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Search Results (13,457)

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17 pages, 698 KiB  
Review
Gut Microbiota in Patients Receiving Dialysis: A Review
by Xintian Lim, Lijin Ooi, Uzhe Ding, Henry H. L. Wu and Rajkumar Chinnadurai
Pathogens 2024, 13(9), 801; https://doi.org/10.3390/pathogens13090801 (registering DOI) - 15 Sep 2024
Abstract
The human gut microbiota constitutes a complex community of microorganisms residing within the gastrointestinal tract, encompassing a vast array of species that play crucial roles in health and disease. The disease processes involved in chronic kidney disease (CKD) and end-stage kidney disease (ESKD) [...] Read more.
The human gut microbiota constitutes a complex community of microorganisms residing within the gastrointestinal tract, encompassing a vast array of species that play crucial roles in health and disease. The disease processes involved in chronic kidney disease (CKD) and end-stage kidney disease (ESKD) are now increasingly established to result in dysregulation of gut microbiota composition and function. Gut microbiota dysbiosis has been associated with poor clinical outcomes and all-cause mortality in patients with ESKD, particularly individuals receiving dialysis. Prior studies highlighted various factors that affect gut microbiota dysbiosis in CKD and ESKD. These include, but are not limited to, uraemic toxin accumulation, chronic inflammation, immune dysfunction, medications, and dietary restrictions and nutritional status. There is a lack of studies at present that focus on the evaluation of gut microbiota dysbiosis in the context of dialysis. Knowledge on gut microbiota changes in this context is important for determining their impact on dialysis-specific and overall outcomes for this patient cohort. More importantly, evaluating gut microbiota composition can provide information into potential targets for therapeutic intervention. Identification of specific microbial signatures may result in further development of personalised treatments to improve patient outcomes and mitigate complications during dialysis. Optimising gut microbiota through various therapeutic approaches, including dietary adjustments, probiotics, prebiotics, medications, and faecal transplantation, have previously demonstrated potential in multiple medical conditions. It remains to be seen whether these therapeutic approaches are effective within the dialysis setting. Our review aims to evaluate evidence relating to alterations in the gut microbiota of patients undergoing dialysis. A growing body of evidence pointing to the complex yet significant relationship which surrounds gut microbiota and kidney health emphasises the importance of gut microbial balance to improve outcomes for individuals receiving dialysis. Full article
(This article belongs to the Special Issue Molecular Epidemiology of Pathogenic Agents)
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<p>Key factors that may explain gut dysbiosis in patients receiving dialysis. PBUT: protein-bound uraemic toxin; IS: indoxyl sulfate; PCS: P-cresyl sulfate; SCFAs: short-chain fatty acids; TMAO: trimethylamine N-oxide; PPI: proton pump inhibitor.</p>
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13 pages, 1965 KiB  
Article
The Impact of Ten Days of Periodic Fasting on the Modulation of the Longevity Gene in Overweight and Obese Individuals: A Quasi-Experimental Study
by Nurma Yuliyanasari, Eva Nabiha Zamri, Purwo Sri Rejeki and Muhammad Miftahussurur
Nutrients 2024, 16(18), 3112; https://doi.org/10.3390/nu16183112 (registering DOI) - 15 Sep 2024
Abstract
Background: Fasting potentially alters the aging process induced by obesity by regulating telomere integrity, which is related to longevity genes. However, the impact of periodic fasting (PF) on the expression of longevity genes, particularly Forkhead Box O Transcription Factors (FOXO3a) and the Human [...] Read more.
