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14 pages, 1191 KiB  
Article
Exploring Dietary- and Disease-Related Influences on Flatulence and Fecal Odor Perception in Inflammatory Bowel Disease
by Lea Pueschel, Sonja Nothacker, Leonie Kuhn, Heiner Wedemeyer, Henrike Lenzen and Miriam Wiestler
J. Clin. Med. 2025, 14(1), 137; https://doi.org/10.3390/jcm14010137 (registering DOI) - 29 Dec 2024
Viewed by 35
Abstract
Background/Objectives: Inflammatory bowel disease (IBD) affects gastrointestinal function and may alter fecal and flatulence odor (intestinal odor) due to changes in inflammation, the gut microbiome, and metabolism. Investigating the relationship between dietary habits and intestinal odor in IBD is critical given the [...] Read more.
Background/Objectives: Inflammatory bowel disease (IBD) affects gastrointestinal function and may alter fecal and flatulence odor (intestinal odor) due to changes in inflammation, the gut microbiome, and metabolism. Investigating the relationship between dietary habits and intestinal odor in IBD is critical given the relationship between diet, gut health, and microbiome diversity. Methods: We performed a cohort analysis of a monocentric, cross-sectional study at a tertiary referral center and compared the perception of fecal and flatulence odor in 233 IBD patients (n = 117 women) with that of 96 healthy controls (HCs) (n = 67 women). In addition to a short screening questionnaire on highly processed foods (sQ-HPF), dietary behavior (Food Frequency Questionnaire (FFQ)), clinical (HBI, PMS) and biochemical (CRP, fecal calprotectin) parameters of disease activity, and adherence to a Mediterranean diet were assessed. Results: A notable predisposition towards elevated levels of intestinal malodor was identified in the IBD cohort when compared to the HC group. The analysis of dietary behavior in conjunction with intestinal malodor revealed more pronounced associations in the HC collective than in the IBD collective. The data further indicated that, in comparison to those in remission, IBD individuals with an active disease status exhibited a higher prevalence of intestinal malodor. In an adjusted logistic regression analysis of the influence of disease- and diet-specific factors on flatulence and fecal malodor in IBD, male sex was identified as a significant risk factor. Conclusions: This study highlights the significance of dietary factors in the management of IBD symptoms, with a particular focus on flatulence and fecal odor. Individuals with IBD demonstrated a higher propensity for intestinal malodor compared to HC, with active disease status further amplifying this prevalence. Dietary behavior showed stronger associations with malodor in the HC group than in IBD individuals, suggesting distinct interaction patterns between diet and gut health in these populations. Full article
(This article belongs to the Special Issue Inflammatory Bowel Disease (IBD): Clinical Diagnosis and Treatment)
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<p>Flow chart of patient enrollment.</p>
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<p>Percentual distribution of (<b>a</b>) flatulence malodor and (<b>b</b>) fecal malodor between IBD patients and healthy controls. Result of chi-square test shows significant differences in intestinal malodor perception between IBD patients and healthy controls (HCs) cohort for (<b>a</b>) flatulence malodor (<span class="html-italic">p</span> &lt; 0.001) and (<b>b</b>) fecal malodor (<span class="html-italic">p</span> &lt; 0.001). *** marks significance level <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Distribution of IBD disease activity in comparison with (<b>a</b>) flatulence malodor and (<b>b</b>) fecal malodor. Result of Bonferroni correction to fisher’s exact test shows significant differences in intestinal malodor perception between IBD patients in remission vs. active disease for (<b>a</b>) flatulence malodor (<span class="html-italic">p</span> = 0.050) and (<b>b</b>) fecal malodor (<span class="html-italic">p</span> = 0.014). Total number of IBD patients is given as <span class="html-italic">n</span>. * marks significance level <span class="html-italic">p</span> &lt; 0.05 and ** marks significance level <span class="html-italic">p</span> &lt; 0.01.</p>
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29 pages, 16481 KiB  
Article
Rumen-Degradable Starch Improves Rumen Fermentation, Function, and Growth Performance by Altering Bacteria and Its Metabolome in Sheep Fed Alfalfa Hay or Silage
by Wenliang Guo, Meila Na, Shuwei Liu, Kenan Li, Haidong Du, Jing Zhang and Renhua Na
Animals 2025, 15(1), 34; https://doi.org/10.3390/ani15010034 (registering DOI) - 26 Dec 2024
Viewed by 173
Abstract
Alfalfa silage due to its high protein can lead to easier feeding management, but its high proportion of rumen-degradable protein can reduce rumen nitrogen utilization. Nevertheless, increasing dietary energy can enhance ruminal microbial protein synthesis. Thirty-two Suffolk female sheep were used in this [...] Read more.
Alfalfa silage due to its high protein can lead to easier feeding management, but its high proportion of rumen-degradable protein can reduce rumen nitrogen utilization. Nevertheless, increasing dietary energy can enhance ruminal microbial protein synthesis. Thirty-two Suffolk female sheep were used in this study, with a 2 × 2 factorial arrangement of treatment. The four treatments were a combination of two forage types (alfalfa hay; AH vs. alfalfa silage; AS) and two rumen-degradable starch levels (low RDS; LR vs. high RDS; HR) with a 15 d adaptation and 60 d experimental period. The rumen content and rumen epithelium samples were collected after slaughter. Feeding AS increased the rumen isobutyrate, valerate, ammonia-N (NH3-N) concentration, urase activity, and papillae height (p < 0.05) and reduced the feed to gain (F:G), rumen bacterial protein (BCP), rumen lactic acid concentration, and papillae width (p < 0.05) of sheep. Increased RDS in the diet improved the daily matter intake, average daily gain, and rumen weight, reduced the F:G, and enhanced the rumen nitrogen capture rate by decreasing total amino acids and the NH3-N concentration to increase BCP, aquaporins 3 gene, and protein expression. The rumen microbiota also changed as the HR diet reduced the Chao index (p < 0.05). The metabolomics analysis showed that feeding AS upregulated the rumen tryptophan metabolism and steroid hormone biosynthesis, while the purine metabolism, linoleic acid metabolism, and amino acid biosynthesis were downregulated. Furthermore, increased RDS in the diet upregulated rumen lysine degradation and sphingolipid metabolism, while aromatic amino acid biosynthesis was downregulated. Additionally, the correlation analysis results showed that ADG was positively correlated with 5-aminopentanoic acid, and three microorganisms (unclassified_f__Selenomonadaceae, Quinella, Christensenellaceae_R-7_group) were positively correlated with the rumen isobutyrate, valerate, NH3-N concentration, urase activity, tryptophan metabolism, and steroid hormone biosynthesis and negatively correlated with linoleic acid metabolism and amino acid biosynthesis in sheep. In summary, increased RDS in the diet improved the growth performance and rumen N utilization and reduced bacterial diversity in sheep. The alfalfa silage diet only increased feed efficiency; it did not affect growth performance. Additionally, it decreased rumen nitrogen utilization, linoleic acid, and amino acid biosynthesis. Nevertheless, there were limited interactions between forage and RDS; increased RDS in the AS diet enhanced the nitrogen capture rate of rumen microorganisms for alfalfa silage, with only slight improvements in the purine metabolism, linoleic acid, and amino acid synthesis. Full article
(This article belongs to the Special Issue Application of Metabolomics in Animal Nutrition Research)
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<p>Rumen epithelium photograph and HE staining of rumen epithelia (40×). AHLR: alfalfa hay and low RDS, AHHR: alfalfa hay and high RDS, ASLR: alfalfa silage and low RDS, ASHR: alfalfa silage and high RDS.</p>
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<p>Rumen fermentation and its relationship with growth performance. TAA: total amino acid. BCP: bacterial protein. DP: dressing percentage. RW: rumen weight. RPH: rumen papillae height. RPW: rumen papillae width. “*”, “**”, and “***” mean significant differences and “<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”, respectively.</p>
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<p>The mRNA and protein expression of urea transporters in rumen epithelium. (<b>A</b>) The mRNA expression of urea transporters in rumen epithelium. (<b>B</b>) WB of AQP3 in rumen epithelium. (<b>C</b>) The protein expression of AQP3 in rumen epithelium. AHLR: alfalfa hay and low RDS, AHHR: alfalfa hay and high RDS, ASLR: alfalfa silage and low RDS, ASHR: alfalfa silage and high RDS. P (F) = alfalfa hay versus alfalfa silage (AH vs. AS); P (R) = low RDS versus high RDS (LR vs. HR); P (F × R) = forage by RDS interaction.</p>
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<p>The protein distribution of urea transporters in rumen epithelium. (<b>A</b>–<b>D</b>) Rumen distribution of AQP3, AQP7, AQP10, and UT-B protein, respectively. AHLR: alfalfa hay and low RDS, AHHR: alfalfa hay and high RDS, ASLR: alfalfa silage and low RDS, ASHR: alfalfa silage and high RDS.</p>
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<p>Effect of rumen-degradable starch on alpha diversity indexes of rumen bacteria in sheep fed AH or AS. (<b>A</b>) Bacterial taxa averaged under phyla. (<b>B</b>) Bacterial taxa averaged under genera. (<b>C</b>) Principal coordinate analysis (PCoA) of beta diversity. (<b>D</b>) Chao index of alpha diversity. (<b>E</b>) Shannon index of alpha diversity. AHLR: alfalfa hay and low RDS, AHHR: alfalfa hay and high RDS, ASLR: alfalfa silage and low RDS, ASHR: alfalfa silage and high RDS. “*” means a significant difference “<span class="html-italic">p</span> &lt; 0.05”.</p>
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<p>Rumen metabolome analysis. (<b>A</b>) Principal coordinate analysis (PCA). (<b>B</b>) Partial least square discriminant analysis (PLS-DA) score. (<b>C</b>) Heatmap of the top 200 differential metabolites in rumen fluids among AHLR, AHHR, ASLR, and ASHR. (<b>D</b>) Venn diagrams show the number of common and unique features of the three comparison pairs. AHLR: alfalfa hay and low RDS, AHHR: alfalfa hay and high RDS, ASLR: alfalfa silage and low RDS, ASHR: alfalfa silage and high RDS.</p>
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<p>Differential metabolic pathway analysis. (<b>A</b>) The heatmap of differential metabolites among three comparison pairs and the biochemical pathways involved. (<b>B</b>) The variable importance in projection (VIP) scores in the three comparison pairs (AHLR vs. AHHR, AHLR vs. ASLR, and AHLR vs. ASHR), respectively (<b>B</b>–<b>D</b>). Top 15 significant features (<span class="html-italic">p</span> &lt; 0.05) based on VIP scores. Relevant metabolic pathways involved in the divergence among three comparison pairs (AHLR vs. AHHR, AHLR vs. ASLR, and AHLR vs. ASHR), respectively (<b>E</b>–<b>G</b>). AHLR: alfalfa hay and low RDS, AHHR: alfalfa hay and high RDS, ASLR: alfalfa silage and low RDS, ASHR: alfalfa silage and high RDS.</p>
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<p>The metabolic pathway reconstruction graphs of the starch, nitrogen, and lipid in the rumen of sheep fed different forage and grain sources. AHLR: alfalfa hay and low RDS, AHHR: alfalfa hay and high RDS, ASLR: alfalfa silage and low RDS, ASHR: alfalfa silage and high RDS. The red line, blue line, yellow line, green line, and black line represent the metabolic pathway of AH rich, AS rich, low RDS rich, high RDS rich, and not enriched, respectively. Each heatmap indicates different metabolites associated with metabolic pathways, and each is presented in <a href="#app1-animals-15-00034" class="html-app">Supplementary File S3, Table S6</a> in detail. PPP: pentose phosphate pathway. EMP: Embden–Meyerhof pathway. VFA: volatile fatty acid. TCA: tricarboxylic acid cycle. MCP: bacterial protein.</p>
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<p>The relationship among significantly different growth performance and rumen fermentation parameters, microbial genera (top ten microbial genera in relative abundance), and rumen differential metabolites. (<b>A</b>) Spearman’s correlation analysis between rumen bacteria and phenotypic traits. (<b>B</b>) Spearman’s correlation analysis between rumen differential metabolites and phenotypic traits. (<b>C</b>) Spearman’s correlation analysis between rumen differential metabolites and bacteria. TAA: total amino acid; BCP: bacterial protein. DP: dressing percentage. RW: rumen weight. “*”, “**”, and “***” mean significant differences and “<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”, respectively.</p>
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28 pages, 10378 KiB  
Article
Effects of Long-Term Fasting on Gut Microbiota, Serum Metabolome, and Their Association in Male Adults
by Feng Wu, Yaxiu Guo, Yihua Wang, Xiukun Sui, Hailong Wang, Hongyu Zhang, Bingmu Xin, Chao Yang, Cheng Zhang, Siyu Jiang, Lina Qu, Qiang Feng, Zhongquan Dai, Chunmeng Shi and Yinghui Li
Nutrients 2025, 17(1), 35; https://doi.org/10.3390/nu17010035 - 26 Dec 2024
Viewed by 337
Abstract
Background: Long-term fasting demonstrates greater therapeutic potential and broader application prospects in extreme environments than intermittent fasting. Method: This pilot study of 10-day complete fasting (CF), with a small sample size of 13 volunteers, aimed to investigate the time-series impacts on gut microbiome, [...] Read more.
