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Functional Foods for Metabolism Regulation and Disease Improvement

A special issue of Nutrients (ISSN 2072-6643). This special issue belongs to the section "Phytochemicals and Human Health".

Deadline for manuscript submissions: closed (15 September 2023) | Viewed by 57856

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Guest Editor
1. School of Pharmacy, Jiangxi Medical College, Nanchang University, Nanchang, China
2. National Engineering Research Center for Bioengineering Drugs and the Technologies, Institution of Translational Medicine, Jiangxi Medical College, Nanchang University, Nanchang, China
Interests: probiotics; chronic disease; intestinal microbiota; functional foods
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Emerging evidence has indicated that functional foods are involved in regulating host health, as well as influencing the therapeutic effect of diseases, including allergy, obesity, inflammatory bowel disease, cancer, mental illness, and other diseases. Functional food interventions have been shown to have a significant potential on preventing and treating various diseases.

In this Special Issue, we welcome submissions including high-quality original research articles, clinical studies, and reviews that contribute innovative knowledge to understand functional foods and their potentials in diseases. Potential topics include but are not limited to the following:

1) The role of functional foods (e.g., probiotics, prebiotics, epigenetics) in the occurrence and development of diseases, e.g., pharmacology, pathology, genetics, neurosciences, infectious diseases;

2) Studies using single and/or combinations of metagenomics, metabonomics, and transcriptomics to reveal the interaction of functional foods and microbes in host health (e.g., animal model, human volunteers);

3) Function and mechanisms of the dietary, prebiotics, probiotics, and symbiotics for personalized nutrition in prevention and treatment of diseases;

4) Studies characterizing gut microbiota on disease development, host immunity, and medical treatment using molecular biology and multi-omics, to reveal deeper mechanisms between functional foods and host health.

Prof. Dr. Tingtao Chen
Guest Editor

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Keywords

  • prebiotics
  • probiotics
  • symbiotics
  • metagenomics
  • metabonomics
  • microbiota
  • disease

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Published Papers (13 papers)

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13 pages, 3692 KiB  
Article
Collagen Peptide Exerts an Anti-Obesity Effect by Influencing the Firmicutes/Bacteroidetes Ratio in the Gut
by Ga Hyeon Baek, Ki Myeong Yoo, Seon-Yeong Kim, Da Hee Lee, Hayoung Chung, Suk-Chae Jung, Sung-Kyun Park and Jun-Seob Kim
Nutrients 2023, 15(11), 2610; https://doi.org/10.3390/nu15112610 - 2 Jun 2023
Cited by 9 | Viewed by 3966
Abstract
Alterations in the intestinal microbial flora are known to cause various diseases, and many people routinely consume probiotics or prebiotics to balance intestinal microorganisms and the growth of beneficial bacteria. In this study, we selected a peptide from fish (tilapia) skin that induces [...] Read more.
Alterations in the intestinal microbial flora are known to cause various diseases, and many people routinely consume probiotics or prebiotics to balance intestinal microorganisms and the growth of beneficial bacteria. In this study, we selected a peptide from fish (tilapia) skin that induces significant changes in the intestinal microflora of mice and reduces the Firmicutes/Bacteroidetes ratio, which is linked to obesity. We attempted to verify the anti-obesity effect of selected fish collagen peptides in a high-fat-diet-based obese mouse model. As anticipated, the collagen peptide co-administered with a high-fat diet significantly inhibited the increase in the Firmicutes/Bacteroidetes ratio. It increased specific bacterial taxa, including Clostridium_sensu_stricto_1, Faecalibaculum, Bacteroides, and Streptococcus, known for their anti-obesity effects. Consequently, alterations in the gut microbiota resulted in the activation of metabolic pathways, such as polysaccharide degradation and essential amino acid synthesis, which are associated with obesity inhibition. In addition, collagen peptide also effectively reduced all obesity signs caused by a high-fat diet, such as abdominal fat accumulation, high blood glucose levels, and weight gain. Ingestion of collagen peptides derived from fish skin induced significant changes in the intestinal microflora and is a potential auxiliary therapeutic agent to suppress the onset of obesity. Full article
(This article belongs to the Special Issue Functional Foods for Metabolism Regulation and Disease Improvement)
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Figure 1

Figure 1
<p>Screening of peptides with a significant effect on the intestinal microflora. (<b>A</b>) The schematics of the animal study (C57BL/6) design. From days 0–9, the peptide solutions were administered to mice by oral gavage. On day 10, the mice were sacrificed, and the fecal matter was sampled. (<b>B</b>) The relative abundance of the gut microbiota at the phylum levels in six peptide-gavaged mice. The nine most abundant bacteria phyla were obtained from 30 mouse fecal samples from seven groups of mice. (CL; collagen, SA; soybean type A, SB; soybean type B, YA; yeast type A, YB; yeast type B, YC; yeast type C). (<b>C</b>) Firmicutes/Bacteroidetes (F/B) ratio. Unpaired <span class="html-italic">t</span>-tests (two-tailed) were used to analyze variations between the two groups. * <span class="html-italic">p</span> &lt; 0.05. (<b>D</b>) Beta diversity. The PCoA was based on the Jaccard distance, and the principal coordinate with the largest contribution rate was selected for graphical display. The two groups were completely separated. The statistical analyses were performed using AMOVA. (<b>E</b>–<b>H</b>) Alpha diversity. Sobs, Shannon, Invsimpson, and Chao indices reflect the differences in richness and evenness among samples.</p>
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<p>The anti-obesity effects of collagen peptide screened by gut microbiome analysis. (<b>A</b>) The schematics of the animal study (C57BL/6) design (High-fat diet model). We administered collagen peptide in the HFD mouse model by oral gavage for three weeks and monitored the body weight and food consumption. After three weeks, we sacrificed the mice and sampled the fecal matter. (<b>B</b>) Daily weight of the HFD mouse model in the presence or absence of collagen peptide. The HFD mice administered collagen peptides daily gained less weight than mice fed with HFD alone. Unpaired <span class="html-italic">t</span>-tests (two-tailed) were used to analyze variations between the two groups. ** <span class="html-italic">p</span> &lt; 0.01. (<b>C</b>) The relative abundance of the gut microbiota at the phylum level in RD, HFD + vehicle, and HFD + collagen mice. The 10 most abundant bacterial phyla were obtained from 17 mouse fecal samples from three groups of mice. (<b>D</b>) Firmicutes/Bacteroidetes (F/B) ratio. The one-way ANOVA test was used to analyze variation between the three groups. Different letters indicate statistical significance. (<b>E</b>) Beta diversity. PCoA was based on the Jaccard distance, and the principal coordinate with the largest contribution rate was selected for graphical display. The three groups were completely separated. Statistics analyses were performed using AMOVA. (<b>F</b>) Abdominal body fat comparison in the three groups. The decrease in abdominal body fat of HFD mouse model in the presence of collagen peptide was evident. (<b>G</b>–<b>J</b>) The results of the blood-biochemical analysis. Cholesterol or glucose levels were increased significantly in the mice fed with HFD for three weeks compared with the RD mice. Unpaired <span class="html-italic">t</span>-tests (two-tailed) were used to analyze variations between the two groups. * <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>The difference of OTU in two groups (HFD + vehicle and HFD + collagen). (<b>A</b>) Log fold changes. The log2 (vehicle/collagen) is the ratio of the relative abundance of OTUs between the two groups. As the size increases, it means that the relative abundance of the corresponding OTU is high. Fourteen distinguishing taxa (OTUs) were significantly marked among 50 differential OTUs between the two groups. (<b>B</b>) Differentially abundant bacterial taxa in fecal samples from mice models (HFD + vehicle and HFD + collagen). A bar plot showing the LDA score (effect size) of differentially abundant OTUs in the HFD + vehicle (blue, <span class="html-italic">n</span> = 6) and HFD + collagen (purple, <span class="html-italic">n</span> = 6) groups as determined using the Linear Discriminant Effect Size (LEfSe) analysis (α = 0.05, LDA score &gt; 2.0). (<b>C</b>) A box plot comparing the relative abundance of OTUs between the two groups. Unpaired <span class="html-italic">t</span>-tests (two-tailed) were used to analyze variation between the two groups. * <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, and **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>The predicted functional potential changes between HFD + vehicle mice and HFD + collagen mice by using PICRUST2. (<b>A</b>) The boxplots showed the relative abundance in 27 pathways related to obesity. There were 17 significantly increased pathways and 10 decreased pathways in HFD + collagen compared to HFD + vehicle. (<b>B</b>) The PCA analysis of intestinal bacterial metabolic pathway, the orange color indicates HFD + vehicle mice and the green color indicates HFD + collagen mice. (<b>C</b>) The extended error bar chart showed the significant difference in the predicted functional pathways related to obesity between the two groups. The middle value represents the mean differences between the two groups (upper-lower bar value), and the error bar represents the 95% confidence intervals with the effect size of difference in mean proportion. The <span class="html-italic">p</span>-value at the side indicates the significance between the upper and lower bars. (<b>D</b>) Heatmaps were drawn with normalized relative abundances in 27 obesity-related pathways in the two groups. The red colors represent a higher abundance and blue colors a lower abundance. * <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, and **** <span class="html-italic">p</span> &lt; 0.0001.</p>
Full article ">Figure 5
<p>The inhibitory effect of collagen peptide on HFD-derived endotoxin excretion in the gut. (<b>A</b>) Endotoxin levels in the sterilized fecal solution were measured by Limulus amebocyte lysate (LAL) assay. (<b>B</b>) Murine macrophage cell line, Raw264.7 cells, were incubated with a sterilized fecal solution for 24 h, and then pro-inflammatory cytokine levels in culture media were measured by ELISA. (<b>A</b>,<b>B</b>) Unpaired <span class="html-italic">t</span>-tests (two-tailed) were used to analyze the variation between the two groups. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001.</p>
Full article ">
17 pages, 5766 KiB  
Article
Alleviation Syndrome of High-Cholesterol-Diet-Induced Hypercholesterolemia in Mice by Intervention with Lactiplantibacillus plantarum WLPL21 via Regulation of Cholesterol Metabolism and Transportation as Well as Gut Microbiota
by Kui Zhao, Liang Qiu, Yao He, Xueying Tao, Zhihong Zhang and Hua Wei
Nutrients 2023, 15(11), 2600; https://doi.org/10.3390/nu15112600 - 1 Jun 2023
Cited by 7 | Viewed by 2034
Abstract
Probiotics are prospective for the prevention and treatment of cardiovascular diseases. Until now, systematic studies on the amelioration of hypercholesterolemia have been rare in terms of (cholesterol metabolism and transportation, reshaping of gut microbiota, as well as yielding SCFAs) intervention with lactic acid [...] Read more.
Probiotics are prospective for the prevention and treatment of cardiovascular diseases. Until now, systematic studies on the amelioration of hypercholesterolemia have been rare in terms of (cholesterol metabolism and transportation, reshaping of gut microbiota, as well as yielding SCFAs) intervention with lactic acid bacteria (LAB). In this study, strains of Lactiplantibacillus plantarum, WLPL21, WLPL72, and ZDY04, from fermented food and two combinations (Enterococcus faecium WEFA23 with L. plantarum WLPL21 and WLPL72) were compared for their effect on hypercholesterolemia. Comprehensively, with regard to the above aspects, L. plantarum WLPL21 showed the best mitigatory effect among all groups, which was revealed by decreasing total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) levels, upregulated cholesterol metabolism (Cyp27a1, Cyp7b1, Cyp7a1, and Cyp8b1) levels in the liver, cholesterol transportation (Abca1, Abcg5, and Abcg8) in the ileum or liver, and downregulated Npc1l1. Moreover, it reshaped the constitution of gut microbiota; specifically, the ratio of Firmicutes to Bacteroidetes (F/B) was downregulated; the relative abundance of Allobaculum, Blautia, and Lactobacillus was upregulated by 7.48–14.82-fold; and that of Lachnoclostridium and Desulfovibrio was then downregulated by 69.95% and 60.66%, respectively. In conclusion, L. plantarum WLPL21 improved cholesterol metabolism and transportation, as well as the abundance of gut microbiota, for alleviating high-cholesterol-diet-induced hypercholesterolemia. Full article
(This article belongs to the Special Issue Functional Foods for Metabolism Regulation and Disease Improvement)
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Figure 1