Background: Fasting potentially alters the aging process induced by obesity by regulating telomere integrity, which is related to longevity genes. However, the impact of periodic fasting (PF) on the expression of longevity genes, particularly Forkhead Box O Transcription Factors (FOXO3a) and the Human Telomerase Reverse Transcriptase (hTERT), is not fully understood. This study aimed to analyze the effects of PF, specifically on FOXO3a, hTERT expression, and other associated factors. Methods: A quasi-experimental 10-day study was conducted in Surabaya, East Java, Indonesia. This study consisted of an intervention group (PFG), which carried out PF for ten days using a daily 12 h time-restricted eating protocol, and a control group (CG), which had daily meals as usual. FOXO3a and hTERT expression were analyzed by quantitative real-time qPCR. A paired t-test/Wilcoxon test, independent t-test/Mann–Whitney U-test, and Spearman’s correlation test were used for statistical analysis. Result: Thirty-six young men participated in this study. During the post-test period, FOXO3a expression in the PFG increased 28.56 (±114.05) times compared to the pre-test, but the difference was not significant. hTERT expression was significantly higher in both the CG and PFG. The hTERT expression in the PFG was 10.26 (±8.46) times higher than in the CG, which was only 4.73 (±4.81) times higher. There was also a positive relationship between FOXO and hTERT in the CG. Conclusions: PF significantly increased hTERT expression in the PFG; however, no significant increase was found in FOXO3a expression. PF regimens using the 12 h time-restricted eating approach may become a potential strategy for preventing obesity-induced premature aging by regulating longevity gene expression. Full article
(This article belongs to the Section Nutrition and Obesity)
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Graphical abstract

Graphical abstract
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<p>Research workflow.</p>
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<p>Effects of PF on glucose and lipid profiles after 10 days. (<b>a</b>) FBG level in the CG; (<b>b</b>) FBG level in the PFG; (<b>c</b>) TC level in the CG; (<b>d</b>) TC level in the PFG. The difference between pre-test and post-test data was analyzed using the paired <span class="html-italic">t</span>-Test or Wilcoxon test. (**) indicates a significant difference.</p>
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<p>The difference in FOXO3a expression of the participants. (<b>a</b>) dCT of FOXO3a in the CG; (<b>b</b>) FOXO3a relative expression from ΔΔCt in the CG; (<b>c</b>) dCT of FOXO3a in the PFG; (<b>d</b>) FOXO3a relative expression from ΔΔCt in the PFG. The difference between pre-test and post-test data was analyzed using the paired <span class="html-italic">t</span>-test or Wilcoxon test.</p>
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<p>The difference in hTERT expression of the participants. (<b>a</b>) dCT of hTERT in the CG; (<b>b</b>) hTERT relative expression from ΔΔCt in the CG; (<b>c</b>) dCT of hTERT in the PFG; (<b>d</b>) hTERT relative expression from ΔΔCt in the PFG. The difference between pre-test and post-test data was analyzed using the paired <span class="html-italic">t</span>-test or Wilcoxon test. (**) indicates a significant difference.</p>
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13 pages, 1596 KiB  
Article
Nitrogen Fixation, Carbohydrate Contents, and Bacterial Microbiota in Unelongated Stem of Manure Compost-Applied Rice at Panicle Initiation
by Zhalaga Ao, Miu Tsuchiya, Juan Xia, Chie Hayakawa, Yukitsugu Takahashi, Hideaki Hirai and Isamu Maeda
Microbiol. Res. 2024, 15(3), 1900-1912; https://doi.org/10.3390/microbiolres15030127 (registering DOI) - 15 Sep 2024
Abstract
In rice, symbiotic N2 fixation via nodule bacteroids does not take place naturally. Although N2 fixation by endophytic and associative diazotrophs has been reported in rice, the main organs and seasonal regulation for the N2 fixation have not been elucidated. [...] Read more.