Background: Long-term fasting demonstrates greater therapeutic potential and broader application prospects in extreme environments than intermittent fasting. Method: This pilot study of 10-day complete fasting (CF), with a small sample size of 13 volunteers, aimed to investigate the time-series impacts on gut microbiome, serum metabolome, and their interrelationships with biochemical indices. Results: The results show CF significantly affected gut microbiota diversity, composition, and interspecies interactions, characterized by an expansion of the Proteobacteria phylum (about six-fold) and a decrease in Bacteroidetes (about 50%) and Firmicutes (about 34%) populations. Notably, certain bacteria taxa exhibited complex interactions and strong correlations with serum metabolites implicated in energy and amino acid metabolism, with a particular focus on fatty acylcarnitines and tryptophan derivatives. A key focus of our study was the effect of Ruthenibacterium lactatiformans, which was highly increased during CF and exhibited a strong correlation with fat metabolic indicators. This bacterium was found to mitigate high-fat diet-induced obesity, glucose intolerance, dyslipidemia, and intestinal barrier dysfunction in animal experiments. These effects suggest its potential as a probiotic candidate for the amelioration of dyslipidemia and for mediating the benefits of fasting on fat metabolism. Conclusions: Our pilot study suggests that alterations in gut microbiota during CF contribute to the shift of energy metabolic substrate and the establishment of a novel homeostatic state during prolonged fasting. Full article
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<p>The impact of 10-day complete fasting on human gut microbiota’s diversity, difference and composition. (<b>A</b>) A schematic of the study design and the time points of sample collection. (<b>B</b>) Comparison of alpha-diversity based on Shannon index in the gut microbiota at different time points. (<b>C</b>) Principal coordinate analysis (PCoA) plot of the gut microbiota during complete fasting experiment based on the Bray–Curtis distances. (<b>D</b>) The distribution of Bray–Curtis distances from samples among the different courses in the complete fasting experiment based on the abundance. (<b>E</b>,<b>G</b>,<b>H</b>) The stacked bar plot showed the relative abundance of the gut microbiota at the phylum (<b>E</b>), genus (<b>G</b>), and species (<b>H</b>) levels, respectively. Each bar represents the mean of all detected samples at each time point. (<b>F</b>) The Firmicutes to Bacteroidetes ratio at each time point. Boxes and whiskers showed quartiles with outliers as individual points. * <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; Significant difference (<span class="html-italic">p</span> &lt; 0.05) determined by Wilcox test. (<b>B</b>,<b>D</b>,<b>F</b>), PERMANOVA test. (<b>C</b>) BF: before fasting; CF3<sup>:</sup> 3rd day of complete fasting; CF9<sup>:</sup> 9th day of complete fasting; CR3<sup>:</sup> 3rd day of calorie restriction; FR5<sup>:</sup> 5th day of full recovery. Sample number is 13 at BF3 and FR5, 7 at CF3, 6 at CF9, and 12 at CR3.</p>
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<p>Ten-day complete fasting induced the different change profiles of human gut species. (<b>A</b>) Clustered profiles of changed species across the complete fasting times inferred by STEM analysis. Statistically significant profiles (<span class="html-italic">p</span> &lt; 0.05) are represented in color. Similar colors represented the same type of change profile. The upper left number is the profile ID, and the lower left number presents the species count in each box. (<b>B</b>) Heatmap of the relative abundance of the species with significant difference (based on the Permutation test) using log10(X + min(X [X! = 0]) (X: the relative abundance of the species)) by R with colors gradually changing from blue to red, corresponding to low and high relative abundance, respectively, and trend (based on the STEM analysis) (<span class="html-italic">p</span> &lt; 0.05) during the complete fasting experiment. (<b>C</b>) The ridgeline plot shows the top 10 most abundant species in the heatmap. BF: before fasting; CF3: 3rd day of complete fasting; CF9: 9th day of complete fasting; CR3: 3rd day of calorie restriction; FR5: 5th day of full recovery.</p>
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<p>Ten-day CF impacted the correlation of the human gut microbiota. (<b>A</b>) Network analysis of the interactions between the differential species based on the Spearman correlation coefficients (|r| ≥ 0.8 and <span class="html-italic">p</span> &lt; 0.05). The fill color of the circles and diamonds represented the corresponding phylum. <span class="html-italic">Ethanoligenens harbinense</span> and <span class="html-italic">Intestinimonas butyriciproducens</span> connected with more species. (<b>B</b>) The relative abundance fluctuations of the species in the correlation network over the five time points. The node size positively correlated with its relative abundance. (<b>C</b>) The ridgeline plot shows the network topological parameters. BF: before fasting; CF3: 3rd day of complete fasting; CF9: 9th day of complete fasting; CR3: 3rd day of calorie restriction; FR5: 5th day of full recovery.</p>
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<p>Ten-day complete fasting restructured serum metabolome. (<b>A</b>) The principal component analysis (PCA) plot of serum metabolites during the CF experiments based on Bray–Curtis distances. (<b>B</b>) Clustered results in the serum metabolites with K-Means. (<b>C</b>) Significant change profiles (<span class="html-italic">p</span> &lt; 0.05) of serum metabolites in ESI+ and ESI− modes across the different time points by STEM analysis. The upper left number was the profile ID, and the lower left number presented the metabolite count in each box. (<b>D</b>) Metabolite set enrichment analysis (MSEA) of the significantly enriched and affected metabolic pathways for the serum metabolites (both ESI+ and ESI−) with an increasing trend. (<b>E</b>,<b>G</b>,<b>H</b>) The top 25 enriched metabolic pathways of differential metabolites at CF3 (<b>E</b>), CF6 (<b>G</b>), and CF9 (<b>H</b>) using the MetaboAnalyst metabolic pathway analysis tool. (<b>F</b>) Venn diagram of the number of enriched metabolic pathways among CF3, CF6, and CF9. The 12 common pathways were highlighted in Subfigure (<b>E</b>) with a red underline. ESI+: positive electrospray ionization; ESI−: negative electrospray ionization; BF: before fasting; CF3: 3rd day of complete fasting; CF9: 9th day of complete fasting; CR3: 3rd day of calorie restriction; FR5: 5th day of full recovery.</p>
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<p>The relationship between differential metabolites and gut microbiota during a 10-day complete fast. (<b>A</b>) Network diagram of the correlation between gut microbiota and serum metabolites. The circle and diamond represented the bacteria species and metabolites, respectively. The node size positively correlates with relative abundance. The fill color of the circle represented its corresponding phylum. The line thickness was proportional to the absolute value of the correlation coefficient (|r| &gt; 0.8 and <span class="html-italic">p</span> &lt; 0.05). The red line means a positive correlation, and the blue line means a negative correlation. (<b>B</b>) The relative abundance fluctuations of gut microbiota and serum metabolites in the correlation network over the five time points. The node size positively correlates with relative abundance. (<b>C</b>) Venn diagram of the metabolite number from host or bacteria. BF: before fasting; CF3: 3rd day of complete fasting; CF9: 9th day of complete fasting; CR3: 3rd day of calorie restriction; FR5: 5th day of full recovery.</p>
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<p>Correlation analysis between serum differential metabolites or fat metabolism relative biochemical indexes (BCIs) and differential gut microbiota during the 10-day complete fasting. (<b>A</b>) The percent of gut microbiota counts with |r| &gt; 0.6 and <span class="html-italic">p</span> &lt; 0.05 with serum metabolites. The correlation diagram between <span class="html-italic">Ruthenibacterium lactatiformans</span> and fat acylcarnitine (<b>B</b>) Hexadec-2-enoylcarnitine, (<b>C</b>) vaccenylcarnitine, (<b>D</b>) L-palmitoylcarnitine, (<b>E</b>) L-acetylcarnitine, and (<b>F</b>) 5-tetradecenoylcarnitine) with |r| &gt; 0.7 and its relative abundance changes of <span class="html-italic">Ruthenibacterium lactatiformans</span> (<b>G</b>). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, VS. BF, n = 13. (<b>H</b>) The percent of gut microbiota counts with |r| &gt; 0.6 and <span class="html-italic">p</span> &lt; 0.05 with fat metabolism relative to BCIs. (<b>I</b>) The percent of fat metabolism relative BCIs counts with |r| &gt; 0.6 and <span class="html-italic">p</span> &lt; 0.05 with gut microbiota. (<b>J</b>,<b>K</b>) Correlation diagram of low-density lipoprotein cholesterol (LDL-C) and total cholesterol (CHOL) with <span class="html-italic">Intestinimonas butyriciproducens</span>. (<b>L</b>,<b>M</b>) The redundancy analysis (RDA) between differential gut microbiota and BCIs during fasting. BF: before fasting; CF3: 3rd day of complete fasting; CF9: 9th day of complete fasting; CR3: 3rd day of calorie restriction; FR5: 5th day of full recovery.</p>
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<p>Serum level changes of tryptophan derivative metabolites and enzymes during 10-day complete fasting. (<b>A</b>) Serotonin detected by ELISA; (<b>B</b>,<b>C</b>,<b>E</b>,<b>G</b>–<b>I</b>) The relative abundance changes original from metabolome. (<b>D</b>,<b>F</b>) The correction between indolelactic acid, indoline, and <span class="html-italic">Ruthenibacterium lactatiformans</span>. (<b>J</b>,<b>K</b>) The relative abundance of Tryptophan metabolic enzymes original from metagenome sequencing. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, VS. BF. BF: before fasting (n = 13); CF3: 3rd day of complete fasting (n = 13 for serum and n = 7 for fecal); CF6: 6th day of complete fasting (n = 13); CF9: 9th day of complete fasting (n = 13 for serum and n = 6 for fecal); CR3: 3rd day of calorie restriction (n = 13 for serum and n = 12 for fecal); FR5: 5th day of full recovery (n = 13).</p>
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<p>The protective effects of <span class="html-italic">Ruthenibacterium lactatiformans</span> and ILA on HFD-induced obesity and metabolic abnormalities. Mice were fed an HFD or co-administrated <span class="html-italic">Ruthenibacterium lactatiformans</span> or ILA alternately every other day for 9 weeks. (<b>A</b>) body weight every week. (<b>B</b>) body weight gain percent at 9th week. The weight of epididymal fat pad (<b>C</b>) and liver (<b>D</b>). The HE staining (<b>E</b>) of epididymal fat and adipocyte size analysis (<b>F</b>). The serum concentration of CHOL (<b>G</b>), triglycerides (<b>H</b>), low-density lipoprotein (<b>I</b>), high-density lipoprotein (<b>J</b>), D-Lactate (<b>L</b>) and Diamine oxidase (<b>M</b>). (<b>K</b>) intraperitoneal glucose tolerance test. CN: with control diet, HFD: high-fat diet, ILA: indolelactic acid, RL: <span class="html-italic">Ruthenibacterium lactatiformans,</span> CHOL: total cholesterol, TG: triglycerides, LDL: low-density lipoprotein, HDL: high-density lipoprotein. AUC: area under curve, IGTT: intraperitoneal glucose tolerance test. ## <span class="html-italic">p</span> &lt; 0.01, vs. HFD; * <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, n = 6 for CN group and n = 8 for other groups.</p>
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18 pages, 2397 KiB  
Article
Kaempferol and Vitamin E Improve Production Performance by Linking the Gut–Uterus Axis Through the Reproductive Hormones and Microbiota of Late-Laying Hens
by Jing Zhang, Jie Zhang, Kangle Li, Xinyue Fu, Yanhui Liang, Minling Zhang, Shaolong Zhuang and Yuyun Gao
Animals 2025, 15(1), 15; https://doi.org/10.3390/ani15010015 - 25 Dec 2024
Viewed by 190
Abstract
This study evaluated the effects of kaempferol (KAE), and vitamin E (VE) on the performance, reproductive hormones, and the composition of the cecum and uterus microbiota in late-laying hens. A total of 192 49-week-old Jinghong No. 1 laying hens were randomly divided into [...] Read more.