Figure 1
<p>Diagram of animal experiment scheme.</p>
Full article ">Figure 2
<p>Effect of probiotics on the decrease in lipids level in serum (<b>A</b>) and liver (<b>B</b>) of mice. Total cholesterol (TC), triglycerides (TG), high-density-lipoprotein cholesterol (HDL-C), low-density-lipoprotein cholesterol (LDL-C). ND: normal diet; HCD: high-cholesterol diet; ZDY04: HCD with <span class="html-italic">Lactiplantibacillus plantarum</span> ZDY04; WLPL21: <span class="html-italic">L. plantarum</span> WLPL21; WLPL72: <span class="html-italic">L. plantarum</span> WLPL72; W21E23: HCD with bacterial cocktail of <span class="html-italic">L. plantarum</span> WLPL21 and <span class="html-italic">Enterococcus faecium</span> WEFA23; W72E23: HCD with bacterial cocktail of <span class="html-italic">L. plantarum</span> WLPL21 and <span class="html-italic">E. faecium</span> WEFA23. Data were expressed as mean ± SD. ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, data in HCD were compared to ND; <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01, <sup>###</sup> <span class="html-italic">p</span> &lt; 0.001, data in probiotic groups were compared to HCD.</p>
Full article ">Figure 3
<p>Effect of probiotics on hepatic injury caused by HCD. Liver index (<b>A</b>), serum aminotransferase of alanine (ALT) and aspartate (AST) (<b>B</b>), H &amp; E staining of the liver (×200) (<b>C</b>) (a, inflammatory cell infiltration; b, lipid droplets accumulation), and hepatic steatosis scores of hypercholesterolemic mice livers (n = 5 per group) (<b>D</b>). ND: normal diet; HCD: high-cholesterol diet; ZDY04: HCD with <span class="html-italic">L. plantarum</span> ZDY04; WLPL21: <span class="html-italic">L. plantarum</span> WLPL21; WLPL72: <span class="html-italic">L. plantarum</span> WLPL72; W21E23: HCD with bacterial cocktail of <span class="html-italic">L. plantarum</span> WLPL21 and <span class="html-italic">E. faecium</span> WEFA23; W72E23: HCD with bacterial cocktail of <span class="html-italic">L. plantarum</span> WLPL21 and <span class="html-italic">E. faecium</span> WEFA23. Data were expressed as mean ± SD. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, **** <span class="html-italic">p</span> &lt; 0.0001, data in HCD were compared to ND; <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01, <sup>###</sup> <span class="html-italic">p</span> &lt; 0.001, data in probiotic groups were compared to HCD.</p>
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<p>Effect of probiotics on expression of gene related to cholesterol metabolism (<b>A</b>), transport (<b>B</b>), and transcription regulatory factor (<b>C</b>) in the liver, and cholesterol transport in ileum (<b>D</b>). ND: normal diet; HCD: high-cholesterol diet; ZDY04: HCD with <span class="html-italic">L. plantarum</span> ZDY04; WLPL21: <span class="html-italic">L. plantarum</span> WLPL21; WLPL72: <span class="html-italic">L. plantarum</span> WLPL72; W21E23: HCD with bacterial cocktail of <span class="html-italic">L. plantarum</span> WLPL21 and <span class="html-italic">E. faecium</span> WEFA23; W72E23: HCD with bacterial cocktail of <span class="html-italic">L. plantarum</span> WLPL21 and <span class="html-italic">E. faecium</span> WEFA23. Data were expressed as mean ± SD. <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01, <sup>###</sup> <span class="html-italic">p</span> &lt; 0.001, <sup>####</sup> <span class="html-italic">p</span> &lt; 0.0001; data in probiotic groups were compared to HCD.</p>
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<p>Effect of probiotics on level of acetate (<b>A</b>), propionate (<b>B</b>), and butyrate (<b>C</b>) in cecal content. ND: normal diet; HCD: high-cholesterol diet; ZDY04: HCD with <span class="html-italic">L. plantarum</span> ZDY04; WLPL21: <span class="html-italic">L. plantarum</span> WLPL21; WLPL72: <span class="html-italic">L. plantarum</span> WLPL72; W21E23: HCD with bacterial cocktail of <span class="html-italic">L. plantarum</span> WLPL21 and <span class="html-italic">E. faecium</span> WEFA23; W72E23: HCD with bacterial cocktail of <span class="html-italic">L. plantarum</span> WLPL21 and <span class="html-italic">E. faecium</span> WEFA23. Data were expressed as mean ± SD. ** <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, data in HCD were compared to ND; <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01, <sup>###</sup> <span class="html-italic">p</span> &lt; 0.001, <sup>####</sup> <span class="html-italic">p</span> &lt; 0.0001, data in probiotic groups were compared to HCD.</p>
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<p>Effect of probiotics on species diversity abundance. Unique flora (<b>A</b>), principal component analysis (PCA) (<b>B</b>), non-metric multidimensional scaling (NMDS) (<b>C</b>), and Shannon (<b>D</b>), ACE (<b>E</b>), and Chao1 indexes (<b>F</b>) are shown. ND: normal diet; HCD: high-cholesterol diet; ZDY04: HCD with <span class="html-italic">L. plantarum</span> ZDY04; WLPL21: <span class="html-italic">L. plantarum</span> WLPL21; WLPL72: <span class="html-italic">L. plantarum</span> WLPL72; W21E23: HCD with bacterial cocktail of <span class="html-italic">L. plantarum</span> WLPL21 and <span class="html-italic">E. faecium</span> WEFA23; W72E23: HCD with bacterial cocktail of <span class="html-italic">L. plantarum</span> WLPL21 and <span class="html-italic">E. faecium</span> WEFA23. Data were expressed as mean ± SD. **** <span class="html-italic">p</span> &lt; 0.0001, data in HCD were compared to ND; <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01, <sup>###</sup> <span class="html-italic">p</span> &lt; 0.001, data in probiotic groups were compared to HCD.</p>
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<p>Effect of probiotics on gut microbial community composition in level of phylum (<b>A</b>) and genus (<b>B</b>), and type of high relative abundance of differential bacteria in level of phylum (<b>C</b>) and genus (<b>D</b>). ND: normal diet; HCD: high-cholesterol diet; ZDY04: HCD with <span class="html-italic">L. plantarum</span> ZDY04; WLPL21: <span class="html-italic">L. plantarum</span> WLPL21; WLPL72: <span class="html-italic">L. plantarum</span> WLPL72; W21E23: HCD with bacterial cocktail of <span class="html-italic">L. plantarum</span> WLPL21 and <span class="html-italic">E. faecium</span> WEFA23; W72E23: HCD with bacterial cocktail of <span class="html-italic">L. plantarum</span> WLPL21 and <span class="html-italic">E. faecium</span> WEFA23. Data were expressed as mean ± SD. * <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, data in HCD were compared to ND, <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01, <sup>###</sup> <span class="html-italic">p</span> &lt; 0.001, data in probiotic groups were compared to HCD.</p>
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<p>Correlation heatmap between bacteria at genus level and major metabolite including serum and liver lipids, SCFAs, and fecal TBA. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001.</p>
Full article ">Figure 9
<p>Identification of most characteristic taxa by linear discriminant analysis (LDA) effect size (LEfSe). The most significant difference of gut microbial taxa among groups after LDA (<b>A</b>). Cladogram visualizing the output of the LEfSe analysis (<b>B</b>). The threshold on the logarithmic LDA score for discriminative features was set to 3.0. The bar length of LDA represents the influence of species abundance on the difference effect. p, phylum; c, class; o, order; f, family; and g, genus. ND: normal diet; HCD: high-cholesterol diet; ZDY04: HCD with <span class="html-italic">L. plantarum</span> ZDY04; WLPL21: <span class="html-italic">L. plantarum</span> WLPL21; WLPL72: <span class="html-italic">L. plantarum</span> WLPL72; W21E23: HCD with bacterial cocktail of <span class="html-italic">L. plantarum</span> WLPL21 and <span class="html-italic">E. faecium</span> WEFA23; W72E23: HCD with bacterial cocktail of <span class="html-italic">L. plantarum</span> WLPL21 and <span class="html-italic">E. faecium</span> WEFA23.</p>
Full article ">
19 pages, 5439 KiB  
Article
Gas-Mediated Intestinal Microbiome Regulation Prompts the Methanol Extract of Schizonepetae Spica to Relieve Colitis
by Xuewei Ye, Yingxin Cen, Kefei Wu, Langyu Xu, Jiahui Ni, Wenxin Zheng and Wei Liu
Nutrients 2023, 15(3), 519; https://doi.org/10.3390/nu15030519 - 19 Jan 2023
Cited by 5 | Viewed by 2114
Abstract
Intestinal dysbiosis plays an important role in the pathogenesis of colitis (UC). Schizonepetae Herba can achieve anti-inflammatory effects as a medicine and food homologous vegetable. Luteolin, eriodictyol, fisetin, and kaempferol are the main anti-inflammatory active compounds obtained through mass spectrometry from the methanol [...] Read more.