In rice, symbiotic N2 fixation via nodule bacteroids does not take place naturally. Although N2 fixation by endophytic and associative diazotrophs has been reported in rice, the main organs and seasonal regulation for the N2 fixation have not been elucidated. In this study, seasonal changes in nitrogenase (acetylene reduction) activity and carbohydrate contents in elongated culm (EC), unelongated stem (US), and crown root (CR) were investigated in manure compost (MC)- and chemical fertilizer (CF)-applied rice. Nitrogenase activity increased after rooting (June) and reached the highest activity in US of MC-applied rice at panicle initiation (August). The sucrose content in EC continued to increase after rooting regardless of the applied materials, whereas the glucose content in US increased after rooting only in CF-applied rice, suggesting higher consumption of glucose in US of MC-applied rice. There were significant differences among bacterial microbiota in EC, US, and CR at panicle initiation. In addition, Clostridia class anaerobes were more abundant in US of MC-applied rice than in EC and CR of MC-applied rice. Such difference was not observed in US of CF-applied rice. These results suggest the suitability of US of MC-applied rice at panicle initiation as a site of N2 fixation under anaerobic conditions. Full article
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<p>The parts of rice cultured in a wetland rice field at Utsunomiya University, Moka, Japan, and collected at panicle initiation. The indicated parts were cut from the whole plant (<b>A</b>). The rice parts from chemical fertilizer (CF)-applied rice (<b>B</b>) and manure compost (MC)-applied rice (<b>C</b>) are shown. The centimeter scale is shown.</p>
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<p>Properties related to nitrogen fixation in elongated culm (EC), unelongated stem (US), and crown root (CR) of manure compost (MC)- and chemical fertilizer (CF)-applied rice at panicle initiation in 2022. Columns with error bars for nitrogenase activity (<b>A</b>), <span class="html-italic">nifH</span> copy number/16S rRNA gene copy number (<b>B</b>), sucrose content (<b>C</b>), and glucose content (<b>D</b>) indicate means ± SD (<span class="html-italic">n</span> = 3). Different lowercase letters show significant differences in multiple comparisons among different material-applied rice specimens in different parts (<span class="html-italic">p</span> &lt; 0.05). ND = not detected.</p>
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<p>The principal coordinate analysis (PCoA) of the bacterial microbiota in the parts of manure compost (MC)-applied and chemical fertilizer (CF)-applied rice at panicle initiation. Coordinate points for elongated culm (EC; green), unelongated stem (US; blue), and crown root (CR; red) of MC-applied (closed) and CF-applied (open) rice are shown. Drawings indicate 95% confidence ellipses for EC (green), US (blue), and CR (red) of MC-applied (solid) and CF-applied (dashed) rice.</p>
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<p>Class-level and species-level taxonomic profiles in elongated culm (EC), unelongated stem (US), and crown root (CR) of manure compost (MC)- and chemical fertilizer (CF)-applied rice at panicle initiation. A divided bar chart with error bars is composed of columns showing class-level relative abundance + SD (<span class="html-italic">n</span> = 3) (<b>A</b>). Columns with error bars indicate means of relative abundance for <span class="html-italic">Clostridiales</span> species ± SD (<span class="html-italic">n</span> = 3) (<b>B</b>). Different lowercase letters show significant difference in multiple comparisons among the relative taxonomic abundances in different rice parts of different material-applied rice (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Properties related to nitrogen fixation in elongated culm (EC), unelongated stem (US), and crown root (CR) of manure compost (MC)- and chemical fertilizer (CF)-applied rice at panicle initiation in 2023. Columns with error bars for nitrogenase activity (<b>A</b>), <span class="html-italic">nifH</span> copy number/16S rRNA gene copy number (<b>B</b>), sucrose content (<b>C</b>), and glucose content (<b>D</b>) indicate means ± SD (<span class="html-italic">n</span> = 5). Different lowercase letters show significant differences in multiple comparisons among different material-applied rice specimens in different parts (<span class="html-italic">p</span> &lt; 0.05). ND = not detected.</p>
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24 pages, 1049 KiB  
Review
Investigating the Impact of Nutrition and Oxidative Stress on Attention Deficit Hyperactivity Disorder
by Malina Visternicu, Viorica Rarinca, Vasile Burlui, Gabriela Halitchi, Alin Ciobică, Ana-Maria Singeap, Romeo Dobrin, Ioannis Mavroudis and Anca Trifan
Nutrients 2024, 16(18), 3113; https://doi.org/10.3390/nu16183113 (registering DOI) - 15 Sep 2024
Abstract
Background/Objectives: Attention deficit hyperactivity disorder (ADHD) is the most common childhood-onset neurodevelopmental disorder, characterized by difficulty maintaining attention, impulsivity, and hyperactivity. While the cause of this disorder is still unclear, recent studies have stated that heredity is important in the development of ADHD. [...] Read more.