This study evaluated the effects of kaempferol (KAE), and vitamin E (VE) on the performance, reproductive hormones, and the composition of the cecum and uterus microbiota in late-laying hens. A total of 192 49-week-old Jinghong No. 1 laying hens were randomly divided into four groups, with six replicates in each group and eight laying hens in each replicate, pre-reared for one week and formally tested for ten weeks. The CON group was fed basal diets, the VE group, the KAE group, and the KAE + VE group were fed a basal diet to which was added 0.2 g/kg VE, 0.4 g/kg KAE, and 0.2 g/kg VE + 0.4 g/kg KAE, respectively. The results are as follows. Compared to the CON group, the VE group, the KAE group, and the KAE + VE group significantly increased the egg production rate, average daily egg weight and significantly decreased the feed-to-egg ratio. The VE + KAE group significantly improved the Haugh unit. The VE group, the KAE group, and the KAE + VE group considerably enhanced the eggshell strength, eggshell relative weight, eggshell thickness, yolk color, and relative yolk weight. The serum E2 and LH levels of the KAE group and the KAE + VE group and the serum FSH levels of the KAE + VE group were significantly higher. In the ovary, the KAE group and the KAE + VE group’s ESR1 gene expression levels were significantly higher, and the KAE + VE group’s FSHR gene expression levels were markedly higher. In the uterus, the KAE group and the KAE + VE group’s ESR1 gene expression levels were dramatically higher, and the KAE + VE group’s ESR2 and FSHR gene expression levels were significantly higher. 16S rRNA gene sequencing revealed a significant aggregation of cecum and uterus colonies in the Beta diversity PCoA. In the cecum, Firmicutes, Bacteroidetes, and WPS-2 were the dominant phylums. In the uterus, the Firmicutes, Proteobacteria, and Bacteroidetes were the dominant phylums. The KAE + VE group’s F/B was significantly higher at the phylum level than in the CON group and the VE group. In summary, the addition of VE and KAE to the diet can improve the production performance of late-laying hens, increase the content of reproductive hormones, and stabilize the cecal and uterus microbiota, which may be related to the hormone and microbiota linkage of the gut–uterus axis. Full article
(This article belongs to the Section Poultry)
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<p>Effects of VE and KAE on serum hormone levels of late-laying hens. (<b>a</b>) E<sub>2</sub>, (<b>b</b>) FSH, (<b>c</b>) PROG, (<b>d</b>) LH. Different lowercase letter superscripts mean notable differences (<span class="html-italic">p</span> &lt; 0.05). Values with the same or no letters mean no significant difference (<span class="html-italic">p</span> ≥ 0.05). Data are presented as the mean ± SD.</p>
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<p>Effects of VE and KAE on gene expression levels of reproductive hormone receptors in ovary and uterus of late-laying hens. (<b>a</b>) Ovary, (<b>b</b>) Uterus. Different lowercase letter superscripts mean notable differences (<span class="html-italic">p</span> &lt; 0.05), while different capital letters show significant differences (<span class="html-italic">p</span> &lt; 0.01). Values with the same or no letters mean no significant difference (<span class="html-italic">p</span> ≥ 0.05). Data are presented as the mean ± SD.</p>
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<p>Effects of VE and KAE on cecal microbiota in late-laying hens. (<b>a</b>) Rank Abundance curve. (<b>b</b>) Rarefaction curve. (<b>c</b>) Venn diagram. (<b>d</b>) Principal coordinate analysis (PCoA) based on Bray–Curtis distance. (<b>e</b>) Relative abundance in composition at phylum level.</p>
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<p>Effects of VE and KAE on cecal microbiota in late-laying hens. (<b>a</b>) Rank Abundance curve. (<b>b</b>) Rarefaction curve. (<b>c</b>) Venn diagram. (<b>d</b>) Principal coordinate analysis (PCoA) based on Bray–Curtis distance. (<b>e</b>) Relative abundance in composition at phylum level.</p>
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<p>Effects of VE and KAE on uterus microbiota in late-laying hens. (<b>a</b>), Rank Abundance curve. (<b>b</b>) Rarefaction curve. (<b>c</b>) Venn diagram. (<b>d</b>) Principal coordinate analysis (PCoA) based on Bray−Curtis distance. (<b>e</b>) Relative abundance in composition at phylum level.</p>
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<p>Effects of VE and KAE on uterus microbiota in late-laying hens. (<b>a</b>), Rank Abundance curve. (<b>b</b>) Rarefaction curve. (<b>c</b>) Venn diagram. (<b>d</b>) Principal coordinate analysis (PCoA) based on Bray−Curtis distance. (<b>e</b>) Relative abundance in composition at phylum level.</p>
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23 pages, 10675 KiB  
Article
The Effects of Novel Co-Amorphous Naringenin and Fisetin Compounds on a Diet-Induced Obesity Murine Model
by Sarai Vásquez-Reyes, Miranda Bernal-Gámez, Jorge Domínguez-Chávez, Karina Mondragón-Vásquez, Mónica Sánchez-Tapia, Guillermo Ordaz, Omar Granados-Portillo, Diana Coutiño-Hernández, Paulina Barrera-Gómez, Nimbe Torres and Armando R. Tovar
Nutrients 2024, 16(24), 4425; https://doi.org/10.3390/nu16244425 - 23 Dec 2024
Viewed by 652
Abstract
Background/Objective: In recent studies, it has been shown that dietary bioactive compounds can produce health benefits; however, it is not known whether an improvement in solubility can enhance their biological effects. Thus, the aim of this work was to study whether co-amorphous (CoA) [...] Read more.