Intestinal dysbiosis plays an important role in the pathogenesis of colitis (UC). Schizonepetae Herba can achieve anti-inflammatory effects as a medicine and food homologous vegetable. Luteolin, eriodictyol, fisetin, and kaempferol are the main anti-inflammatory active compounds obtained through mass spectrometry from the methanol extract of Schizonepetae Spica (JJSM). JJSM intervention resulted in attenuated weight loss, high disease-activity-index score, colon length shortening and colonic pathological damage in DSS-induced colitis mice. Interestingly, hydrogen sulfide (H2S) was inhibited remarkably, which is helpful to elucidate the relationship between active substance and intestinal flora. Furthermore, JJSM administration improved intestinal flora with down-regulating the abundance of harmful bacteria such as Clostridiales and Desulfovibrio and up-regulating the abundance of beneficial bacteria such as Muribaculaceae and Ligolactobacillus and enhanced the production of SCFAs. It is worth noticing that Desulfovibrio is related to the production of intestinal gas H2S. The elevated levels of Desulfovibrio and H2S will hasten the onset of colitis, which is a crucial risk factor for colitis. The results displayed that JJSM could considerably ameliorate colitis by rebuilding H2S-related intestinal flora, which provides a new therapeutic strategy for Schizonepetae Spica to be utilized as a functional food and considered as an emerging candidate for intestinal inflammation. Full article
(This article belongs to the Special Issue Functional Foods for Metabolism Regulation and Disease Improvement)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>JJSM ameliorated the clinical symptoms of DSS-induced colitis in mice. (<b>A</b>) Macroscopic view of colon length; (<b>B</b>) Colon length; (<b>C</b>) Changes in body weight; (<b>D</b>) DAI scores. Effect of JJSM on histopathological changes. (<b>E</b>) Hematoxylin and Eosin (H &amp; E)-stained representative colonic tissues; (<b>F</b>) Histological score. Data are presented as the mean ± S.D. <sup>###</sup> <span class="html-italic">p</span> &lt; 0.001 vs. Control group; * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 vs. Model group, *** <span class="html-italic">p</span> &lt; 0.001 vs. Model group.</p>
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<p>Gas production. (<b>A</b>) The pressure that remains after fermentation. <span class="html-italic">Schizonepetae Spica</span> results in increased pressure after incubation; (<b>B</b>) NH<sub>3</sub>; (<b>C</b>) H<sub>2</sub>S; (<b>D</b>) H<sub>2</sub>; (<b>E</b>) CO<sub>2</sub>; (<b>F</b>) CH<sub>4</sub>. The x-axis displays the sample groups, the y-axis displays gas amount, the gas abundance value is the mean of three biological replicates, and the error bars reflect the 95% confidence intervals. Short-chain fatty acid (SCFA) production. (<b>G</b>) Acetic acid; (<b>H</b>) Propionic acid; (<b>I</b>) Butyric acid; (<b>J</b>) Isobutyric acid; (<b>K</b>) Valeric acid; (<b>L</b>) Isovaleric acid. Data are presented as the mean ± S.D. <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05 vs. control, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 vs. control, <sup>###</sup> <span class="html-italic">p</span> &lt; 0.001 vs. control, * <span class="html-italic">p</span> &lt; 0.05 vs. model, *** <span class="html-italic">p</span> &lt; 0.001 vs. model. M: molecular weight of gas.</p>
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<p>Analysis of microbial Alpha diversity. (<b>A</b>) Goods_coverage; (<b>B</b>) Chao 1 index and (<b>C</b>) Shannon index. Beta diversity: (<b>D</b>) Weighted NMDS analysis and (<b>E</b>) Weighted cluster tree analysis. Microbial community structure of each group: (<b>F</b>) Distribution of microbiome at phylum; (<b>G</b>) Distribution of microbiome at genus; (<b>H</b>) Relative abundance of <span class="html-italic">Muribaculaceae_unclassified</span>; (<b>I</b>) Relative abundance of <span class="html-italic">Ligilactobacillus</span>; (<b>J</b>) Relative abundance of <span class="html-italic">Ruminococcus</span>; (<b>K</b>) Relative abundance of <span class="html-italic">Clostridiales_unclassified</span>; (<b>L</b>) Relative abundance of <span class="html-italic">Lachnospiraceae_NK4A136_group</span>; (<b>M</b>) Relative abundance of <span class="html-italic">Desulfovibrio</span>. Data are presented as the mean ± S.D. <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 vs. control, * <span class="html-italic">p</span> &lt; 0.05 vs. model, ** <span class="html-italic">p</span> &lt; 0.01 vs. model, *** <span class="html-italic">p</span> &lt; 0.001 vs. model.</p>
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<p>Circos diagram of species-sample relationships (top 5 bacteria). (<b>A</b>) At family level; (<b>B</b>) At genus level. LDA Effect Size (LEfSe): (<b>C</b>) Histogram of linear discriminant analysis (LDA) value distribution. LDA score ≥ 3.5. The length of the bar chart represents the size of the impact of significantly different species. (<b>D</b>) Cladogram. Each node’s size reflects the species’ relative abundance. (phylum = p; class = c; order = o; family = f; genus = g; species = s). (<b>E</b>) Microbiota and index correlation clustering heat map. Rows indicate species, and columns indicate gas and SCFAs relevant to this study. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01. Red indicates a positive correlation, blue indicates a negative correlation. The darker the color, the stronger the correlation.</p>
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<p>Function prediction. (<b>A</b>) Level 3 KEGG pathway alterations. KOentries with dominant abundance and significant differences in the pathway. (<b>B</b>) Beta-Lactam resistance; (<b>C</b>) Bacterial chemotaxis; (<b>D</b>) Flagellar assembly; (<b>E</b>) Bacterial motility proteins; (<b>F</b>) Two-component system. <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05 vs. control, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 vs. control, <sup>###</sup> <span class="html-italic">p</span> &lt; 0.001 vs. control, * <span class="html-italic">p</span> &lt; 0.05 vs. model, ** <span class="html-italic">p</span> &lt; 0.01 vs. model, *** <span class="html-italic">p</span> &lt; 0.001 vs. model.</p>
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<p>Schematic diagram of JJSM relieving colitis.</p>
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15 pages, 3667 KiB  
Article
Postoperative Probiotics Administration Attenuates Gastrointestinal Complications and Gut Microbiota Dysbiosis Caused by Chemotherapy in Colorectal Cancer Patients
by Feng Huang, Shengjie Li, Wenjie Chen, Yiyang Han, Yue Yao, Liang Yang, Qiang Li, Qun Xiao, Jing Wei, Zhaoxia Liu, Tingtao Chen and Xiaorong Deng
Nutrients 2023, 15(2), 356; https://doi.org/10.3390/nu15020356 - 11 Jan 2023
Cited by 44 | Viewed by 6945
Abstract
The current study aims to evaluate the potential roles of taking probiotics postoperatively in attenuating the gastrointestinal complications and disturbed gut microbiota in colorectal cancer (CRC) patients undergoing chemotherapy. One hundred eligible CRC patients who were treated with radical surgery and needed to [...] Read more.
The current study aims to evaluate the potential roles of taking probiotics postoperatively in attenuating the gastrointestinal complications and disturbed gut microbiota in colorectal cancer (CRC) patients undergoing chemotherapy. One hundred eligible CRC patients who were treated with radical surgery and needed to receive chemotherapy were recruited. Half of them were randomly assigned to the Probio group to take a probiotic combination from post-operation to the end of the first chemotherapeutic course. The other half of patients taking placebo instead were classified as the Placebo group. Gastrointestinal complications such as nausea, acid reflux, abdominal pain, abdominal distention, constipation, and diarrhea were recorded during chemotherapy. Fecal samples were collected preoperatively and after the first cycle of postoperative chemotherapy for 16S rRNA high-throughput sequencing and short-chain fatty acids (SCFAs) analysis. Results showed that probiotics administration could effectively reduce chemotherapy-induced gastrointestinal complications, particularly in diarrhea (p < 0.01). Additionally, chemotherapy also reduced the bacterial diversity indexes of the gut microbiota in CRC patients, which could be significantly increased by taking probiotics. Moreover, this chemotherapy caused significant changes in the composition of the gut microbiota, as indicated by decreased phylum levels of Firmicutes and increased Bacteroidetes, Proteobacteria, and Verrucomicrobia. In particular, several bacterial genera such as Akkermansia and Clostridium were significantly increased, while Prevotella, Lactobacillus, and Roseburia were decreased (p < 0.05). However, probiotic administration could effectively restore these taxa changes both at the phylum and genus levels, and mildly increase the genus levels of Bifidobacterium, Streptococcus, and Blautia. Furthermore, probiotics could also promote the production of SCFAs, particularly increasing acetate, butyrate, and propionate (p < 0.0001). These results support the beneficial effects of the probiotic interventions as novel alternative or complementary strategies in chemoprevention. Full article
(This article belongs to the Special Issue Functional Foods for Metabolism Regulation and Disease Improvement)
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<p>Flow diagram showing the schedule of the study.</p>