Background/Objectives: Attention deficit hyperactivity disorder (ADHD) is the most common childhood-onset neurodevelopmental disorder, characterized by difficulty maintaining attention, impulsivity, and hyperactivity. While the cause of this disorder is still unclear, recent studies have stated that heredity is important in the development of ADHD. This is linked to a few comorbidities, including depression, criminal behavior, and anxiety. Although genetic factors influence ADHD symptoms, there are also non-genetic factors, one of which is oxidative stress (OS), which plays a role in the pathogenesis and symptoms of ADHD. This review aims to explore the role of OS in ADHD and its connection to antioxidant enzyme levels, as well as the gut–brain axis (GBA), focusing on diet and its influence on ADHD symptoms, particularly in adults with comorbid conditions. Methods: The literature search included the main available databases (e.g., Science Direct, PubMed, and Google Scholar). Articles in the English language were taken into consideration and our screening was conducted based on several words such as “ADHD”, “oxidative stress”, “diet”, “gut–brain axis”, and “gut microbiota.” The review focused on studies examining the link between oxidative stress and ADHD, the role of the gut–brain axis, and the potential impact of dietary interventions. Results: Oxidative stress plays a critical role in the development and manifestation of ADHD symptoms. Studies have shown that individuals with ADHD exhibit reduced levels of key antioxidant enzymes, including glutathione peroxidase (GPx), catalase (CAT), and superoxide dismutase (SOD), as well as a diminished total antioxidant status (TOS) compared to healthy controls. Additionally, there is evidence of a close bidirectional interaction between the nervous system and gut microbiota, mediated by the gut–brain axis. This relationship suggests that dietary interventions targeting gut health may influence ADHD symptoms and related comorbidities. Conclusions: Oxidative stress and the gut–brain axis are key factors in the pathogenesis of ADHD, particularly in adults with comorbid conditions. A better understanding of these mechanisms could lead to more targeted treatments, including dietary interventions, to mitigate ADHD symptoms. Further research is required to explore the therapeutic potential of modulating oxidative stress and gut microbiota in the management of ADHD. Full article
(This article belongs to the Section Nutrition and Metabolism)
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<p>PRISMA flow chart illustrating the selection of studies and exclusion criteria.</p>
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<p>Treatment used for ADHD.</p>
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20 pages, 5074 KiB  
Article
Chlorogenic Acid Enhances the Intestinal Health of Weaned Piglets by Inhibiting the TLR4/NF-κB Pathway and Activating the Nrf2 Pathway
by Beibei Zhang, Min Tian, Jing Wu, Yueqin Qiu, Xiaoming Xu, Chaoyang Tian, Jing Hou, Li Wang, Kaiguo Gao, Xuefen Yang and Zongyong Jiang
Int. J. Mol. Sci. 2024, 25(18), 9954; https://doi.org/10.3390/ijms25189954 (registering DOI) - 15 Sep 2024
Abstract
Chlorogenic acid (CGA) is a natural polyphenol with potent antioxidant and anti-inflammatory activities. However, the exact role of it in regulating intestinal health under oxidative stress is not fully understood. This study aims to investigate the effects of dietary CGA supplementation on the [...] Read more.
Chlorogenic acid (CGA) is a natural polyphenol with potent antioxidant and anti-inflammatory activities. However, the exact role of it in regulating intestinal health under oxidative stress is not fully understood. This study aims to investigate the effects of dietary CGA supplementation on the intestinal health of weaned piglets under oxidative stress, and to explore its regulatory mechanism. Twenty-four piglets were randomly divided into two groups and fed either a basal diet (CON) or a basal diet supplemented with 200 mg/kg CGA (CGA). CGA reduced the diarrhea rate, increased the villus height in the jejunum, and decreased the crypt depth in the duodenum, jejunum, and ileum of the weaned piglets (p < 0.05). Moreover, CGA increased the protein abundance of Claudin-1, Occludin, and zonula occludens (ZO)-1 in the jejunum and ileum (p < 0.05). In addition, CGA increased the mRNA expression of pBD2 in the jejunum, and pBD1 and pBD2 in the ileum (p < 0.05). The results of 16S rRNA sequencing showed that CGA altered the ileal microbiota composition and increased the relative abundance of Lactobacillus reuteri and Lactobacillus pontis (p < 0.05). Consistently, the findings suggested that the enhancement of the intestinal barrier in piglets was associated with increased concentrations of T-AOC, IL-22, and sIgA in the serum and T-AOC, T-SOD, and sIgA in the jejunum, as well as T-AOC and CAT in the ileum caused by CGA (p < 0.05). Meanwhile, CGA decreased the concentrations of MDA, IL-1β, IL-6, and TNF-α in the serum and jejunum and IL-1β and IL-6 in the ileum (p < 0.05). Importantly, this study found that CGA alleviated intestinal inflammation and oxidative stress in the piglets by inhibiting the TLR4/NF-κB signaling pathway and activating the Nrf2 signaling pathway. These findings showed that CGA enhances the intestinal health of weaned piglets by inhibiting the TLR4/NF-κB pathway and activating the Nrf2 pathway. Full article