Background/Objective: In recent studies, it has been shown that dietary bioactive compounds can produce health benefits; however, it is not known whether an improvement in solubility can enhance their biological effects. Thus, the aim of this work was to study whether co-amorphous (CoA) naringenin or fisetin with enhanced solubility modify glucose and lipid metabolism, thermogenic capacity and gut microbiota in mice fed a high-fat, high-sucrose (HFSD) diet. Methods: Mice were fed with an HFSD with or without CoA-naringenin or CoA-fisetin for 3 months. Body weight, food intake, body composition, glucose tolerance, hepatic lipid composition and gut microbiota were assessed. Results: CoA-naringenin demonstrated significant reductions in fat-mass gain, improved cholesterol metabolism, and enhanced glucose tolerance. Mice treated with CoA-naringenin gained 45% less fat mass and exhibited improved hepatic lipid profiles, with significant reductions seen in liver triglycerides and cholesterol. Additionally, both CoA-flavonoids increased oxygen consumption (VO2), contributing to enhanced energy expenditure and improved metabolic flexibility. Thermogenic activation, indicated by increased UCP1 and PGC-1α levels, was observed with CoA-fisetin, supporting its role in fat oxidation and adipocyte size reduction. Further, both CoA-flavonoids modulated gut microbiota, restoring diversity and promoting beneficial bacteria, such as Akkermansia muciniphila, which has been linked to improved metabolic health. Conclusions: These findings suggest that co-amorphous naringenin or fisetin offers promising applications in improving solubility, metabolic health, and thermogenesis, highlighting the potential of both as therapeutic agents against obesity and related disorders. Full article
(This article belongs to the Section Nutrition and Obesity)
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Graphical abstract

Graphical abstract
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<p>Solubility of CoA flavonoids. Solubility graphs of (<b>A</b>) naringenin (NAR) and CoA-naringenin (NAR:Arg) and (<b>B</b>) fisetin (FST) and CoA-fisetin (FST:Arg) at different pH values. The image in each graph shows the solubility of CoA-naringenin and CoA-fisetin in water before plasticization. Results are shown as mean ± S.E.M.</p>
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<p>Effects of CoA-flavonoids in body weight, food intake, percentage of fat mass and percentage of lean mass, in mice with AIN-93 diet and HFSD. (<b>A</b>) Experimental design. (<b>B</b>) Body weight gain during the study, (<b>C</b>) body weight at the end of the study, (<b>D</b>) energy intake at the end of the study and (<b>E</b>) percentages of fat mass and (<b>F</b>) lean mass at the end of the study. Results are shown as means ± S.E.M. (<span class="html-italic">n</span> = 8–9 mice per group). One-way ANOVA was performed; letters indicate differences between groups (<span class="html-italic">p</span> = 0.05) a &gt; b &gt; c.</p>
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<p>Effects of CoA-flavonoids in serum concentrations of insulin, glucose, cholesterol, hepatic transaminases, urea and creatinine in mice with AIN-93 diet and HFSD. (<b>A</b>) Glucose, (<b>B</b>) Insulin, (<b>C</b>) Total Cholesterol, (<b>D</b>) LDL cholesterol, (<b>E</b>,<b>F</b>) Hepatic Transaminases, (<b>G</b>) Creatinine and (<b>H</b>) Urea. All parameters were measured at the end of the study by COBAS c111 Analyze; Insulin was measured using an EKISA Kit. Results are shown as the means ± S.E.M. (<span class="html-italic">n</span> = 8–9 mice per group). One-way ANOVA was performed; letters indicate differences between groups (<span class="html-italic">p</span> = 0.05) a &gt; b. Results are shown as means ± S.E.M.</p>
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<p>Measurement of energy expenditure and glucose tolerance in mice with an AIN-93 diet and HFSD with or without CoA-flavonoids. determined by indirect calorimetry. (<b>A</b>) Oxygen consumption (VO<sub>2</sub> mL/h) and (<b>B</b>) total oxygen consumption (VO<sub>2</sub> mL/h), (<b>C</b>) RER time course (VCO2/VO<sub>2</sub>) and (<b>D</b>) RER in fasting and feeding period, (<b>E</b>) Glucose tolerance test (GTT) and (<b>F</b>) area under the curve of GTT fed with a control or HFSD with or without CoA-flavonoids. The slope and intercept in mL/h or Kcal/h are indicated for each condition (<span class="html-italic">n</span> = 8–9 per group). One-way ANOVA was performed; letters indicate differences between groups (<span class="html-italic">p</span> = 0.05) a &gt; b &gt; c. Results are shown as means ± S.E.M.</p>
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<p>Effect of CoA-flavonoids on hepatic lipids. (<b>A</b>) H/E staining of liver; (<b>B</b>) quantification of total hepatic triglycerides and (<b>C</b>) total cholesterol; (<b>D</b>) hepatic determination of linolenic acid, DHA, and EPA and (<b>E</b>) linolenic acid, as well as the DHA+EPA ratio; (<b>F</b>) quantifications of palmitic and palmitoleic acid, (<b>G</b>) stearic and oleic acid and (<b>H</b>) saturated fatty acids, as determined by gas chromatography. All of the quantifications were determined for all mice fed with a control diet or HFSD, with or without CoA-flavonoids. Lipids samples were obtained through the Folch method. Results are shown as the means ± S.E.M. (<span class="html-italic">n</span> = 8–9 mice per group). One-way ANOVA was performed; letters indicate differences between groups (<span class="html-italic">p</span> = 0.05) a &gt; b. Results are shown as means ± S.E.M.</p>
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<p>Effect of CoA-flavonoids in white and brown adipose tissue metabolism. (<b>A</b>) H/E staining of WAT. (<b>B</b>) Size quantification of adipocytes and the (<b>C</b>) mean size of WAT, quantification performed by adiposoft. (<b>D</b>) Serum concentrations of adiponectin and (<b>E</b>) leptin, measured by an ELISA Kit. (<b>F</b>) Immunoblotting and densitometric analysis of UCP-1 and PGC1-α from iWAT, fed with a control and HFSD with or without CoA-flavonoids. Results are shown as the means ± S.E.M. (<span class="html-italic">n</span> = 4 per group). One-way ANOVA was performed; letters indicate differences between groups (<span class="html-italic">p</span> = 0.05) a &gt; b &gt; c. Results are shown as means ± S.E.M.</p>
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<p>Effect of CoA-flavonoids on intestinal microbiota. (<b>A</b>) Alpha diversity by Shannon index. (<b>B</b>) Beta diversity by Jaccard. (<b>C</b>) Relative abundance at the phylum level and relative abundance at the genus level for (<b>D</b>) <span class="html-italic">Akkermansia</span>, (<b>E</b>) <span class="html-italic">Bifidobacterium</span>, (<b>F</b>) <span class="html-italic">Roseburia</span>, (<b>G</b>) <span class="html-italic">Lactococcus</span>, (<b>H</b>) <span class="html-italic">Parasutterella</span>, (<b>I</b>) <span class="html-italic">Anerovorax</span>, (<b>J</b>) <span class="html-italic">Marvibryantia</span>, (<b>K</b>) <span class="html-italic">Paraprevotella</span> and (<b>L</b>) <span class="html-italic">Butyricicoccus</span>. Linear discriminant analyses comparing (<b>M</b>) C and HFSD groups, (<b>N</b>) Fisetin and HFSD groups, (<b>O</b>) Naringenin and HFSD groups and (<b>P</b>) Resveratrol and HFSD groups. Letters indicate differences between groups (<span class="html-italic">p</span> = 0.05) a &gt; b &gt; c &gt; d. Results are shown as means ± S.E.M.</p>
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<p>Correlogram of the intestinal microbiota and metabolic markers with CoA-flavonoids consumption. (<b>A</b>) Correlogram of CoA-naringenin consumption. (<b>B</b>) Correlograms of CoA-fisetin consumption and of (<b>C</b>) HFSD consumption. (Pearson correlation.).</p>
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14 pages, 8023 KiB  
Article
Effects of Oregano Essential Oil and/or Yeast Cultures on the Rumen Microbiota of Crossbred Simmental Calves
by Ting Liu, Zhihao Luo, Tao Zhang, Huan Chen, Xuejiao Yi, Jiang Hu, Bingang Shi, Yuxi An, Changze Cui and Xiangyan Wang
Animals 2024, 14(24), 3710; https://doi.org/10.3390/ani14243710 - 23 Dec 2024
Viewed by 265
Abstract
This study hypothesized that combining oregano essential oil (OEO) and yeast cultures (YCs) would modulate rumen microbiota to promote gastrointestinal homeostasis and function. Twenty-four newborn, healthy, disease-free, crossbred Simmental male calves (birth weight ≥ 35 kg) were assigned to [...] Read more.
This study hypothesized that combining oregano essential oil (OEO) and yeast cultures (YCs) would modulate rumen microbiota to promote gastrointestinal homeostasis and function. Twenty-four newborn, healthy, disease-free, crossbred Simmental male calves (birth weight ≥ 35 kg) were assigned to one of four treatments based on birth data. Treatments were as follows: (1) Control (CON), calves fed calf starter without additives; (2) OEO, calves fed calf starter containing 60 mg/kg body weight (BW) of OEO per day; (3) YCs, calves fed calf starter containing 45 mg/kg BW of YC per day; and (4) MIX, calves fed calf starter with OEO (60 mg/kg, BW) and YC (45 mg/kg, BW) combination. The experimental period lasted 70 days. Rumen fluid was collected on the final day, and 16S rRNA sequencing was performed to assess alterations in rumen microbiota. Calves fed MIX exhibited significantly greater microbial richness, species diversity, and lineage diversity (p < 0.05) compared with calves in the other groups. MIX-fed calves also showed changes (p < 0.05) in the relative abundance of certain rumen species, identified as through LEfSe analysis (LDA > 4, p < 0.05). These biomarkers included f_Rikenellaceae, g_Rikenellaceae_RC9_gut_group, g_Erysipelotrichaceae_UCG-002, c_Saccharimonadia, o_Saccharimonadales, f_Saccharimonadaceae, and g_Candidatus_Saccharimonas. Pathways enriched (p < 0.05) in MIX-fed calves involved nucleotide metabolism, lipid metabolism, glycan biosynthesis and metabolism, amino acid metabolism, terpenoids and polyketides metabolism, antimicrobial drug resistance, xenobiotic biodegradation and metabolism, antineoplastic drug resistance, and excretory system pathways. In conclusion, this study demonstrates that the OEO and YC combination enhances rumen microbial community modulation in calves more effectively than OEO or YCs fed individually or with the control diet. Full article
(This article belongs to the Section Cattle)
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<p>Pan–core species analysis. OTUs, or operational taxonomic units, cluster valid sequences obtained from sequencing, and cluster clean tags into OTUs at a default given similarity (default 97%). (<b>A</b>) Pan curves refer to pan OTUs and show how the number of all OTUs included in different samples varies as the number of samples increases. (<b>B</b>) Core curves refer to core OTUs and show how the number of shared OTUs present in different samples varies as the number of samples increases.</p>
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<p>(<b>A</b>) Venn species diagram. (<b>B</b>) Average variability of bacterial communities. Different colors represent different subgroups. The same color blocks represent different subgroups, overlapping sections represent the number of species common to the other groups, non-overlapping sections represent the number of species common to the various groups, and non-overlapping sections represent the number of species common to the different groups.</p>
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<p>Species Alpha Diversity: (<b>A</b>) Chao1 index: representing species richness. (<b>B</b>) Coverage index: representing species coverage. (<b>C</b>) Shannon index: representing species diversity. (<b>D</b>) Species lineage diversity Pd index: representing species lineage diversity. In the figure, * indicates a significant difference, ** indicates a highly significant difference.</p>
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<p>Species Beta Diversity: PCoA analysis and Principal coordinates were analyzed, with distances between colored circles representing similarities or differences in community composition between groups.</p>
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<p>Species composition at different levels (Top 10). The vertical coordinate is the proportion of species abundance in that sample, with different colored bars representing different species and the length of the bar representing the size of that species: (<b>A</b>) phylum-level species; (<b>B</b>) genus-level species.</p>
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<p>Analysis of species differences. The horizontal coordinate is the LDA value, and the vertical coordinate is the species name, with larger LDA scores representing a greater influence of species abundance on the differential effect.</p>
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<p>Species function prediction analysis. * means the difference is significant (<span class="html-italic">p</span> &lt; 0.05); ** means the difference is highly significant (<span class="html-italic">p</span> &lt; 0.01).</p>
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23 pages, 2016 KiB  
Article
The Gut Microbiota’s Role in Neurological, Psychiatric, and Neurodevelopmental Disorders
by Ioannis Alexandros Charitos, Angelo Michele Inchingolo, Laura Ferrante, Francesco Inchingolo, Alessio Danilo Inchingolo, Francesca Castellaneta, Antonella Cotoia, Andrea Palermo, Salvatore Scacco and Gianna Dipalma
Nutrients 2024, 16(24), 4404; https://doi.org/10.3390/nu16244404 - 22 Dec 2024
Viewed by 681
Abstract
Aim: This article aims to explore the role of the human gut microbiota (GM) in the pathogenesis of neurological, psychiatric, and neurodevelopmental disorders, highlighting its influence on health and disease, and investigating potential therapeutic strategies targeting GM modulation. Materials and Methods: A comprehensive [...] Read more.
