes and decreases in Christensenellaceae, Ruminococcaceae, and Gammaproteobacteria, along with metabolomic changes. Probiotic treatment significantly modulated the patients’ microbiome beta diversity (p = 0.001) and introduced the Enterococcus faecium W54 strain. Symptoms, COVID-19-related taxa, and the fecal metabolome were not affected by the intervention. Conclusions: Patients with mild COVID-19 disease in home quarantine exhibited respiratory symptoms, a reduced gastrointestinal quality of life, and changes in the fecal microbiome and metabolome. Full article
(This article belongs to the Section Prebiotics and Probiotics)
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<p>CONSORT flowchart of the study.</p>
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<p>COVID-19-related changes in the intestinal microbiome. (<b>A</b>–<b>D</b>) Alpha diversity estimates for infected patients before intervention and non-infected controls. (<b>E</b>) LEfSe plot for differences in the intestinal microbiome between patients and controls.</p>
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<p>Changes in the stool metabolome of patients infected with COVID-19 compared to non-infected controls. (<b>A</b>) oPLS-DA plot showing clear separation between groups; (<b>B</b>) importance of the metabolites with the highest VIP scores. The color of the points indicates the group in which the metabolite was more concentrated. (<b>C</b>–<b>F</b>) Concentration of the top 4 metabolites in oPLS-DA.</p>
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<p>Recovery from COVID-19 disease. (<b>A</b>) Patients with the need to stay in bed because of COVID-19 disease; (<b>B</b>) patients with the need to cancel work activities because of COVID-19 disease; (<b>C</b>) patients with the need to cancel leisure activities because of COVID-19 disease. T—Timepoint of the study which corresponds to the day of intervention.</p>
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<p>Recovery from COVID-19 disease as a function of days after positive PCR test for probiotic and placebo group. Models were fitted using Locally Weighted Scatterplot Smoothing (LOESS) regression. (<b>A</b>) Stayed in bed (<b>B</b>) Cancelled work (<b>C</b>) Cancelled leisure activities.</p>
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<p>Changes in the microbiome composition of patients in the probiotic group compared to the placebo group; (<b>A</b>) PCoA plot of Bray–Curtis dissimilarities for the placebo group on the left side and the probiotic group on the right side; samples from the same individual are connected with a gray line; (<b>B</b>) visualization of the redundancy analysis testing the influence of intervention, time, and individual variation of the microbiome; *** <span class="html-italic">p</span> &lt; 0.001; samples from the same individual are connected by a gray line; abundance of <span class="html-italic">Enterococcus faecium</span> (<b>C</b>) and its parent genus <span class="html-italic">Enterococcus</span> (<b>D</b>) throughout the study period in both groups.</p>
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<p>(<b>A</b>) Clustering of microbiome composition and the specific response to probiotic intervention. (<b>B</b>) Shannon index of patients of the probiotic group according to cluster allocation. (<b>C</b>) Microbiome modulation quantified as the area of the convex hull according to clustering. (<b>D</b>) Abundance of <span class="html-italic">Enterococcus faecium</span> as part of the probiotic formulation in different clusters.</p>
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<p>(<b>A</b>–<b>C</b>) Increase in physical functioning measured by the Gastrointestinal Quality of Life Index was more pronounced in responding clusters (i.e., clusters with stable probiotic enrichment) compared to the non-responding clusters.</p>
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13 pages, 7485 KiB  
Article
A Comparative Metabolomics Study of the Potential Marker Compounds in Feces from Different Hybrid Offspring of Huainan Pigs
by Yufu Li, Mingyang Jia, Junfeng Chen, Fujiu Liu, Qiaoling Ren, Xiangzhou Yan, Baosong Xing, Chuanying Pan and Jing Wang
Animals 2024, 14(22), 3282; https://doi.org/10.3390/ani14223282 - 14 Nov 2024
Viewed by 519
Abstract
As a notable native Chinese genetic population, the Huainan pig has an exceptional meat quality but a low percentage of lean meat and subpar genetic performance. To better exploit the superior genetic traits of the Huainan pig and address knowledge gaps regarding the [...] Read more.