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<p>Probiotics combination restored the changed gut bacterial diversity in CRC patients receiving chemotherapy. (<b>a</b>) Chao1, Simpson, and Pielou_e (PE) indexes of gut microbial α diversity in the fecal samples among the three groups. (<b>b</b>,<b>c</b>) The PCoA analysis of gut microbial β diversity based on Jaccard and Unweighted_unifrac distances, respectively. (<b>d</b>) Venn diagram of the identified bacterial species among CRC patients. CRC, the standard reference group before anticancer treatment. Placebo, the control group taking placebo. Probio, the treatment group taking probiotic combination. Multiple comparison analysis based on Kruskal−Wallis test following Dunn’s test, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Probiotics combination remodeled the different gut microbial taxa among CRC patients. (<b>a</b>) Stacking map of taxa distribution at the phylum level. (<b>b</b>,<b>c</b>) The relative abundance of Firmicutes and Verrucomicrobia at the phylum level. (<b>d</b>) Stacking map of species distribution at the genus level. (<b>e</b>,<b>f</b>) The relative abundance of <span class="html-italic">Akkermansia</span> (<b>e</b>), <span class="html-italic">Lachnospiraceae_Clostridium</span> (<b>f</b>), <span class="html-italic">Prevotella</span> (<b>g</b>), <span class="html-italic">Lactobacillus</span> (<b>h</b>), <span class="html-italic">Roseburia</span> (<b>i</b>), and <span class="html-italic">Blautia</span> (<b>j</b>). CRC, the standard reference group before anticancer treatment. Placebo, the control group taking placebo. Probio, the treatment group taking probiotics combination. One−way ANOVA Multiple comparision was based on Kruskal−Wallis test following Dunn’s test. <span class="html-italic">p</span> value &lt; 0.05 means statistical significance.</p>
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<p>LEfSe cladogram showing differently abundant gut microbiota taxa among CRC patients at different levels. The current LDA threshold score is over 2; <span class="html-italic">p</span>, phylum; <span class="html-italic">c</span>, class; <span class="html-italic">o</span>, order; <span class="html-italic">f</span>, family; <span class="html-italic">g</span>, genus. The blue, red, and green color refers to different bacterial taxa in CRC group, Placebo, and Probio group, respectively.</p>
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<p>Probiotics combination promoted the production of SCFAs in the gut of CRC patients undergoing chemotherapy. CRC, the standard reference group before anticancer treatment. Placebo, the control group taking placebo. Probio, the treatment group taking probiotics combination. Multiple comparisons based on two-way ANOVA analysis following Tukey’s test, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, **** <span class="html-italic">p</span> &lt; 0.0001; ns refers to no significant difference detected. The capped line refers to comparison among the three groups; the half tick-down line refers to comparison between the two groups.</p>
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<p>Schematic diagram of actions of probiotics combination in attenuating chemotherapy-induced gastrointestinal responses and gut microbiota dysbiosis.</p>
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13 pages, 2451 KiB  
Article
In Vitro Fermentation of Beechwood Lignin–Carbohydrate Complexes Provides Evidence for Utilization by Gut Bacteria
by Xiaochen Ma, Shujun Liu, Hongliang Wang, Yulu Wang, Zhen Li, Tianyi Gu, Yulong Li, Fengjiao Xin and Boting Wen
Nutrients 2023, 15(1), 220; https://doi.org/10.3390/nu15010220 - 1 Jan 2023
Cited by 5 | Viewed by 2368
Abstract
Lignin–carbohydrate complexes (LCCs) are emerging as a new and natural product with pharmacological and nutraceutical potential. It is uncertain, however, whether LCCs have a positive effect on the microbiota of the gut based on the current evidence. Here, the LCC extracted from beechwood [...] Read more.
Lignin–carbohydrate complexes (LCCs) are emerging as a new and natural product with pharmacological and nutraceutical potential. It is uncertain, however, whether LCCs have a positive effect on the microbiota of the gut based on the current evidence. Here, the LCC extracted from beechwood (BW-LCC) was used as a substrate for in vitro fermentation. The lignin in BW-LCC consisted of guaiacyl (G) and syringyl (S) units, which are mainly linked by β-O-4 bonds. After 24 h of in vitro fermentation, the pH had evidently declined. The concentrations of acetic acid and propionic acid, the two main short-chain fatty acids (SCFAs), were significantly higher than in the control group (CK). In addition, BW-LCC altered the microbial diversity and composition of gut microbes, including a reduction in the relative abundance of Firmicutes and an increase in the relative abundance of Proteobacteria and Bacteroidetes. The relative abundance of Escherichia coli-Shigella and Bacteroides were the most variable at the genus level. The genes of carbohydrate-active enzymes (CAZymes) also changed significantly with the fermentation and were related to the changes in microbes. Notably, the auxiliary actives (AAs), especially AA1, AA2, and AA3_2, play important roles in lignin degradation and were significantly enriched and concentrated in Proteobacteria. From this study, we are able to provide new perspectives on how gut microbes utilize LCC. Full article
(This article belongs to the Special Issue Functional Foods for Metabolism Regulation and Disease Improvement)
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<p>2D HSQC NMR spectrum of BW-LCC.</p>
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<p>Changes of pH (<b>A</b>) and SCFA concentration compare with CK, including total acid (<b>B</b>), acetic acid (<b>C</b>) and propionic acid (<b>D</b>). *, <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>Diversity index of the intestinal microbial community after fermentation. (<b>A</b>) Venn diagram analysis at the OTU level. (<b>B</b>) The Shannon diversity index of microbial communities under each treatment at 24 h. Significance was determined between BW-LCC and CK using Wilcoxon rank-sum test; **, <span class="html-italic">p</span> &lt; 0.01. (<b>C</b>) Principal Component Analysis (PCoA) based on diversity at the OTU level between the CK and BW-LCC groups.</p>
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<p>Comparison of microbial community composition between the BW-LCC and CK groups at the phylum (<b>A</b>) and genus (<b>B</b>) levels.</p>
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<p>Comparison of CAZymes between the BW-LCC and CK groups at the class (<b>A</b>) and genus (<b>B</b>) levels. Wilcoxon rank-sum tests indicate significant differences at the family level (*, <span class="html-italic">p</span> &lt; 0.05) (<b>C</b>). Species contribution of CAZyme families that were significantly enhanced (<b>D</b>).</p>
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<p>Relative abundance of AAs in gut microbes: (<b>A</b>) AAs with significant changes after in vitro fermentation, *, <span class="html-italic">p</span> &lt; 0.05; (<b>B</b>) species contribution of AAs in gut microbes.</p>
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19 pages, 5661 KiB  
Article
Dietary Methionine Restriction Alleviates Choline-Induced Tri-Methylamine-N-Oxide (TMAO) Elevation by Manipulating Gut Microbiota in Mice
by Manman Lu, Yuhui Yang, Yuncong Xu, Xiaoyue Wang, Bo Li, Guowei Le and Yanli Xie
Nutrients 2023, 15(1), 206; https://doi.org/10.3390/nu15010206 - 1 Jan 2023
Cited by 4 | Viewed by 3279
Abstract
Dietary methionine restriction (MR) has been shown to decrease plasma trimethylamine-N-oxide (TMAO) levels in high-fat diet mice; however, the specific mechanism used is unknown. We speculated that the underlying mechanism is related with the gut microbiota, and this study aimed to confirm the [...] Read more.
Dietary methionine restriction (MR) has been shown to decrease plasma trimethylamine-N-oxide (TMAO) levels in high-fat diet mice; however, the specific mechanism used is unknown. We speculated that the underlying mechanism is related with the gut microbiota, and this study aimed to confirm the hypothesis. In this study, we initially carried out an in vitro fermentation experiment and found that MR could reduce the ability of gut microbiota found in the contents of healthy mice and the feces of healthy humans to produce trimethylamine (TMA). Subsequently, mice were fed a normal diet (CON, 0.20% choline + 0.86% methionine), high-choline diet (H-CHO, 1.20% choline + 0.86% methionine), or high-choline + methionine-restricted diet (H-CHO+MR, 1.20% choline + 0.17% methionine) for 3 months. Our results revealed that MR decreased plasma TMA and TMAO levels in H-CHO-diet-fed mice without changing hepatic FMO3 gene expression and enzyme activity, significantly decreased TMA levels and expression of choline TMA-lyase (CutC) and its activator CutD, and decreased CutC activity in the intestine. Moreover, MR significantly decreased the abundance of TMA-producing bacteria, including Escherichia-Shigella (Proteobacteria phylum) and Anaerococcus (Firmicutes phylum), and significantly increased the abundance of short-chain fatty acid (SCFA)-producing bacteria and SCFA levels. Furthermore, both MR and sodium butyrate supplementation significantly inhibited bacterial growth, down-regulated CutC gene expression levels in TMA-producing bacteria, including Escherichia fergusonii ATCC 35469 and Anaerococcus hydrogenalis DSM 7454 and decreased TMA production from bacterial growth under in vitro anaerobic fermentation conditions. In conclusion, dietary MR alleviates choline-induced TMAO elevation by manipulating gut microbiota in mice and may be a promising approach to reducing circulating TMAO levels and TMAO-induced atherosclerosis. Full article