Aim: This article aims to explore the role of the human gut microbiota (GM) in the pathogenesis of neurological, psychiatric, and neurodevelopmental disorders, highlighting its influence on health and disease, and investigating potential therapeutic strategies targeting GM modulation. Materials and Methods: A comprehensive analysis of the gut microbiota’s composition and its interaction with the human body, particularly, its role in neurological and psychiatric conditions, is provided. The review discusses factors influencing GM composition, including birth mode, breastfeeding, diet, medications, and geography. Additionally, it examines the GM’s functions, such as nutrient absorption, immune regulation, and pathogen defense, alongside its interactions with the nervous system through the gut–brain axis, neurotransmitters, and short-chain fatty acids (SCFAs). Results: Alterations in the GM are linked to various disorders, including Parkinson’s disease, multiple sclerosis, depression, schizophrenia, ADHD, and autism. The GM influences cognitive functions, stress responses, and mood regulation. Antibiotic use disrupts GM diversity, increasing the risk of metabolic disorders, obesity, and allergic diseases. Emerging therapies such as probiotics, prebiotics, and microbiota transplantation show promise in modulating the GM and alleviating symptoms of neurological and psychiatric conditions. Conclusions. The modulation of the GM represents a promising approach for personalized treatment strategies. Further research is needed to better understand the underlying mechanisms and to develop targeted therapies aimed at restoring GM balance for improved clinical outcomes. Full article
(This article belongs to the Special Issue Implications of Diet and the Gut Microbiome in Neuroinflammation)
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<p>The main taxa found during childhood. These taxa and some of their species are implicated in neurological and psychiatric diseases due to their increased population in the microbiota, such as <span class="html-italic">Actinomycetota</span> (<span class="html-italic">Bifidobacterium</span> spp.), <span class="html-italic">Verrucomicrobia</span> (<span class="html-italic">Akkermansia</span> spp.), <span class="html-italic">Bacillota</span> (<span class="html-italic">Faecalibacterium</span> spp.), <span class="html-italic">Bacteroidota</span> (such as <span class="html-italic">Prevotella</span> spp.), and <span class="html-italic">Fusobacteriota</span> (such as <span class="html-italic">Fusobacterium</span> spp.). Credits: Original figure by I.A. Charitos.</p>
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<p>The main bacteria at level of families found during childhood in the gut microbiota. Several species from these families have a connection with neurological and psychiatric diseases or disorders such as <span class="html-italic">Bacteroides</span> spp., <span class="html-italic">Doria</span> spp., <span class="html-italic">Bifidobacteria</span> spp., <span class="html-italic">Prevotella</span> spp. and others.</p>
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<p>The main genera found in pediatric population.</p>
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<p>The three enterotypes are recognized based on the predominant bacterium: (1) <span class="html-italic">Bacteroides</span>, (2) <span class="html-italic">Prevotella</span>, and (3) <span class="html-italic">Ruminococcus</span>. In the first intestinal type, <span class="html-italic">Slackia</span>, <span class="html-italic">Parabacteroides</span>, <span class="html-italic">Clostridiales</span>, <span class="html-italic">Alkaliphilus</span>, <span class="html-italic">Lactobacillus</span>, <span class="html-italic">Catenibacterium</span>, and <span class="html-italic">Geobacter coexist</span>. <span class="html-italic">Eggerthella</span>, <span class="html-italic">Veillonella</span>, <span class="html-italic">Ruminococcaceae</span>, <span class="html-italic">Holdemania</span>, <span class="html-italic">Peptostreptococcaceae</span>, <span class="html-italic">Staphylococcus</span>, <span class="html-italic">Leuconostoc</span>, <span class="html-italic">Desulfovibrionaceae</span>, <span class="html-italic">Rhodospirillum</span>, <span class="html-italic">Helicobacter</span>, <span class="html-italic">Escherichia</span>, <span class="html-italic">Shigella</span>, and <span class="html-italic">Akkermansia muciniphila</span> also occur in the second intestinal type. Credits: Original figure by I.A. Charitos The third enteric type also includes <span class="html-italic">Gordonibacter</span>, <span class="html-italic">Sphingobacterium</span>, <span class="html-italic">Staphylococcus</span>, <span class="html-italic">Marvinbryantia</span>, <span class="html-italic">Symbiobacterium</span>, <span class="html-italic">Dialister</span>, and <span class="html-italic">Akkermansia muciniphila</span>. Credits: Original figure by I.A. Charitos.</p>
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<p>The figure describes the hypotheses of how gut dysbiosis, due to emotional or stressful causes or not, can influence the bidirectional communication of the GBA, causing direct and indirect effects on the ENS and vice versa. Credits: Original figure by I.A. Charitos.</p>
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24 pages, 4556 KiB  
Article
Mosla Chinensis Extract Enhances Growth Performance, Antioxidant Capacity, and Intestinal Health in Broilers by Modulating Gut Microbiota
by Wei Wang, Yuyu Wang, Peng Huang, Junjuan Zhou, Guifeng Tan, Jianguo Zeng and Wei Liu
Microorganisms 2024, 12(12), 2647; https://doi.org/10.3390/microorganisms12122647 - 20 Dec 2024
Viewed by 409
Abstract
This study aimed to evaluate the effects of Mosla chinensis extract (MCE) on broiler intestinal health. A total of 240 1-day-old Arbor Acres (AA) broilers (balanced for sex) were randomly allocated into four treatment groups, each with six replicates of 10 chickens. The [...] Read more.
This study aimed to evaluate the effects of Mosla chinensis extract (MCE) on broiler intestinal health. A total of 240 1-day-old Arbor Acres (AA) broilers (balanced for sex) were randomly allocated into four treatment groups, each with six replicates of 10 chickens. The study comprised a starter phase (days 1–21) and a grower phase (days 22–42). The control group (C) received a basal diet, while the experimental groups were supplemented with low (S1, 500 mg/kg), medium (S2, 1000 mg/kg), and high doses (S3, 2000 mg/kg) of MCE. The results showed that MCE supplementation significantly improved average daily gain in broilers (p < 0.05) and reduced the feed-to-gain ratio in broilers. Additionally, MCE enhanced the anti-inflammatory and antioxidant capacity of broilers. In the duodenum and cecum, MCE significantly upregulated the expression of tight junction proteins Claudin-1, and Occludin, with the high-dose group showing the strongest effect on intestinal barrier protection (p < 0.05). There was no significant difference in ZO-1 in dudenum (p > 0.05). Microbial analysis indicated that MCE supplementation significantly reduced the Chao and Sobs indices in both the small and large intestines (p < 0.05). At the same time, the Coverage index of the small intestine increased, with the high-dose group demonstrating the most pronounced effect. Beta diversity analysis revealed that MCE had a significant modulatory effect on the microbial composition in the large intestine (p < 0.05), with a comparatively smaller impact on the small intestine. Furthermore, MCE supplementation significantly increased the relative abundance of Ruminococcaceae and Alistipes in the large intestine, along with beneficial genera that promote short-chain fatty acid (SCFA) production, thus optimizing the gut microecological environment. Correlation analysis of SCFAs further confirmed a significant association between the enriched microbiota and the production of acetate, propionate, and butyrate (p < 0.05). In conclusion, dietary supplementation with MCE promotes healthy growth and feed intake in broilers and exhibits anti-inflammatory and antioxidant effects. By optimizing gut microbiota composition, enhancing intestinal barrier function, and promoting SCFA production, MCE effectively maintains gut microecological balance, supporting broiler intestinal health. Full article
(This article belongs to the Special Issue Advances in Diet–Host–Gut Microbiome Interactions)
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<p>Experimental grouping of broiler chickens. This figure illustrates the randomized allocation of 240 Arbor Acres (AA) broiler chicks into four treatment groups, with each group containing six replicates of 10 chicks each. The dosing levels of M. chinensis extract for each group were based on established experimental protocols. The design ensures a balanced distribution of subjects to examine the effects on growth performance, serum biochemistry, antioxidant capacity, immune function, and gut microbiota over a 42-day period.</p>
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<p>Effects of M. chinensis extract on antioxidant activity in serum and liver of white feather broilers: (<b>A</b>) total antioxidant capacity (T-AOC) in the liver; (<b>B</b>) glutathione peroxidase (GSH-PX) activity in the liver; (<b>C</b>) superoxide dismutase (SOD) activity in the liver; (<b>D</b>) catalase (CAT) activity in the liver; (<b>E</b>) malondialdehyde (MDA) levels in the liver; (<b>F</b>) glutathione peroxidase (GSH-PX) activity in the serum; (<b>G</b>) malondialdehyde (MDA) levels in the serum; (<b>H</b>) nitric oxide (NO) levels in the serum. * <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>Effects of M. chinensis extract on the immune performance of white feather broilers: (<b>A</b>) serum IgA levels; (<b>B</b>) serum IgM levels; (<b>C</b>) serum IgG levels; (<b>D</b>) serum IL-4 levels; (<b>E</b>) serum IL-10 levels; (<b>F</b>) serum IFN-γ levels * <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.</p>
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<p>Effects of MCE on intestinal tight junction protein gene expression in white feather broilers: (<b>A</b>) ZO-1 expression in the duodenum; (<b>B</b>) Claudin-1 expression in the duodenum; (<b>C</b>) Occludin expression in the duodenum; (<b>D</b>) ZO-1 expression in the cecum; (<b>E</b>) Claudin-1 expression in the cecum. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Effects of MCE on short-chain fatty acid production in intestinal contents of white feather broilers: (<b>A</b>) acetic acid in the large intestine; (<b>B</b>) propionic acid in the large intestine; (<b>C</b>) butyric acid in the large intestine; (<b>D</b>) valeric acid in the large intestine; (<b>E</b>) acetic acid in the small intestine; (<b>F</b>) propionic acid in the small intestine. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Microbial composition analysis of intestinal contents: (<b>A</b>) gate level composition analysis of colonic contents; (<b>B</b>) gate level composition analysis of small intestine contents; (<b>C</b>) analysis of the genus level composition of the contents of the large intestine; (<b>D</b>) analysis of genus level composition of small intestine contents.</p>
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<p>Effects of dietary MCE on OTU counts in the intestinal microbiota of broilers: (<b>A</b>) OTU count changes in large intestinal contents; (<b>B</b>) OTU count changes in small intestinal contents.</p>
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<p>Effects of dietary MCE on alpha diversity of intestinal microbiota in broilers: (<b>A</b>) Chao index for large intestinal contents; (<b>B</b>) coverage index for large intestinal contents; (<b>C</b>) Simpson index for large intestinal contents; (<b>D</b>) Sobs index for large intestinal contents; (<b>E</b>) Chao index for small intestinal contents; (<b>F</b>) coverage index for small intestinal contents; (<b>G</b>) Simpson index for small intestinal contents; (<b>H</b>) Sobs index for small intestinal contents. * <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>Principal component analysis of intestinal microbiota at the genus level in broilers supplemented with MCE: (<b>A</b>) PCA plot of large intestinal microbiota; (<b>B</b>) PCoA plot of large intestinal microbiota; (<b>C</b>) PCA plot of small intestinal microbiota; (<b>D</b>) PCoA plot of small intestinal microbiota.</p>
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<p>Differential analysis of intestinal microbiota at the genus level in broilers supplemented with MCE: (<b>A</b>) bar chart of the large intestinal microbial community composition; (<b>B</b>) inter-group differential analysis of large intestinal microbiota; (<b>C</b>) bar chart of the small intestinal microbial community composition; (<b>D</b>) inter-group differential analysis of small intestinal microbiota. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Correlation analysis between short-chain fatty acids and microbiota in the large intestine. This figure presents the Spearman correlation analysis between short-chain fatty acids (SCFAs) and the microbial genera in the large intestine. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p>
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18 pages, 513 KiB  
Article
Status of Inappropriate Complementary Feeding and Its Associated Factors Among Infants of 9–23 Months
by Iqra Ashraf, Prince L. Bestman, Abdullah A. Assiri, Ghulam Mustafa Kamal, Jalal Uddin, Jiayou Luo, Khalid M. Orayj and Azfar A. Ishaqui
Nutrients 2024, 16(24), 4379; https://doi.org/10.3390/nu16244379 - 19 Dec 2024
Viewed by 449
Abstract
Background: Inappropriate complementary feeding during the first two years of life significantly impacts children’s health, increasing risks of malnutrition and illness. Methods: This study investigates factors influencing early feeding patterns among 600 mothers of children aged 9–23 months in selected hospitals in [...] Read more.