(This article belongs to the Special Issue Antibacterial and Antioxidant Effects of Plant-Sourced Compounds)
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<p>Effects of dietary supplementation with CGA on the villi height and crypt depth of the small intestine in piglets. (<b>A</b>) H&amp;E staining of the intestine (scale bar, 500 μm); (<b>B</b>–<b>D</b>) analysis of villi height and crypt depth in the intestine. Values are presented as the mean ± SEM (<span class="html-italic">n</span> = 6). *, 0.01 &lt; <span class="html-italic">p</span> ≤ 0.05. **, <span class="html-italic">p</span> ≤ 0.01.</p>
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<p>Effects of dietary CGA supplementation on the expression of tight junctions in the jejunum and ileum. (<b>A</b>,<b>B</b>) The immunoreactivity of tight junctions in the jejunum and ileum of piglets; (<b>C</b>–<b>F</b>) Western blot analysis of tight junctions in the jejunum and ileum of piglets. <span class="html-italic">n</span> = 6. *, 0.01 &lt; <span class="html-italic">p</span> ≤ 0.05. **, <span class="html-italic">p</span> ≤ 0.01.</p>
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<p>Effects of dietary CGA supplementation on the mRNA expression of mucins and porcine beta defensins in the jejunum (<b>A</b>) and ileum (<b>B</b>) mucosa of piglets. MUC, mucin; pBD, porcine beta defensins; PG1, Protegrin-1. Values are presented as the mean ± SEM (<span class="html-italic">n</span> = 6). *, 0.01 &lt; <span class="html-italic">p</span> ≤ 0.05.</p>
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<p>Effects of dietary CGA supplementation on the ileal microbiota of piglets. (<b>A</b>–<b>G</b>) Relative abundance of microbiota at the phylum, genus, and species levels. (<b>H</b>) Beta diversity; (<b>I</b>–<b>K</b>) alpha diversity. Values are presented as the mean ± SEM (<span class="html-italic">n</span> = 6). *, 0.01 &lt; <span class="html-italic">p</span> ≤ 0.05. **, <span class="html-italic">p</span> ≤ 0.01.</p>
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<p>Effects of dietary supplementation with CGA on antioxidant status and immune-inflammatory level in the serum of piglets. (<b>A</b>–<b>D</b>) The antioxidative and oxidative indicators in the serum. (<b>E</b>–<b>H</b>); the concentration of inflammatory factors in the serum; (<b>I</b>,<b>J</b>) the immunoglobulin concentration in the serum. Values are presented as the mean ± SEM (<span class="html-italic">n</span> = 6). *, 0.01 &lt; <span class="html-italic">p</span> ≤ 0.05. **, <span class="html-italic">p</span> ≤ 0.01.</p>
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<p>Effects of dietary supplementation with CGA on antioxidant status and immune-inflammatory level in the jejunum and ileum of piglets. (<b>A</b>–<b>D</b>) The antioxidative and oxidative indicators in the serum; (<b>E</b>–<b>H</b>) the concentration of inflammatory factors in the serum; (<b>I</b>) the immunoglobulin concentration in the serum. Values are presented as the mean ± SEM (<span class="html-italic">n</span> = 6). *, 0.01 &lt; <span class="html-italic">p</span> ≤ 0.05. **, <span class="html-italic">p</span> ≤ 0.01.</p>
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<p>Effects of dietary CGA supplementation on the activation of the NF-κB and Nrf2 signaling pathways in the jejunum and ileum of piglets. (<b>A</b>–<b>D</b>) Jejunum; (<b>E</b>–<b>H</b>) ileum. *, 0.01 &lt; <span class="html-italic">p</span> ≤ 0.05. **, <span class="html-italic">p</span> ≤ 0.01.</p>
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<p>Correlation analysis between microorganisms, oxidative stress indicators, immunoglobulins, and inflammatory factors in the intestines of piglets. *, 0.01 &lt; <span class="html-italic">p</span> ≤ 0.05. **, <span class="html-italic">p</span> ≤ 0.01.</p>
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<p>Network pharmacological analysis between CGA, oxidative stress, and inflammation. (<b>A</b>,<b>B</b>) Intersection analysis between CGA targets and the disease targets of oxidative stress and inflammation, as well as screening of core targets; (<b>C</b>) the schematic diagram of the interaction between CGA and TLR4; (<b>D</b>) the top 20 KEGG pathways.</p>
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23 pages, 573 KiB  
Review
Substitutive Effects of Milk vs. Vegetable Milk on the Human Gut Microbiota and Implications for Human Health
by Alicia del Carmen Mondragon Portocarrero, Aroa Lopez-Santamarina, Patricia Regal Lopez, Israel Samuel Ibarra Ortega, Hatice Duman, Sercan Karav and Jose Manuel Miranda
Nutrients 2024, 16(18), 3108; https://doi.org/10.3390/nu16183108 (registering DOI) - 14 Sep 2024
Viewed by 253
Abstract
Background: In the last two decades, the consumption of plant-based dairy substitutes in place of animal-based milk has increased in different geographic regions of the world. Dairy substitutes of vegetable origin have a quantitative composition of macronutrients such as animal milk, although the [...] Read more.