(This article belongs to the Special Issue Functional Foods for Metabolism Regulation and Disease Improvement)
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<p>Effects of different levels of MR on TMA production by bacteria from mouse colonic contents (<b>A</b>) and healthy human feces (<b>B</b>) cultured anaerobically in fermentation medium, and on expression levels of <span class="html-italic">CutC</span> (<b>C</b>,<b>E</b>), <span class="html-italic">CutD</span> (<b>D</b>,<b>F</b>), <span class="html-italic">CntA</span> (<b>G</b>,<b>I</b>), <span class="html-italic">CntB</span> (<b>H</b>,<b>J</b>), <span class="html-italic">YeaW</span> (<b>K</b>,<b>M</b>), and <span class="html-italic">YeaX</span> (<b>L</b>,<b>N</b>) in bacteria from mouse colonic contents and healthy human feces cultured anaerobically in fermentation medium. TMA, trimethylamine; MR, methionine restriction; CON, 0.86% methionine; MR-20%, 0.69% methionine; MR-40%, 0.52% methionine; MR-60%, 0.34% methionine; MR-80%, 0.17% methionine. <sup>&amp;</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>&amp;&amp;</sup> <span class="html-italic">p</span> &lt; 0.01, compared with the CON group.</p>
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<p>Effects of MR on body weight (<b>A</b>), body weight gain (<b>B</b>), plasma TMA (<b>C</b>) and TMAO levels (<b>D</b>), hepatic FMO3 gene expression levels (<b>E</b>) and activity (<b>F</b>), and atherosclerosis index (<b>G</b>) in mice under H-CHO diet conditions. MR, methionine restriction; CON, normal diet group; H-CHO, high-choline diet group; H-CHO+MR, high-choline + methionine restricted diet group. <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 (H-CHO vs. CON); * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 (H-CHO+MR vs. H-CHO).</p>
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<p>α-diversity analysis and β-diversity analysis of microbiota in mouse feces. (<b>A</b>) OTUs Venn analysis; (<b>B</b>) Rarefaction curve; (<b>C</b>) OTU Rank; (<b>D</b>) Ace index; (<b>E</b>) Chao1 index; (<b>F</b>) Shannon index; (<b>G</b>) Simpson index; (<b>H</b>) Anosim (H-CHO vs. CON), the closer the R value is to 1, the greater the difference between groups than the difference in the group, <span class="html-italic">p</span> &lt; 0.05 indicates that the difference is statistically significant; (<b>I</b>) Anosim (H-CHO vs. H-CHO+MR); (<b>J</b>) Anosim (H-CHO+MR vs. CON); (<b>K</b>) PCA score plots; (<b>L</b>) unweighted unifrac cluster tree. Anosim, analysis of similarities; PCA, principal component analysis; MR, methionine restriction; OTU, operational taxonomic units; CON, normal diet group; H-CHO, high-choline diet group; H-CHO+MR, high-choline + methionine restricted diet group. <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05 (H-CHO vs. CON); * <span class="html-italic">p</span> &lt; 0.05 (H-CHO+MR vs. H-CHO).</p>
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<p>MR altered the gut microbiota composition in feces of mice fed with the H-CHO diet. (<b>A</b>) relative abundance of the major bacterial phyla in mouse feces; (<b>B</b>) heat map of the microbiota in mouse feces at the genus level (relative abundance &gt; 0.2%); (<b>C</b>) comparison of relative abundance at the genus levels among the CON, H-CHO, and H-CHO+MR groups. MR, methionine restriction; CON, normal diet group; H-CHO, high-choline diet group; H-CHO+MR, high-choline + methionine restricted diet group. <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 (H-CHO vs. CON); * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 (H-CHO+MR vs. H-CHO).</p>
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<p>Effects of MR on TMA levels (<b>A</b>,<b>E</b>), <span class="html-italic">CutC</span> (<b>B</b>,<b>F</b>) and <span class="html-italic">CutD</span> (<b>C</b>,<b>G</b>) expression, and CutC activity (<b>D</b>,<b>H</b>) in mouse cecal contents and colonic contents under H-CHO diet conditions. MR, methionine restriction; TMA, trimethylamine; CON, normal diet group; H-CHO, high-choline diet group; H-CHO+MR, high-choline + methionine restricted diet group; CON, 0.86% methionine; MR-80%, 0.17% methionine. <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 (H-CHO vs. CON); ** <span class="html-italic">p</span> &lt; 0.01 (H-CHO+MR vs. H-CHO).</p>
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<p>Effects of MR on TMA production (<b>A</b>,<b>E</b>), bacteria abundance (<b>B</b>,<b>F</b>), and CutC expression (<b>C</b>,<b>G</b>) and activity (<b>D</b>,<b>H</b>) in <span class="html-italic">Escherichia fergusonii ATCC 35,469</span> and <span class="html-italic">Anaerococcus hydrogenalis DSM 7454</span> cultured anaerobically in medium. MR, methionine restriction; TMA, trimethylamine; CutC, choline trimethylamine-lyase; CON, normal diet group; MR-80%, 0.17% methionine. <sup>&amp;&amp;</sup> <span class="html-italic">p</span> &lt; 0.01 (MR-80% vs. CON).</p>
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<p>Effects of MR on mRNA expression levels of <span class="html-italic">IL-10</span> (<b>A</b>,<b>G</b>), <span class="html-italic">IL-6</span> (<b>B</b>,<b>H</b>), <span class="html-italic">TNF-α</span> (<b>C</b>,<b>I</b>), and <span class="html-italic">IL-1β</span> (<b>D</b>,<b>J</b>) in the aorta tissue and colon tissue, respectively, and on the morphological structure of the colon ((<b>E</b>), 400× magnification), as well as mRNA expression of <span class="html-italic">Occludin</span>, <span class="html-italic">ZO-1</span>, <span class="html-italic">Claudin-3</span> (<b>F</b>) in the colon of H-CHO mice. MR, methionine restriction; CON, normal diet group; H-CHO, high-choline diet group; H-CHO+MR, high-choline + methionine restricted diet group. <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 (H-CHO vs. CON); * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 (H-CHO+MR vs. H-CHO).</p>
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<p>Effects of MR on acetate (<b>A</b>), propionate (<b>B</b>), butyrate (<b>C</b>), and total acid (<b>D</b>) levels in the cecal contents of mice fed with the H-CHO diet, and the effects of sodium butyrate supplementation on TMA production by bacteria from human feces (<b>E</b>) cultured anaerobically in medium, <span class="html-italic">CutC</span>, <span class="html-italic">CntA</span>, and <span class="html-italic">YeaW</span> expression in healthy human fecal fermentation broth (<b>F</b>–<b>H</b>), TMA production from <span class="html-italic">Escherichia fergusonii ATCC 35469</span> and <span class="html-italic">Anaerococcus hydrogenalis DSM 7454</span> (<b>I</b>,<b>K</b>), and bacteria abundance of <span class="html-italic">Escherichia fergusonii ATCC 35469</span> and <span class="html-italic">Anaerococcus hydrogenalis DSM 7454</span> (<b>J</b>,<b>L</b>). MR, methionine restriction; TMA, trimethylamine; CutC, CntA, and YeaW, trimethylamine-lyase; NaBut, sodium butyrate; CON, normal diet group; H-CHO, high-choline diet group; H-CHO+MR, high-choline + methionine restricted diet group. <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 (H-CHO vs. CON); * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 (H-CHO+MR vs. H-CHO); <sup>&amp;&amp;</sup> <span class="html-italic">p</span> &lt; 0.01 (NaBut vs. CON).</p>
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14 pages, 2690 KiB  
Article
Protective Effects of Piperine on Ethanol-Induced Gastric Mucosa Injury by Oxidative Stress Inhibition
by Zhouwei Duan, Shasha Yu, Shiping Wang, Hao Deng, Lijun Guo, Hong Yang and Hui Xie
Nutrients 2022, 14(22), 4744; https://doi.org/10.3390/nu14224744 - 10 Nov 2022
Cited by 21 | Viewed by 2895
Abstract
Piper nigrum Linnaeus is often used as a treatment for chills, stomach diseases, and other ailments. Piperine has many biological functions; however, its mechanism for preventing gastric mucosal damage is still unclear. The objective of this study was to investigate the preventive effects [...] Read more.
Piper nigrum Linnaeus is often used as a treatment for chills, stomach diseases, and other ailments. Piperine has many biological functions; however, its mechanism for preventing gastric mucosal damage is still unclear. The objective of this study was to investigate the preventive effects of piperine on ethanol-induced gastric mucosal injury by using GES-1 cells and rats. SOD, CAT, GSH-Px and MDA were effectively regulated in GES-1 cells pre-treated with piperine. Piperine significantly increased SOD, CAT and GSH-Px activities, but decreased the ulcer area, MDA, ROS and MPO levels in the gastric tissues of rats. RT-PCR analysis showed that piperine downregulated the mRNA expression levels of keap1, JNK, ERK and p38, and upregulated the mRNA transcription levels of Nrf2 and HO-1. Western blotting results indicated that piperine could activate the protein expression levels of Nrf2 and HO-1 and inhibit the protein expression levels of keap1, p-JNK, p-ERK and p-p38. In conclusion, piperine suppressed ethanol-induced gastric ulcers in vitro and in vivo via oxidation inhibition and improving gastric-protecting activity by regulating the Nrf2/HO-1 and MAPK signalling pathways. Full article
(This article belongs to the Special Issue Functional Foods for Metabolism Regulation and Disease Improvement)
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Graphical abstract