Background: Inappropriate complementary feeding during the first two years of life significantly impacts children’s health, increasing risks of malnutrition and illness. Methods: This study investigates factors influencing early feeding patterns among 600 mothers of children aged 9–23 months in selected hospitals in Punjab, Pakistan. Using a structured questionnaire, data were collected and analyzed, with associations measured by odds ratios (ORs) and 95% confidence intervals (CIs). Results: The results showed the key indicators of inappropriate complementary feeding among young children, including timely complementary feeding, minimum meal frequency, dietary diversity, and acceptable diet. The rates for these factors were found to be 60.3%, 32.7%, 24.6%, and 48.5%, respectively. The study identified several significant factors influencing these practices. Key predictors of inappropriate feeding included the order of birth, the mother’s employment status, parental education, the number of children, household income, maternal knowledge, and maternal health. Conclusion: The findings underscore that maternal education, employment, and health significantly influence complementary feeding. Targeted interventions and education programs are essential to support healthy feeding behaviors, especially for mothers facing challenges related to education, work, or health conditions. Addressing these practices can improve child health outcomes, contributing to economic growth and a healthier future for Pakistan’s youngest population. Full article
(This article belongs to the Special Issue Advances in Infant and Pediatric Feeding and Nutrition)
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<p>Prevalence of inappropriate complementary feeding by indicators.</p>
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13 pages, 957 KiB  
Article
Associations of Plant-Based Foods, Animal Products, and Selected Sociodemographic Factors with Gastroesophageal Reflux Disease Risk
by Ahlam El Shikieri, Zakaria Eltahir, Abdulmannan Aman and Mohamad Alhadramy
Int. J. Environ. Res. Public Health 2024, 21(12), 1696; https://doi.org/10.3390/ijerph21121696 - 19 Dec 2024
Viewed by 416
Abstract
Background: Diet influences the symptoms of gastroesophageal reflux disease (GERD). Plant-based diets rich in vegetables, fruits, legumes, seeds, and nuts may reduce inflammation and improve gut health, while high-fat foods may worsen symptoms. Objective: We examined the association between plant-based and animal-based foods, [...] Read more.
Background: Diet influences the symptoms of gastroesophageal reflux disease (GERD). Plant-based diets rich in vegetables, fruits, legumes, seeds, and nuts may reduce inflammation and improve gut health, while high-fat foods may worsen symptoms. Objective: We examined the association between plant-based and animal-based foods, selected demographic characteristics, and the likelihood of GERD in Al Madinah Al Munawarah, Saudi Arabia. Method: A cross-sectional study using the GerdQ tool assessed the GERD likelihood among 303 adults. Dietary diversity scores were used to assess the quality of their diet. quality. Results: The participants were predominantly women (68.6%) and had low education levels (88.4%). Cereals were the most consumed plant-based foods, while vitamin A-rich fruits and vegetables were the least consumed. There was significant variation in the consumption of legumes, nuts, seeds, and milk and milk products among the GERD groups. The participants with a 50% GERD likelihood had the highest consumption (34.5%), followed by the 89% likelihood group (21.4%) and the 79% likelihood group (14.5%). The lowest consumption of milk and milk products was among those with an 89% GERD likelihood who also consumed more organ meat. In addition, GERD likelihood was inversely associated with age (r = −0.153; p = 0.008). The likelihood of GERD was negatively correlated with the intake of legumes, nuts, and seeds (r = −0.163; p = 0.005). Furthermore, the intake of cereals and tubers (r = 0.114; p = 0.047) and legumes, nuts, and seeds (r = 0.231; p = 0.0001) increased significantly with education. Conclusion: GERD prevention programs should target women, those with a low education level, and individuals consuming fewer plant-based foods and more organ meats. Full article
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<p>GERD likelihood distribution among participants (n = 303) in the GERMS project conducted in Saudi Arabia. Most participants had a 50% likelihood of GERD.</p>
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<p>The distribution of participants’ dietary diversity scores (n = 303) in the GERMS project conducted in Saudi Arabia. The x-axis represents the scores, whereas the y-axis shows the number of participants. Most of the participants scored 3–4.</p>
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16 pages, 2226 KiB  
Article
Effects of the Ketogenic Diet on Microbiota Composition and Short-Chain Fatty Acids in Women with Overweight/Obesity
by Müge Güzey Akansel, Murat Baş, Cansu Gençalp, Meryem Kahrıman, Eray Şahin, Hakan Öztürk, Gürsel Gür and Ceren Gür
Nutrients 2024, 16(24), 4374; https://doi.org/10.3390/nu16244374 - 19 Dec 2024
Viewed by 609
Abstract
Background/Objectives: The ketogenic diet (KD) is a dietary model that can impact metabolic health and microbiota and has been widely discussed in recent years. This study aimed to evaluate the effects of a 6-week KD on biochemical parameters, gut microbiota, and fecal [...] Read more.
Background/Objectives: The ketogenic diet (KD) is a dietary model that can impact metabolic health and microbiota and has been widely discussed in recent years. This study aimed to evaluate the effects of a 6-week KD on biochemical parameters, gut microbiota, and fecal short-chain fatty acids (SCFAs) in women with overweight/obesity. Methods: Overall, 15 women aged 26–46 years were included in this study. Blood samples, fecal samples, and anthropometric measurements were evaluated at the beginning and end of this study. Results: After KD, the mean body mass index decreased from 29.81 ± 4.74 to 27.12 ± 4.23 kg/m2, and all decreases in anthropometric measurements were significant (p < 0.05). Fasting glucose, insulin, homeostasis model assessment of insulin resistance, hemoglobin A1C, urea, and creatinine levels decreased, whereas uric acid levels increased (p < 0.05). Furthermore, increased serum zonulin levels were noted (p = 0.001), whereas fecal butyrate, propionate, acetate, and total SCFA levels decreased (p < 0.05). When the changes in microbiota composition were examined, a decrease in beta diversity (p = 0.001) was observed. After the intervention, a statistically significant increase was noted in the Firmicutes/Bacteroidetes ratio (p = 0.001). Although Oscilibacter, Blautia, and Akkermensia relative abundances increased, Prevotella relative abundance and Bifidobacter abundance, which were the dominant genera before the KD, decreased. Moreover, the abundance of some pathogenic genera, including Escherichia, Klebsilella, and Listeria, increased. Conclusions: In healthy individuals, KD may cause significant changes in microbial composition, leading to dysbiosis and long-term adverse outcomes with changes in serum zonulin and fecal SCFA levels. Full article
(This article belongs to the Section Prebiotics and Probiotics)
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<p>(<b>a</b>) Alpha diversity of participants before and after KD; (<b>b</b>) Beta diversity of participants before and after KD.</p>
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<p>Relative abundance of the four most abundant phyla at the beginning and end of the KD.</p>
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<p>LDA scores at phylum level.</p>
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<p>Relative abundance of the 10 most abundant genera at the beginning and end of the KD.</p>
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<p>LDA scores at genus level.</p>
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26 pages, 4896 KiB  
Article
Oat Beta-Glucans Modulate the Gut Microbiome, Barrier Function, and Immune Responses in an In Vivo Model of Early-Stage Colorectal Cancer
by Magdalena Guzowska, Katarzyna Dziendzikowska, Łukasz Kopiasz, Małgorzata Gajewska, Jacek Wilczak, Joanna Harasym, Malwina Czerwińska and Joanna Gromadzka-Ostrowska
Int. J. Mol. Sci. 2024, 25(24), 13586; https://doi.org/10.3390/ijms252413586 - 19 Dec 2024
Viewed by 399
Abstract
Oat beta-glucans (OBGs) are known for their beneficial effects on gut health, including anti-inflammatory and prebiotic effects. The aim of this study was to evaluate the impact of two doses (1% or 3% w/w) of dietary low-molar-mass OBG supplementation on [...] Read more.