As a notable native Chinese genetic population, the Huainan pig has an exceptional meat quality but a low percentage of lean meat and subpar genetic performance. To better exploit the superior genetic traits of the Huainan pig and address knowledge gaps regarding the optimization of its hybrid offspring, this study used Huainan pigs as the maternal line and bred them with Yorkshire, Landrace, and Berkshire sires. This approach produced three hybrid combinations: Yorkshire × Huainan (YH), Landrace × Huainan (LH), and Berkshire × Huainan (BH). The body size, fat ratio, and average backfat thickness of these hybrid progeny were evaluated under the same feeding management and nutritional circumstances. The results revealed that the average backfat thickness of YH was significantly lower than that of LH and BH. In order to better understand the causes of these variations, fecal samples were taken from three pigs in each group for metabolomic analysis. A total of 2291 metabolites were identified, including benzene derivatives (16.6%), amino acids and their metabolites (14.5%), and organic acids (13.4%), with pyruvaldehyde and norethindrone acetate elevated in YH compared to LH and BH. In addition, the three hybrid pig groups commonly exhibited differences in the “glycerophospholipid metabolism” pathway. This variation may also contribute to differences in their fat ratio and backfat thickness. Our findings provide a novel perspective on the role of hybrid vigor in advancing the genetic population of Huainan pigs, while also revealing the unique metabolic characteristics of the YH with regard to fat deposition. This study is expected to enhance the conservation and effective utilization of genetic resources within the Huainan pig population. Full article
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<p>The overall workflow of the hybrid experiment and metabolomics strategy.</p>
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<p>Comparative analysis of the three hybrid pig combinations in terms of weight (<b>A</b>), body height (<b>B</b>), body length (<b>C</b>) chest girth (<b>D</b>) fat ratio (<b>E</b>), and average backfat thickness (<b>F</b>). “*” means: <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Composition (<b>A</b>) and clustering analysis heatmap (<b>B</b>) of metabolites in feces from three hybrid pig combinations. Colors correspond to the distinct values achieved following relative content normalization (red denotes high levels and green denotes low levels).</p>
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<p>Investigation of DAMs across the three distinct hybrid pig combinations. Fecal metabolite profiling was performed using OPLS-DA models between (<b>A</b>) YH and LH, (<b>B</b>) YH and BH, and (<b>C</b>) LH and BH participants. Volcano graphs (<b>D</b>–<b>F</b>) showing the DAMs for the three groups. (<b>G</b>) Upset plots showing the overlapping and accession-specific DAMs.</p>
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<p>The KEGG enrichment plots show the metabolic pathways enriched with specific metabolites that are expressed differently between YH and LH (<b>A</b>), YH and BH (<b>B</b>), and LH and BH (<b>C</b>). The <span class="html-italic">x</span>-axis signifies the Rich Factor associated with each pathway, while the <span class="html-italic">y</span>-axis shows the names of the pathways arranged in order of their <span class="html-italic">p</span>-value. The color of the data points reflects the size of the <span class="html-italic">p</span>-value, where red shades suggest a higher level of enrichment. The magnitude of the data points corresponds to the quantity of metabolites that are differentially expressed and enriched in that particular pathway. (<b>D</b>) Upset plots showing the overlapping pathways.</p>
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20 pages, 5195 KiB  
Article
The Impact of Green Tea Kombucha on the Intestinal Health, Gut Microbiota, and Serum Metabolome of Individuals with Excess Body Weight in a Weight Loss Intervention: A Randomized Controlled Trial
by Gabriela Macedo Fraiz, Dandara Baia Bonifácio, Udielle Vermelho Lacerda, Rodrigo Rezende Cardoso, Viviana Corich, Alessio Giacomini, Hércia Stampini Duarte Martino, Sergio Esteban-Echeverría, Ana Romo-Hualde, David Muñoz-Prieto, Frederico Augusto Ribeiro de Barros, Fermín I. Milagro and Josefina Bressan
Foods 2024, 13(22), 3635; https://doi.org/10.3390/foods13223635 - 14 Nov 2024
Viewed by 1828
Abstract
Green tea kombucha (GTK) has emerged as a promising probiotic fermented beverage. Few studies have investigated its effect on human health, mainly focusing on intestinal health, microbiota composition, and metabolomics. The present study is a pioneer in investigating the effect of GTK consumption [...] Read more.