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<p>Effect of piperine on GES-1 cells. (<b>A</b>) Cell viability. (<b>B</b>) Protective activity of piperine against ethanol-induced GES-1 cells. Ave ± SD, <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 vs. control group; ** <span class="html-italic">p</span> &lt; 0.01 vs. ethanol group.</p>
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<p>Effect of piperine pretreatment on oxidative factors in ethanol-treated GES-1 cells. (<b>A</b>) SOD; (<b>B</b>) CAT; (<b>C</b>) GSH-Px; (<b>D</b>) MDA. Ave ± SD, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 vs. control group; ** <span class="html-italic">p</span> &lt; 0.01 vs. ethanol group.</p>
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<p>Effect of piperine on ethanol-induced gastric mucosa in rats. (<b>A</b>) Macroscopic evaluation of rat gastric tissue, (<b>B</b>,<b>C</b>) H&amp;E staining Sections 200× and 400×, (<b>D</b>) ulcer area, and (<b>E</b>) ulceration protection. Ave ± SD, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 vs. NC group; ** <span class="html-italic">p</span> &lt; 0.01 vs. MC group.</p>
Full article ">Figure 4
<p>Effect of piperine pretreatment on biochemical indicators in gastric tissue. (<b>A</b>) SOD; (<b>B</b>) CAT; (<b>C</b>) GSH-Px; (<b>D</b>) MDA; (<b>E</b>) ROS; (<b>F</b>) MPO. Ave ± SD, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 vs. NC group; * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 vs. MC group.</p>
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<p>Effects of piperine on the mRNA expression of Nrf2 (<b>A</b>), Keap1 (<b>B</b>), HO-1 (<b>C</b>), ERK (<b>D</b>), JNK (<b>E</b>) and p38 (<b>F</b>) in the full-thickness gastric tissue of the rats. Ave ± SD, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 vs. NC group; * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 vs. MC group.</p>
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<p>Piperine inhibited ethanol-induced oxidative effects through Nrf2/ HO-1 and MAPK pathways in the gastric tissue of the rats. (<b>A</b>) The protein levels of Nrf2, HO-1, Keap1, <span class="html-italic">p</span>-ERK, ERK, <span class="html-italic">p</span>-JNK, JNK, <span class="html-italic">p</span>-p38 and p38 were measured by Western blot. The relative levels of (<b>B</b>) Nrf2, (<b>C</b>) Keap1, (<b>D</b>) HO-1, (<b>E</b>) <span class="html-italic">p</span>-ERK/ERK, (<b>F</b>) <span class="html-italic">p</span>-JNK/JNK, (<b>G</b>) <span class="html-italic">p</span>-p38/p38. Ave ± SD, <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 vs. NC group; * <span class="html-italic">p</span> &lt; 0.05 and *<span class="html-italic">* p</span> &lt; 0.01 vs. MC group.</p>
Full article ">Figure 6 Cont.
<p>Piperine inhibited ethanol-induced oxidative effects through Nrf2/ HO-1 and MAPK pathways in the gastric tissue of the rats. (<b>A</b>) The protein levels of Nrf2, HO-1, Keap1, <span class="html-italic">p</span>-ERK, ERK, <span class="html-italic">p</span>-JNK, JNK, <span class="html-italic">p</span>-p38 and p38 were measured by Western blot. The relative levels of (<b>B</b>) Nrf2, (<b>C</b>) Keap1, (<b>D</b>) HO-1, (<b>E</b>) <span class="html-italic">p</span>-ERK/ERK, (<b>F</b>) <span class="html-italic">p</span>-JNK/JNK, (<b>G</b>) <span class="html-italic">p</span>-p38/p38. Ave ± SD, <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 vs. NC group; * <span class="html-italic">p</span> &lt; 0.05 and *<span class="html-italic">* p</span> &lt; 0.01 vs. MC group.</p>
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13 pages, 3364 KiB  
Article
Heat-Treated Meat Origin Tracing and Authenticity through a Practical Multiplex Polymerase Chain Reaction Approach
by Yan Cheng, Sha Wang, Shilong Ju, Song Zhou, Xiaoqun Zeng, Zhen Wu, Daodong Pan, Guowei Zhong and Zhendong Cai
Nutrients 2022, 14(22), 4727; https://doi.org/10.3390/nu14224727 - 9 Nov 2022
Cited by 8 | Viewed by 1685
Abstract
Meat adulteration have become a global issue, which has increasingly raised concerns due to not only economic losses and religious issues, but also public safety and its negative effects on human health. Using optimal primers for seven target species, a multiplex PCR method [...] Read more.
Meat adulteration have become a global issue, which has increasingly raised concerns due to not only economic losses and religious issues, but also public safety and its negative effects on human health. Using optimal primers for seven target species, a multiplex PCR method was developed for the molecular authentication of camel, cattle, dog, pig, chicken, sheep and duck in one tube reaction. Species-specific amplification from the premixed total DNA of seven species was corroborated by DNA sequencing. The limit of detection (LOD) is as low as 0.025 ng DNA for the simultaneous identification of seven species in both raw and heat-processed meat or target meat: as little as 0.1% (w/w) of the total meat weight. This method is strongly reproducible even while exposed to intensively heat-processed meat and meat mixtures, which renders it able to trace meat origins in real-world foodstuffs based on the authenticity assessment of commercial meat samples. Therefore, this method is a powerful tool for the inspection of meat adulterants and has broad application prospects. Full article
(This article belongs to the Special Issue Functional Foods for Metabolism Regulation and Disease Improvement)
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<p>Verification of primer specificity with conventional simplex PCR. (<b>A</b>) Simplex PCR detection using species-specific primers for camel, cattle, dog, pig, chicken, sheep and duck origin and respective genomic DNA as the template. (<b>B</b>) PCR amplification with premixed universal primers of eukaryotic 12S rRNA, 16S rRNA and 18S rRNA genes for each meat species, respectively. (<b>C</b>) PCR amplification using individual template DNA from camel, cattle, dog, pig, chicken, sheep and duck species. MIX, a mixture of seven primer pairs of camel, cattle, dog, pig, chicken and sheep species; 1–7, a mixture of six primer pairs of six nontarget species. (<b>D</b>) PCR amplification with a pair of target primers. CM, a complete DNA mixture of seven species including camel, cattle, dog, pig, chicken, sheep and duck; 1–7, DNA mixture of six nontarget species. Lane M is ladder DNA.</p>
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<p>Validation of sensitivity of multiplex PCR assay in raw meat tissues. (<b>A</b>) Gel image of PCR fragments amplified by multiplex PCR using premixed DNA contents of seven species (10, 5, 2.5, 1, 0.5, 0.25, 0.1, 0.05, 0.025 and 0.01 ng) with seven sets of species-specific primers, respectively. (<b>B</b>) The corresponding electropherograms represent camel, cattle, dog, pig, chicken, sheep and duck in each lane. Lanes 1–10 are presented with labels of 10, 5, 2.5, 1, 0.5, 0.25, 0.1, 0.05, 0.025 and 0.01 in (<b>A</b>). The value of number at the horizontal line means the relative position of peaks distant from the top of agarose gel. The value of number at the vertical line means the fluorescent intensity of DNA-bound dyes (4 S GelRed Nucleic Acid Stain). Lane M is ladder DNA.</p>
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<p>Validation of sensitivity of multiplex PCR assay in meat model mixtures. (<b>A</b>) Gel image of PCR fragments amplified by multiplex PCR using DNA from model mixtures of camel, dog, pig, chicken, sheep and duck added to cattle at 15%, 10%, 5%, 2.5%, 1%, 0.5%, 0.25% and 0.1% of total weight, respectively. (<b>B</b>) The corresponding electropherograms represent camel, cattle, dog, pig, chicken, sheep and duck in each lane. Lanes 1–8 are presented with labels (15%, 10%, 5%, 2.5%, 1%, 0.5%, 0.25% and 0.1%) in (<b>A</b>). The value of number at the horizontal line means the relative position of peaks distant from the top of agarose gel. The value of number at the vertical line means the fluorescent intensity of DNA-bound dyes (4 S GelRed Nucleic Acid Stain). Lane M is ladder DNA.</p>
Full article ">Figure 4
<p>Validation of sensitivity of multiplex PCR assay in boiling meat tissues. (<b>A</b>) Gel image of PCR fragments amplified by multiplex PCR using premixed DNA contents of seven species (10, 5, 2.5, 1, 0.5, 0.25, 0.1, 0.05, 0.025 and 0.01 ng) with seven sets of target primers, respectively. (<b>B</b>) The corresponding electropherograms represent camel, cattle, dog, pig, chicken, sheep and duck in each lane. Lanes 1–10 are presented with labels (10, 5, 2.5, 1, 0.5, 0.25, 0.1, 0.05, 0.025 and 0.01) in (<b>A</b>). The value of number at the horizontal line means the relative position of peaks distant from the top of agarose gel. The value of number at the vertical line means the fluorescent intensity of DNA-bound dyes (4S GelRed Nucleic Acid Stain). Lane M is ladder DNA.</p>
Full article ">Figure 5
<p>Validation of sensitivity of multiplex PCR assay in microwave-cooking meat tissues. (<b>A</b>) Gel image of PCR fragments amplified by multiplex PCR using premixed DNA contents of seven species (10, 5, 2.5, 1, 0.5, 0.25, 0.1, 0.05, 0.025 and 0.01 ng) with seven sets of target primers, respectively. (<b>B</b>) The corresponding electropherograms represent camel, cattle, dog, pig, chicken, sheep and duck in each lane. Lanes 1–10 are presented with labels (10, 5, 2.5, 1, 0.5, 0.25, 0.1, 0.05, 0.025 and 0.01) in (<b>A</b>). The value of number at the horizontal line means the relative position of peaks distant from the top of agarose gel. The value of number at the vertical line means the fluorescent intensity of DNA-bound dyes (4 S GelRed Nucleic Acid Stain). Lane M is ladder DNA.</p>