Oat beta-glucans (OBGs) are known for their beneficial effects on gut health, including anti-inflammatory and prebiotic effects. The aim of this study was to evaluate the impact of two doses (1% or 3% w/w) of dietary low-molar-mass OBG supplementation on colorectal cancer (CRC) development, immune cell profiles, intestinal barrier protein expression, and microbiota composition in a rat model of CRC induced by azoxymethane (AOM). Microbiome analysis revealed significant differences between the control and CRC groups. OBG supplementation influenced microbial diversity and abundance, particularly increasing the population of beneficial bacteria, such as Lachnospiraceae and Ruminococcaceae, associated with butyrate production. However, higher doses of OBG (3%) led to a decrease in butyrate-producing bacteria and a shift toward higher levels of Akkermansia muciniphila and Enterococcus faecalis. Immune cell profiling showed a higher percentage of T lymphocytes (CD3+) in rats fed a diet supplemented with 3% OBG, both in the intraepithelial (IEL) and lamina propria lymphocytes (LPLs). Immunohistochemical analysis of the large intestine revealed a significantly elevated expression of intestinal barrier proteins, i.e., claudin 3 and 4 in rats receiving 1% OBG, while claudin 7 expression was reduced in early-stage CRC. Gene expression analysis also revealed a significant downregulation of Cldn1 in CRC rats. These findings suggest that dietary OBG supplementation modulates the gut microbiota, immune response, and intestinal barrier integrity, with potential implications for nutritional CRC development prevention and treatment strategies. Full article
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<p>Microbiota composition at the taxonomic class levels of genus (<b>A</b>), phylum (<b>B</b>), and family (<b>C</b>). Cumulated bar plots represent cumulative microbiota composition in each group; C0—control group of rats fed a diet without OBG (n = 7); C1—control group of rats fed a diet supplemented with 1% OBG (n = 6); C3—control group of rats fed a diet supplemented with 3% OBG (n = 5); A0—CRC model rats group fed a diet without OBG (n = 8); A1—CRC model rats group fed a diet supplemented with 1% OBG (n = 7); A3—CRC model rats group fed a diet supplemented with 3% OBG (n = 7).</p>
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<p>Colorectum bacterial profiles of control and CRC model groups including the diet regime information. Data from 6 groups (3 control and 3 CRC model) are shown. (<b>A</b>) Global community structure, bacterial diversity, evenness, and richness represented by observed species richness, the Chao1 index, Pielou’s evenness index, and Shannon diversity. Data are presented as box and whisker plots. Correction for multiple comparisons was made using the Benjamini–Hochberge procedure (BH; threshold of 0.05); * adjusted <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; NS, not significant. (<b>B</b>) Hellinger-transformed species abundance data visualized with a principal component analysis (PCA) plot. The first two principal coordinate axes explain 27.52% and 10.6% of the variation, respectively. (<b>C</b>) Beta diversity assessed with Bray–Curtis visualized with a principal coordinate analysis (PCoA) plot. The first two principal coordinate axes explain 19.48% and 13.15% of the variation, respectively. ASV components with the highest significance are SV_1 <span class="html-italic">Akkermansia muciniphila</span>, SV_3 <span class="html-italic">Clostridium disporicum</span>, SV_4 <span class="html-italic">Leptogranulimonas caecicola</span>, SV_8 <span class="html-italic">Allobaculum</span> sp., and SV_13 <span class="html-italic">Blautia pseudococcoides.</span> Group description as in <a href="#ijms-25-13586-f001" class="html-fig">Figure 1</a>.</p>
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<p>Dissimilarity in community composition between samples quantified by distance or divergence (PERMANOVA analysis results). Discriminating species of groups A0 and C0 (top) and A3 and C3 (bottom). The species with distances with values (<span class="html-italic">x</span>-axis) below 0 (orange background) are enriched in control groups, and those with values above 0 (blue background) are enriched in CRC groups. Group description as in <a href="#ijms-25-13586-f001" class="html-fig">Figure 1</a>.</p>
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<p>The SCFA profile in colorectal content. Changes in the profile of butyric acid (<b>A</b>), hydroxybutyric acid (<b>B</b>), propionic acid (<b>C</b>) and lactic acid (<b>D</b>) are presented as percentage of total SCFA content. Significant differences from the control group (C0) (* <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) (Tukey’s post hoc test). Significant differences within the control (C) and CRC (A) groups between dietary subgroups (# <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) (Tukey’s post hoc test). Group description as in <a href="#ijms-25-13586-f001" class="html-fig">Figure 1</a>.</p>
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<p>The profile of immune cells in colon lamina propria lymphocytes (LPLs). The profile of T (CD3+) cells (<b>A</b>), B (CD3−CD45RA+) cells (<b>B</b>) and natural killer (NK)(CD45+CD161a+) cells (<b>C</b>) are presented as percentage in total lymphocytes, while the profile of Tc (CD3+CD4−CD8+) cells (<b>D</b>) and Th (CD3+CD4+CD8−) cells (<b>E</b>) are presented as percentage in T lymphocytes. Significant differences from the control group (C0) (* <span class="html-italic">p</span> &lt; 0.05) (Tukey’s post hoc test). Significant differences within the control (C) and CRC (A) groups between dietary subgroups (# <span class="html-italic">p</span> &lt; 0.05) (Tukey’s post hoc test). Group description as in <a href="#ijms-25-13586-f001" class="html-fig">Figure 1</a>.</p>
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<p>The profile of colon intraepithelial immune cells (IELs). The profile of T (CD3+) cells (<b>A</b>), B (CD3−CD45RA+) cells (<b>B</b>) and natural killer (NK)(CD45+CD161a+) cells (<b>C</b>) are presented as percentage in total lymphocytes, while the profile of Tc (CD3+CD4−CD8+) cells (<b>D</b>) and Th (CD3+CD4+CD8−) cells (<b>E</b>) are presented as percentage in T lymphocytes. Significant differences from the control group (C0) (** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001) (Tukey’s post hoc test). Significant differences within the control (C) and CRC (A) groups between dietary subgroups (# <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) (Tukey’s post hoc test). Group description as in <a href="#ijms-25-13586-f001" class="html-fig">Figure 1</a>.</p>
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<p>Gene and protein expression of claudins (CLD-1, 3, 4, 7) in the large intestine. Changes in the protein expression of CLD-1 (<b>A</b>), CLD-3 (<b>C</b>), CLD-4 (<b>E</b>), and CLD-7 (<b>G</b>) are presented as the integrated optical density (IOD) (mean ± SE). Light micrographs imaged (×400 magnification) (<b>I</b>). Brown precipitate indicates high expression of the analyzed claudins. Changes in the relative gene expression of <span class="html-italic">Cld1</span> (<b>B</b>), <span class="html-italic">Cld3</span> (<b>D</b>), <span class="html-italic">Cld4</span> (<b>F</b>), and <span class="html-italic">Cld7</span> (<b>H</b>) presented in arbitrary units as a ratio of the expression of the target gene to the mean expression of the reference genes (<span class="html-italic">B2m</span> and <span class="html-italic">Ldha</span>), with the control group calculated as 1. Significant differences from the control group (C0) (* <span class="html-italic">p</span> &lt; 0.05) (Tukey’s post hoc test). Group description as in <a href="#ijms-25-13586-f001" class="html-fig">Figure 1</a>.</p>
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<p>Gene and protein expression of claudins (CLD-1, 3, 4, 7) in the large intestine. Changes in the protein expression of CLD-1 (<b>A</b>), CLD-3 (<b>C</b>), CLD-4 (<b>E</b>), and CLD-7 (<b>G</b>) are presented as the integrated optical density (IOD) (mean ± SE). Light micrographs imaged (×400 magnification) (<b>I</b>). Brown precipitate indicates high expression of the analyzed claudins. Changes in the relative gene expression of <span class="html-italic">Cld1</span> (<b>B</b>), <span class="html-italic">Cld3</span> (<b>D</b>), <span class="html-italic">Cld4</span> (<b>F</b>), and <span class="html-italic">Cld7</span> (<b>H</b>) presented in arbitrary units as a ratio of the expression of the target gene to the mean expression of the reference genes (<span class="html-italic">B2m</span> and <span class="html-italic">Ldha</span>), with the control group calculated as 1. Significant differences from the control group (C0) (* <span class="html-italic">p</span> &lt; 0.05) (Tukey’s post hoc test). Group description as in <a href="#ijms-25-13586-f001" class="html-fig">Figure 1</a>.</p>
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<p>Scheme of the in vivo study. AOM—azoxymethane; CRC—colorectal cancer (AOM-induced early stage of colorectal carcinogenesis); 1% OBG—feed containing 1% (<span class="html-italic">w</span>/<span class="html-italic">w</span>) low-molar-mass oat beta-glucan; 3% OBG—feed containing 3% (<span class="html-italic">w</span>/<span class="html-italic">w</span>) low-molar-mass oat beta-glucan; OBG-—feed without low-molar-mass oat beta-glucan.</p>
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14 pages, 1944 KiB  
Article
Effects of Fructus Aurantii Extract on Growth Performance, Nutrient Apparent Digestibility, Serum Parameters, and Fecal Microbiota in Finishing Pigs
by Haiqing Gan, Qian Lin, Yecheng Xiao, Qiyu Tian, Chao Deng, Renjie Xie, Hongkun Li, Jiajie Ouyang, Xingguo Huang, Yang Shan and Fengming Chen
Animals 2024, 14(24), 3646; https://doi.org/10.3390/ani14243646 - 17 Dec 2024
Viewed by 339
Abstract
This study investigated the effects of Fructus Aurantii extract (FAE) on growth performance, nutrient apparent digestibility, serum parameters, fecal microbial composition, and short-chain fatty acids (SCFAs) in finishing pigs. In total, 75 Duroc × Landrace × Yorkshire pigs (equally divided by sex), with [...] Read more.
This study investigated the effects of Fructus Aurantii extract (FAE) on growth performance, nutrient apparent digestibility, serum parameters, fecal microbial composition, and short-chain fatty acids (SCFAs) in finishing pigs. In total, 75 Duroc × Landrace × Yorkshire pigs (equally divided by sex), with an initial body weight of 79.49 ± 4.27 kg, were randomly assigned to three treatment groups. The pigs were fed either a basic diet (CON) or a basal diet supplemented with 500 mg/kg of FAE (FAE500) and 1000 mg/kg of FAE (FAE1000). The FAE1000 group exhibited a significantly higher final body weight (FBW) (p < 0.05), and the average daily feed intake (ADFI) showed an increasing tendency in the FAE500 and FAE1000 groups (p = 0.056) compared to the CON group. Additionally, the inclusion of FAE resulted in the significantly higher apparent digestibility of crude ash (Ash), gross energy (GE), and crude protein (CP) (p < 0.05), with a tendency to the increased digestibility of dry matter (DM) (p = 0.053). Dietary FAE supplementation led to elevated serum levels of reduced glutathione (GSH) and decreased levels of serum L-lactic dehydrogenase (LDH), along with a tendency to increase serum glucose (GLU) levels (p = 0.084). The FAE500 group demonstrated higher serum concentrations of motilin (MTL) and gastrin (GAS) (p < 0.05), and a tendency for reduced serum glucagon-like peptide-1 (GLP-1) level (p = 0.055) compared to the CON group. Furthermore, alpha diversity analysis revealed that the FAE500 group significantly increased the Chao 1 and Observed_species indexes (p < 0.05). Similarly, beta diversity analysis indicated that FAE feeding altered the fecal microbial structure (p = 0.083). Notably, compared with the control group, CF231, Pediococcus, and Mogibacterium displayed higher relative abundance in the feces of the FAE500 group, whereas Tenericutes showed a reduction in relative abundance (p < 0.05). Additionally, the relative abundance of Tenericute was negatively correlated with the digestibility of DM, GE, Ash, and CP (p < 0.05). Serum MTL and GAS levels correlated positively with the Coprococcus, Dorea, Pediococcus, and Mogibacterium relative abundances (p < 0.05). Collectively, dietary FAE supplementation could enhance growth performance by boosting beneficial bacteria in feces, stimulating gastrointestinal hormone secretion, and improving nutrient digestibility. Full article
(This article belongs to the Section Pigs)
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<p>Effects of <span class="html-italic">Fructus Aurantii</span> extract (FAE) extract (FAE) on serum hormone level of finishing pigs. (<b>A</b>) Motilin concentration. (<b>B</b>) Gastrin concentration. (<b>C</b>) Glucagon-like peptide−1 concentration. CON = basal diet; FAE500 = basal diet containing 500 mg/kg <span class="html-italic">Fructus Aurantii</span> extract; FAE1000 = basal diet containing 1000 mg/kg <span class="html-italic">Fructus Aurantii</span> extract; (lowercase letters) values are represented as mean ± SEM, <span class="html-italic">n</span> = 4.</p>
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<p>The effect of dietary <span class="html-italic">Fructus Aurantii</span> extract (FAE) on microbiota diversity in finishing pigs. (<b>A</b>) Rarefaction curve based on the Chao 1 index. (<b>B</b>) Venn analysis of amplicon sequence variants (ASV). (<b>C</b>) the principal coordinate analysis (PCoA) plot of the bacterial community based on Bray–Curtis distances. Values are represented as mean ± SEM, <span class="html-italic">n</span> = 4. ASV, amplicon sequence variant; PCoA, principal coordinates analysis.</p>
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<p>Effects of <span class="html-italic">Fructus Aurantii</span> extract (FAE) on the taxon abundance of gut microbiota in finishing pigs (<b>A</b>,<b>B</b>). It presents the relative abundance of gut microbiota at both the phylum and genus levels, respectively (<b>C</b>–<b>F</b>). The relative abundance of differential bacteria among groups. The linear discriminant analysis (LDA) value distribution histogram of significantly distinct species (<b>G</b>) was derived from Lefse analysis. (lowercase letters) Values are represented as mean ± SEM, n = 4. LDA, linear discriminant analysis; Lefse, linear discriminant analysis—effect size.</p>
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<p>Spearman correlation analysis of gut microbiota with apparent digestibility and serum parameters of finishing pigs. Red and blue colors indicate positive and negative correlations, respectively. Significance is presented as * <span class="html-italic">p</span> &lt; 0.05 (<span class="html-italic">n</span> = 4).</p>
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14 pages, 1197 KiB  
Review
Maternal Gut Microbiome-Mediated Epigenetic Modifications in Cognitive Development and Impairments: A New Frontier for Therapeutic Innovation
by Shabnam Nohesara, Hamid Mostafavi Abdolmaleky, Faith Dickerson, Adrián A. Pinto-Tomás, Dilip V. Jeste and Sam Thiagalingam
Nutrients 2024, 16(24), 4355; https://doi.org/10.3390/nu16244355 - 17 Dec 2024
Viewed by 465
Abstract
Cognitive impairment in various mental illnesses, particularly neuropsychiatric disorders, has adverse functional and clinical consequences. While genetic mutations and epigenetic dysregulations of several genes during embryonic and adult periods are linked to cognitive impairment in mental disorders, the composition and diversity of resident [...] Read more.