Green tea kombucha (GTK) has emerged as a promising probiotic fermented beverage. Few studies have investigated its effect on human health, mainly focusing on intestinal health, microbiota composition, and metabolomics. The present study is a pioneer in investigating the effect of GTK consumption in individuals with excess body weight. This is a randomized controlled trial, lasting ten weeks, with two groups placed under an energy-restricted diet: control (CG, n = 29), kombucha (KG, n = 30; 200 mL/d). Biological samples and questionnaires were collected before and after the intervention. Microbiota analysis used an amplification of the V4 region of 16S rRNA. Serum untargeted metabolomics used HPLC-TOF mass spectrometry. Intestinal permeability considered the urine excretion of lactulose and mannitol, plasma zonulin, and LPS-binding protein. After the intervention, no differences related to intestinal permeability and microbiota were found between groups, but only the CG had increased fecal pH, lactulose/mannitol ratio, and zonulin. In addition to this, the KG reported lower gastrointestinal symptoms related to motility compared to the CG, and discriminant metabolites (e.g., diethyl malonate) were found strictly in the KG. GTK did not significantly improve gut microbiota and intestinal permeability. However, GTK ameliorated gastrointestinal symptoms and positively influenced the serum metabolome, which may contribute to enhancing the metabolic health of individuals with excess body weight. Full article
(This article belongs to the Section Food Microbiology)
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<p>Experimental design. This is a randomized controlled trial involving individuals with excess body weight allocated in control or kombucha groups. All participants attended the first meeting for screening; those who met the inclusion criteria had to accomplish a run-in period. Participants went to a second meeting to collect all the data and biological samples. In the middle of the intervention, after 5 weeks, they had a nutritional return appointment. After 10 weeks, all participants repeated the data collection. ICF: Informed Consent Form.</p>
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<p><b>A</b> CONSORT flow diagram of the participants. In total, 29 individuals completed the intervention in the control group and 30 in the kombucha group. Analysis considered the totality of participants with exception of LPS-Binding Protein (LBP) and zonulin due to insufficient biological material.</p>
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<p>Comparison of gastrointestinal symptoms evaluated through the Gastrointestinal Symptom Rating Scale (GSRS) questionnaire, according to the allocation group. Values expressed as means (SEM). Comparison between baseline and endpoint results across the same group (paired <span class="html-italic">t</span>-test) and comparisons between baseline, endpoint and Δ between groups (independent <span class="html-italic">t</span>-test), only significant <span class="html-italic">p</span>-values expressed (&lt;0.05). Δ = final <span class="html-italic">−</span> baseline.</p>
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<p>Comparison of gastrointestinal symptoms evaluated through the Gastrointestinal Symptom Rating Scale (GSRS) questionnaire, according to the allocation group. Values expressed as means (SEM). Comparison between baseline and endpoint results across the same group (paired <span class="html-italic">t</span>-test) and comparisons between baseline, endpoint and Δ between groups (independent <span class="html-italic">t</span>-test), only significant <span class="html-italic">p</span>-values expressed (&lt;0.05). Δ = final <span class="html-italic">−</span> baseline.</p>
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<p>Alpha diversity represented by Chao 1 and Shannon indices according to allocation group (control: red; kombucha: blue) and intervention visit (baseline and final). (<b>A</b>) Chao 1 and Shannon indices by genus level and (<b>B</b>) by family level. Values were compared by Wilcoxon Rank Sum Test.</p>
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<p>Venn diagram in ESI+ (<b>A</b>) and ESI− (<b>B</b>) modes showing metabolites common in both groups and those detected in just kombucha and control groups.</p>
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<p>PLS-DA plots representing the baseline (color: red) and end-of-intervention (color: green) data of the kombucha group in ESI+ (<b>A</b>) and ESI− (<b>B</b>).</p>
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<p>Correlation chart for changes in microbiota and putative metabolites found only in kombucha group after intervention. Sperman correlation considered with significance at <span class="html-italic">p</span> &lt; 0.05. Red indicates positive correlation and purple negative correlation. ***: FDR &lt; 0.001; **: FDR &lt; 0.01; *: <span class="html-italic">p</span> &lt; 0.05.</p>
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