Full article ">
26 pages, 9985 KiB  
Article
Efficacy and Mechanism of Pueraria lobata and Pueraria thomsonii Polysaccharides in the Treatment of Type 2 Diabetes
by Zhujun Wang, Hui Du, Wanqian Peng, Shilin Yang, Yulin Feng, Hui Ouyang, Weifeng Zhu and Ronghua Liu
Nutrients 2022, 14(19), 3926; https://doi.org/10.3390/nu14193926 - 22 Sep 2022
Cited by 19 | Viewed by 3414
Abstract
Diabetes is called a “wasting and thirsting disorder” in Chinese traditional medicine because there is a depletion of vital substances in the body independent of the intake of food or water and an inability to reintroduce fluids through drinking. Pueraria lobata (Willd.) Ohwi [...] Read more.
Diabetes is called a “wasting and thirsting disorder” in Chinese traditional medicine because there is a depletion of vital substances in the body independent of the intake of food or water and an inability to reintroduce fluids through drinking. Pueraria lobata (Willd.) Ohwi (GG) and Pueraria thomsonii Benth. (FG) are traditional Chinese herbal medicines used in the treatment of wasting-thirst that reduce blood glucose levels. Flavonoids are the main pharmacodynamic components of GG and FG, and they are also the most studied components at present, but polysaccharides are also active components of GG and FG, which, however, are less studied. Therefore, this study aimed to investigate the effect of Pueraria polysaccharides (GG and FG polysaccharides) on type 2 diabetes (T2D), as well as their related mechanisms of action in terms of both intestinal flora and metabolomics. The C57BL/KsJ-db/db mouse model, a well-established model of obesity-induced T2D, was used in this study. The metabolomic analysis showed that Pueraria polysaccharides improved the metabolic profile of diabetic mice and significantly regulated metabolites and metabolic pathways. Both GG and FG polysaccharides regulated insulin resistance in mice by regulating PPAR signaling pathway so as to treat T2D. Additionally, Pueraria polysaccharides regulated the structure of gut microbiota and improved the diabetes-related metabolic pathway. Therefore, this study discovered the antidiabetic effects and potential mechanisms of Pueraria polysaccharides through multiple pathways involving gut microbiota and metabolites, providing a theoretical basis for further studies on their effects in the treatment of T2D. Full article
(This article belongs to the Special Issue Functional Foods for Metabolism Regulation and Disease Improvement)
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<p>(<b>A</b>) Effect of <span class="html-italic">Pueraria</span> polysaccharide on body weight of db/db mice (<span class="html-italic">n</span> = 10; ** <span class="html-italic">p</span> &lt; 0.01 compared with the model group). (<b>B</b>) Effect of <span class="html-italic">Pueraria</span> polysaccharide on the water intake of db/db mice (<span class="html-italic">n</span> = 10; ** <span class="html-italic">p</span> &lt; 0.01 compared with the model group). (<b>C</b>) Effect of <span class="html-italic">Pueraria</span> polysaccharide on food intake of db/db mice (<span class="html-italic">n</span> = 10; ** <span class="html-italic">p</span> &lt; 0.01 compared with the model group). (<b>D</b>) Effect of <span class="html-italic">Pueraria</span> polysaccharide on fasting blood glucose in db/db mice (<span class="html-italic">n</span> = 10; * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01 compared with the model group).</p>
Full article ">Figure 2
<p>Effects of <span class="html-italic">Pueraria</span> polysaccharide on the glucose tolerance of db/db mice (<span class="html-italic">n</span> = 10; * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01 compared with the model group). (<b>A</b>) Effect of <span class="html-italic">Pueraria</span> polysaccharide on blood glucose value in db/db mice; (<b>B</b>) Effect of <span class="html-italic">Pueraria</span> polysaccharide on AUC in db/db mice.</p>
Full article ">Figure 3
<p>Effect of <span class="html-italic">Pueraria</span> polysaccharide on the insulin tolerance of db/db mice (<span class="html-italic">n</span> = 10; * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01 compared with the model group). (<b>A</b>) Effect of <span class="html-italic">Pueraria</span> polysaccharide on blood glucose value in db/db mice; (<b>B</b>) Effect of <span class="html-italic">Pueraria</span> polysaccharide on AUC in db/db mice.</p>
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<p>H&amp;E staining of the liver tissue of db/db mice (400× magnification).</p>
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<p>Metabolic profile analysis of the serum of the T2D model in db/db mice ((<b>A</b>). NEG, PCA; (<b>B</b>). POS, PCA; (<b>C</b>). NEG, OPLS-DA; (<b>D</b>). POS, OPLS-DA).</p>
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<p>PRT analysis of the T2D model in db/db mice ((<b>A</b>). NEG, PRT; (<b>B</b>). POS, PRT).</p>
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<p>Heat map analysis of differential metabolites in T2D db/db mice. (Control 1–10 represents <a href="#nutrients-14-03926-t001" class="html-table">Table 1</a>. Model represents the model group).</p>
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<p>Overview of the metabolic pathway analysis. 1. Arachidonic acid metabolism; 2. Alpha-linolenic acid metabolism; 3. Glycerophospholipid metabolism; 4. Retinol metabolism; 5. Steroid hormone biosynthesis; 6. Glycerolipid metabolism; 7. Biosynthesis of unsaturated fatty acids.</p>
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<p>Metabolic profile in the serum of the T2D db/db mice treated with GG polysaccharide ((<b>A</b>). NEG, PCA; (<b>B</b>). POS, PCA; (<b>C</b>). NEG, PLS-DA; (<b>D</b>). POS, PLS-DA).</p>
Full article ">Figure 10
<p>Metabolic profile in the serum samples of T2D db/db mice treated with FG polysaccharide ((<b>A</b>). NEG, PCA; (<b>B</b>). POS, PCA; (<b>C</b>). NEG, PLS-DA; (<b>D</b>). POS, PLS-DA).</p>
Full article ">Figure 11
<p>(<b>A</b>) Heat map analysis of differential metabolites in the serum after GG polysaccharide treatment. (<b>B</b>) Heat map analysis of differential metabolites in the serum after FG polysaccharide treatment.</p>
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<p>Correlation analysis of metabolites and biochemical indices affected by <span class="html-italic">Pueraria</span> polysaccharide intervention ((<b>A</b>): GG; (<b>B</b>): FG; red represents a positive correlation, blue represents a negative correlation, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001).</p>
Full article ">Figure 13
<p>Changes in the relative abundance of cecal microflora at the phylum level in db/db mice (<span class="html-italic">n</span> = 10). (<b>A</b>) community barplot analysis on Phylum level; (<b>B</b>) community heatmap analysis on Phylum level.</p>
Full article ">Figure 13 Cont.
<p>Changes in the relative abundance of cecal microflora at the phylum level in db/db mice (<span class="html-italic">n</span> = 10). (<b>A</b>) community barplot analysis on Phylum level; (<b>B</b>) community heatmap analysis on Phylum level.</p>
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<p>(<b>A</b>) PCoA analysis of the cecal microflora in db/db mice based on the weighted-unifrac distance (<span class="html-italic">n</span> = 10); (<b>B</b>) the distribution of different groups of samples on the PC1 axis.</p>
Full article ">Figure 15
<p>LEfSe linear discriminant analysis of the cecal microflora in db/db mice ((<b>A</b>). normal group vs. model group; (<b>B</b>). model group vs. FG group; (<b>C</b>). model group vs. GG group).</p>
Full article ">Figure 15 Cont.
<p>LEfSe linear discriminant analysis of the cecal microflora in db/db mice ((<b>A</b>). normal group vs. model group; (<b>B</b>). model group vs. FG group; (<b>C</b>). model group vs. GG group).</p>
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<p>Effects of <span class="html-italic">Pueraria</span> polysaccharide on the cecal microflora in db/db mice ((<b>A</b>). FG; (<b>B</b>). GG).</p>
Full article ">Figure 16 Cont.
<p>Effects of <span class="html-italic">Pueraria</span> polysaccharide on the cecal microflora in db/db mice ((<b>A</b>). FG; (<b>B</b>). GG).</p>
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<p>KEGG pathway level 1 box diagram (<span class="html-italic">n</span> = 10; * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01 compared with the model group).</p>
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<p>Heat map of KEGG pathway level 3 functional abundance (<span class="html-italic">n</span> = 10).</p>
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<p>Correlation analysis between the cecal microflora and metabolites affected by <span class="html-italic">Pueraria</span> polysaccharide intervention ((<b>A</b>): GG; (<b>B</b>): FG; red represents a positive correlation, blue represents a negative correlation, * <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 GG polysaccharide and FG polysaccharide on liver protein expression in db/db mice ((<b>A</b>). LKB1; (<b>B</b>). P-AMPK/AMPK; (<b>C</b>). P-TSC2/TSC2; (<b>D</b>). P-mTOR/mTOR; (<b>E</b>). PPAR gamma. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 compared with the model group).</p>
Full article ">