Cognitive impairment in various mental illnesses, particularly neuropsychiatric disorders, has adverse functional and clinical consequences. While genetic mutations and epigenetic dysregulations of several genes during embryonic and adult periods are linked to cognitive impairment in mental disorders, the composition and diversity of resident bacteria in the gastrointestinal tract—shaped by environmental factors—also influence the brain epigenome, affecting behavior and cognitive functions. Accordingly, many recent studies have provided evidence that human gut microbiota may offer a potential avenue for improving cognitive deficits. In this review, we provide an overview of the relationship between cognitive impairment, alterations in the gut microbiome, and epigenetic alterations during embryonic and adult periods. We examine how various factors beyond genetics—such as lifestyle, age, and maternal diet—impact the composition, diversity, and epigenetic functionality of the gut microbiome, consequently influencing cognitive performance. Additionally, we explore the potential of maternal gut microbiome signatures and epigenetic biomarkers for predicting cognitive impairment risk in older adults. This article also explores the potential roles of nutritional deficiencies in programming cognitive disorders during the perinatal period in offspring, as well as the promise of gut microbiome-targeted therapeutics with epigenetic effects to prevent or alleviate cognitive dysfunctions in infants, middle-aged adults, and older adults. Unsolved challenges of gut microbiome-targeted therapeutics in mitigating cognitive dysfunctions for translation into clinical practice are discussed, lastly. Full article
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<p>Association between various factors (nutritional interventions, age, antibiotics, and environmental factors such as chemicals), changes in the composition of the gut microbiome, and cognitive performance.</p>
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<p>Gut microbiome-targeted therapeutics for improving cognitive impairments.</p>
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18 pages, 5017 KiB  
Article
Identification of Three POMCa Genotypes in Largemouth Bass (Micropterus salmoides) and Their Differential Physiological Responses to Feed Domestication
by Jie Hu, Jie Yang, Huan Zhong, Qifang Yu, Jun Xiao and Chun Zhang
Animals 2024, 14(24), 3638; https://doi.org/10.3390/ani14243638 - 17 Dec 2024
Viewed by 316
Abstract
Diverse feeding habits in teleosts involve a wide range of appetite-regulating factors. As an appetite-suppressing gene, the polymorphisms of POMCa in largemouth bass (Micropterus salmoides) were validated via sequencing and high-resolution melting (HRM). The frequency distribution of different POMCa genotypes were [...] Read more.
Diverse feeding habits in teleosts involve a wide range of appetite-regulating factors. As an appetite-suppressing gene, the polymorphisms of POMCa in largemouth bass (Micropterus salmoides) were validated via sequencing and high-resolution melting (HRM). The frequency distribution of different POMCa genotypes were analyzed in two populations, and physiological responses of different POMCa genotypes to feed domestication were investigated. The indel of an 18 bp AU-rich element (ARE) in the 3′ UTR and four interlocked SNP loci in the ORF of 1828 bp of POMCa cDNA sequence were identified in largemouth bass and constituted three genotypes of POMC-A I, II, and III, respectively. POMC-A I and Allele I had increased frequencies in the selection population than in the non-selection population (p < 0.01), 63.55% vs. 43.33% and 0.7850 vs. 0.6778, respectively. POMC-A I possessed the lowest value of POMCa mRNA during fasting (p < 0.05) and exhibited growth and physiological advantages under food deprivation and refeeding according to the levels of body mass and four physiological indicators, i.e., cortisol (Cor), growth hormone (GH), insulin-like growth factor-1 (IGF-1), and glucose (Glu). The identification of three POMCa genotypes, alongside their varying physiological responses during feed domestication, suggests a selective advantage that could be leveraged in molecular marker-assisted breeding of largemouth bass that are adapted to feeding on formula diet. Full article
(This article belongs to the Section Aquatic Animals)
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<p>Structure of <span class="html-italic">POMC</span> cDNA and phylogenetic analysis of amino acid sequences between largemouth bass and other Perciform fishes. (<b>A</b>) 1828 bp of <span class="html-italic">POMC</span> cDNA sequence in largemouth bass, including a 96 bp of 5′ UTR, a 1075 bp of 3′ UTR, and a 657 bp of ORF. (<b>B</b>) Phylogenetic analysis of <span class="html-italic">POMC</span> in Perciformes. (<b>C</b>) POMC structure was analyzed in largemouth bass and several other Perciform fishes. The arrows indicated a set of conserved four cysteine residues located at NPP of the molecule. The boxes indicated the core motifs of peptide precursors of the <span class="html-italic">POMC</span> gene family: the “YGGF” sequence of β-endorphin and a core sequence of “HFRW” in α-MSH and β-MSH peptides.</p>
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<p>The indel of an 18 bp ARE “ATATCAATATTGTCTCGG” was found in the 3′ UTR of LMB <span class="html-italic">POMCa</span>. (<b>A</b>) “RACE” represents the sequence of 3′ UTR amplified according to the protocol of the SMARTer RACE 5′/3′ Kit. #13 and #14 represent the sequences of 3′ UTR amplified by the primer pair of POMC-F1/R1. “*” represents identical. (<b>B</b>) Chromatogram of POMC-A I 3′ UTR with an 18 bp homozygous insertion. (<b>C</b>) Chromatogram of POMC-A II 3′ UTR with a coexistence of the 18 bp ARE insertion and deletion. (<b>D</b>) Chromatogram of POMC-A III 3′ UTR with an 18 bp homozygous deletion.</p>
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<p>Genotyping of three <span class="html-italic">POMCa</span> genotypes by sequencing and HRM detection in largemouth bass. (<b>A</b>) SNP Chromatogram of three <span class="html-italic">POMCa</span> genotypes. Four SNP loci are tightly linked and indicated by arrows. (1) The homozygote POMC−A I with 220TT/327GG/452CC/504TT; (2) The heterozygote POMC−A II with 220CT/327AG/452CT/504CT; (3) The homozygote POMC−A III with 220CC/327AA/452TT/504CC. (<b>B</b>) Genotyping of three <span class="html-italic">POMCa</span> genotypes by HRM detection in largemouth bass. (1) Normalized temperature-shifted melting curve. (2) Temperature-shifted difference curve. The blue curve corresponds to the genotype of POMC−A I, the red indicates POMC−A II, and the green profiles represent POMC−A III. The blank control is shown in black, with GG as the reference cluster.</p>
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<p>Frequency distribution of three <span class="html-italic">POMCa</span> genotypes in largemouth bass. (<b>A</b>,<b>B</b>) Percentages of three genotypes in non-selection population (<b>A</b>) and selection population (<b>B</b>), respectively. (<b>C</b>,<b>D</b>) The genetic structure of the top 10%, 20%, and 30% of the largest and smallest individuals in the non-selection population (<b>C</b>) and selection population (<b>D</b>), respectively.</p>
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<p>Transcriptional levels of LMB <span class="html-italic">POMCa</span> in response to fasting and refeeding. (<b>A</b>) <span class="html-italic">POMC</span> expression in mixed genotypes (<span class="html-italic">n</span> = 9) of largemouth bass fasted for three days (3 d), one week (7 d), and then refed for three days (10 d) and one week (14 d). <span class="html-italic">n</span> = 3 for each genotype. (<b>B</b>) Differential expression of three <span class="html-italic">POMCa</span> genotypes in the treatment group, which was fasted for one week and then refed for three days. Fold changes of three <span class="html-italic">POMCa</span> genotypes in different groups were calculated using the 2<sup>−ΔΔCT</sup> method. Data were expressed as mean ± SE (<span class="html-italic">n</span> = 6 for each genotype). <sup>a,b,c,d</sup>, and, <sup>e</sup> represent the significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Physiological responses to fasting and refeeding among three <span class="html-italic">POMCa</span> genotypes. (<b>A</b>) mean values of Cor, GH, IGF-1, and Glu. The blue line corresponds to the genotype of POMC-A I, the yellow indicates POMC-A II, and the green represents POMC-A III. Data were expressed as mean ± SE (n ≥ 9) for each genotype in each group. Thirteen POMC-A I, 9 POMC-A II, and 13 POMC-A III for the control group; 9 POMC-A I, 9 POMC-A II, and 9 POMC-A III for the fasting group; 18 POMC-A I, 9 POMC-A II, and 10 POMC-A III for the refeeding group. (<b>B</b>) Body weights (mean ± SE) of three <span class="html-italic">POMCa</span> genotypes before fasting and after refeeding. <sup>a, b,</sup> and <sup>c</sup> represent the significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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