Review

Jump to: Research

32 pages, 4386 KiB  
Review
Polyphenols in Oral Health: Homeostasis Maintenance, Disease Prevention, and Therapeutic Applications
by Yuanyuan Guo, Zhiquan Li, Feng Chen and Yujuan Chai
Nutrients 2023, 15(20), 4384; https://doi.org/10.3390/nu15204384 - 16 Oct 2023
Cited by 9 | Viewed by 3837
Abstract
Polyphenols, a class of bioactive compounds with phenolic structures, are abundant in human diets. They have gained attention in biomedical fields due to their beneficial properties, including antioxidant, antibacterial, and anti-inflammatory activities. Therefore, polyphenols can prevent multiple chronic or infectious diseases and may [...] Read more.
Polyphenols, a class of bioactive compounds with phenolic structures, are abundant in human diets. They have gained attention in biomedical fields due to their beneficial properties, including antioxidant, antibacterial, and anti-inflammatory activities. Therefore, polyphenols can prevent multiple chronic or infectious diseases and may help in the prevention of oral diseases. Oral health is crucial to our well-being, and maintaining a healthy oral microbiome is essential for preventing various dental and systemic diseases. However, the mechanisms by which polyphenols modulate the oral microbiota and contribute to oral health are still not fully understood, and the application of polyphenol products lies in different stages. This review provides a comprehensive overview of the advancements in understanding polyphenols’ effects on oral health: dental caries, periodontal diseases, halitosis, and oral cancer. The mechanisms underlying the preventive and therapeutic effects of polyphenols derived from dietary sources are discussed, and new findings from animal models and clinical trials are included, highlighting the latest achievements. Given the great application potential of these natural compounds, novel approaches to dietary interventions and oral disease treatments may emerge. Moreover, investigating polyphenols combined with different materials presents promising opportunities for developing innovative therapeutic strategies in the treatment of oral diseases. Full article
(This article belongs to the Special Issue Functional Foods for Metabolism Regulation and Disease Improvement)
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<p>Flowchart of the search strategy and the literature selection process.</p>
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<p>Polyphenols in dental caries, periodontal diseases, halitosis, and oral cancers. In dental caries, polyphenols have been investigated for their antibacterial properties, effectively suppressing bacterial growth and adhesion. They also inhibit glycosyltransferase enzyme (GTF) activity, reduce the cariogenic impact of exopolysaccharides (EPS), and disrupt biofilm formation. In conditions involving inflammation, bleeding, and gum recession, polyphenols offer antimicrobial effects, enhance immunomodulation, and exhibit anti-inflammatory properties. They help mitigate oxidative stress, a critical factor in periodontal diseases. For halitosis, polyphenols possess antibacterial and antioxidant properties. In the meantime, they help reduce the volatile sulfur compounds (VSC), the primary source of halitosis. Regarding oral cancers, polyphenols have a multifaceted impact on oral cancer cells, including the inhibition of growth and division, decreased invasion and migration, enhanced apoptotic activity, and reduced expression of inflammatory cytokines such as IL-1β, IL-6, and IL-8. The small blue arrows indicate the upregulation/promotion or downregulation/inhibition of the biomarkers or behaviors.</p>
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<p>Current approaches and prospects of polyphenols-based oral hygiene. Diverse strategies are employed in polyphenols-based oral hygiene, ranging from conventional methods to cutting-edge developments in biomaterials. Current approaches include widely-used products for routine cleaning, e.g., toothpaste and mouthwash; saliva stimulation, e.g., chewing gum and lozenges; or even polyphenols-containing gels and tinctures. Emerging trends include the development of hydrogels, human-like collagen, and other new biomaterials, such as nanoparticles, programmed core-shell nanofibers, and ceramic granulated biomaterials.</p>
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14 pages, 901 KiB  
Review
Effects of Diet and Exercise on Circadian Rhythm: Role of Gut Microbiota in Immune and Metabolic Systems
by Yidan Cai, Yanan Liu, Zufang Wu, Jing Wang and Xin Zhang
Nutrients 2023, 15(12), 2743; https://doi.org/10.3390/nu15122743 - 14 Jun 2023
Cited by 7 | Viewed by 4503
Abstract
A close relationship exists between the intestinal microbiota and the circadian rhythm, which is mainly regulated by the central-biological-clock system and the peripheral-biological-clock system. At the same time, the intestinal flora also reflects a certain rhythmic oscillation. A poor diet and sedentary lifestyle [...] Read more.
A close relationship exists between the intestinal microbiota and the circadian rhythm, which is mainly regulated by the central-biological-clock system and the peripheral-biological-clock system. At the same time, the intestinal flora also reflects a certain rhythmic oscillation. A poor diet and sedentary lifestyle will lead to immune and metabolic diseases. A large number of studies have shown that the human body can be influenced in its immune regulation, energy metabolism and expression of biological-clock genes through diet, including fasting, and exercise, with intestinal flora as the vector, thereby reducing the incidence rates of diseases. This article mainly discusses the effects of diet and exercise on the intestinal flora and the immune and metabolic systems from the perspective of the circadian rhythm, which provides a more effective way to prevent immune and metabolic diseases by modulating intestinal microbiota. Full article
(This article belongs to the Special Issue Functional Foods for Metabolism Regulation and Disease Improvement)
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<p>The interaction between gut microbiota and circadian rhythm.</p>
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<p>The effects of reasonable exercise for disease prevention through intestinal microbiota. “↑”: Increase, “↓”: Decrease.</p>
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16 pages, 754 KiB  
Review
Benefits of Whey Proteins on Type 2 Diabetes Mellitus Parameters and Prevention of Cardiovascular Diseases
by Jean-François Lesgards
Nutrients 2023, 15(5), 1294; https://doi.org/10.3390/nu15051294 - 6 Mar 2023
Cited by 8 | Viewed by 8433
Abstract
Type 2 diabetes mellitus (T2DM) is a major cause of morbidity and mortality, and it is a major risk factor for the early onset of cardiovascular diseases (CVDs). More than genetics, food, physical activity, walkability, and air pollution are lifestyle factors, which have [...] Read more.
Type 2 diabetes mellitus (T2DM) is a major cause of morbidity and mortality, and it is a major risk factor for the early onset of cardiovascular diseases (CVDs). More than genetics, food, physical activity, walkability, and air pollution are lifestyle factors, which have the greatest impact on T2DM. Certain diets have been shown to be associated with lower T2DM and cardiovascular risk. Diminishing added sugar and processed fats and increasing antioxidant-rich vegetable and fruit intake has often been highlighted, as in the Mediterranean diet. However, less is known about the interest of proteins in low-fat dairy and whey in particular, which have great potential to improve T2DM and could be used safely as a part of a multi-target strategy. This review discusses all the biochemical and clinical aspects of the benefits of high-quality whey, which is now considered a functional food, for prevention and improvement of T2DM and CVDs by insulin- and non-insulin-dependent mechanisms. Full article
(This article belongs to the Special Issue Functional Foods for Metabolism Regulation and Disease Improvement)
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<p>Mechanisms implicated in whey protein activity on postprandial glycemia reduction. GIP: glucose-dependent insulinotropic polypeptide; GLP-1: glucagon-like-peptide-1; CCK: cholecystokinin; PYY: peptide YY; DPP-IV: dipeptidyl peptidase-IV; BCAAs: branched-chain amino acids.</p>
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17 pages, 1242 KiB  
Review
Postbiotics in Human Health: A Narrative Review
by Linxi Ma, Huaijun Tu and Tingtao Chen
Nutrients 2023, 15(2), 291; https://doi.org/10.3390/nu15020291 - 6 Jan 2023
Cited by 43 | Viewed by 10986
Abstract
In the 21st century, compressive health and functional foods are advocated by increasingly more people in order to eliminate sub-health conditions. Probiotics and postbiotics have gradually become the focus of scientific and nutrition communities. With the maturity and wide application of probiotics, the [...] Read more.
In the 21st century, compressive health and functional foods are advocated by increasingly more people in order to eliminate sub-health conditions. Probiotics and postbiotics have gradually become the focus of scientific and nutrition communities. With the maturity and wide application of probiotics, the safety concerns and other disadvantages are non-negligible as we review here. As new-era products, postbiotics continue to have considerable potential as well as plentiful drawbacks to optimize. “Postbiotic” has been defined as a “preparation of inanimate microorganisms and/or their components that confers a health benefit on the host”. Here, the evolution of the concept “postbiotics” is reviewed. The underlying mechanisms of postbiotic action are discussed. Current insight suggests that postbiotics exert efficacy through protective modulation, fortifying the epithelial barrier and modulation of immune responses. Finally, we provide an overview of the comparative advantages and the current application in the food industry at pharmaceutical and biomedical levels. Full article
(This article belongs to the Special Issue Functional Foods for Metabolism Regulation and Disease Improvement)
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Figure 1

Figure 1
<p>Predominant mechanisms of probiotic action. Probiotic-pathogen interactions in the middle part of the figure include three mechanisms: (1) direct combination, (2) competitive exclusion, (3) secretion of antimicrobial compounds; Probiotic-host interactions in the right part of the figure include three mechanisms: (4) synergistic effects with indigenous microbiota, (5) enhancement of epithelial barrier integrity, (6) modulation of immune system. At the intestinal level in the left part of the figure, probiotics have an effect through: (7) upregulation of electrolyte absorption, (8) modulation of gut motility, (9) alteration of painful sensations. (Figure was created with Biorender. com).</p>
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<p>Predominant mechanisms of postbiotic action. (<b>A</b>). Protective modulation against pathogens. (<b>B</b>). Fortify the epithelial barrier. (<b>C</b>). Modulation of immune responses. (figure was created with Biorender. com).</p>
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<p>Comparison between probiotics and postbiotics in application.</p>
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