Background: In the last two decades, the consumption of plant-based dairy substitutes in place of animal-based milk has increased in different geographic regions of the world. Dairy substitutes of vegetable origin have a quantitative composition of macronutrients such as animal milk, although the composition of carbohydrates, proteins and fats, as well as bioactive components, is completely different from that of animal milk. Many milk components have been shown to have relevant effects on the intestinal microbiota. Methods: Therefore, the aim of this review is to compare the effects obtained by previous works on the composition of the gut microbiota after the ingestion of animal milk and/or vegetable beverages. Results: In general, the results obtained in the included studies were very positive for animal milk intake. Thus, we found an increase in gut microbiota richness and diversity, increase in the production of short-chain fatty acids, and beneficial microbes such as Bifidobacterium, lactobacilli, Akkermansia, Lachnospiraceae or Blautia. In other cases, we found a significant decrease in potential harmful bacteria such as Proteobacteria, Erysipelotrichaceae, Desulfovibrionaceae or Clostridium perfingens after animal-origin milk intake. Vegetable beverages have also generally produced positive results in the gut microbiota such as the increase in the relative presence of lactobacilli, Bifidobacterium or Blautia. However, we also found some potential negative results, such as increases in the presence of potential pathogens such as Enterobacteriaceae, Salmonella and Fusobacterium. Conclusions: From the perspective of their effects on the intestinal microbiota, milks of animal origin appear to be more beneficial for human health than their vegetable substitutes. These different effects on the intestinal microbiota should be considered in those cases where the replacement of animal milks by vegetable substitutes is recommended. Full article
19 pages, 7242 KiB  
Article
Proteomics and Microbiota Conjoint Analysis in the Nasal Mucus: Revelation of Differences in Immunological Function in Manis javanica and Manis pentadactyla
by Qing Han, Yepin Yu, Hongbin Sun, Xiujuan Zhang, Ping Liu, Jianfeng Deng, Xinyuan Hu and Jinping Chen
Animals 2024, 14(18), 2683; https://doi.org/10.3390/ani14182683 (registering DOI) - 14 Sep 2024
Viewed by 245
Abstract
All eight pangolin species, especially captive Manis pentadactyla, are critically endangered and susceptible to various pathogenic microorganisms, causing mass mortality. They are involved in the complement system, iron transport system, and inflammatory factors. M. pentadactyla exhibited a higher abundance of opportunistic pathogens, [...] Read more.
All eight pangolin species, especially captive Manis pentadactyla, are critically endangered and susceptible to various pathogenic microorganisms, causing mass mortality. They are involved in the complement system, iron transport system, and inflammatory factors. M. pentadactyla exhibited a higher abundance of opportunistic pathogens, Moraxella, which potentially evaded complement-mediated immune response by reducing C5 levels and counteracting detrimental effects through transferrin neutralization. In addition, we found that the major structure of C5a, an important inflammatory factor, was lacking in M. javanica. In brief, this study revealed the differences in immune factors and microbiome between M. javanica and M. pentadactyla, thus providing a theoretical basis for subsequent immunotherapy. Full article
(This article belongs to the Section Mammals)
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<p>PCA and differentially expressed proteins statistics of the nasal mucus in pangolins. (<b>a</b>, <b>left</b>) PCA of <span class="html-italic">M. javanica</span> and <span class="html-italic">M. pentadactyla</span>; (<b>a</b>, <b>right</b>) PCA of male and female pangolins; the dots named M− mean <span class="html-italic">M. javanica</span>, and the dots named Z− mean <span class="html-italic">M. pentadactyla</span>, the detailed information about samples were provided in <a href="#app1-animals-14-02683" class="html-app">Supplementary Table S1</a>. (<b>b</b>) Numbers of up-/down-regulated proteins in different species and sexuality (based on the expression of proteins in <span class="html-italic">M. pentadactyla</span> and male pangolins).</p>
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<p>GO classification of differentially expressed proteins between <span class="html-italic">M. javanica</span> and <span class="html-italic">M. pentadactyla</span>. (<b>a</b>) Increased enrichment (<span class="html-italic">M. javanica</span> compared with <span class="html-italic">M. pentadactyla</span>); (<b>b</b>) Decreased enrichment (<span class="html-italic">M. javanica</span> compared with <span class="html-italic">M. pentadactyla</span>). The numbers indicated the number of proteins significantly enriched in each term. The statistical significances were achieved using <span class="html-italic">t</span> test, with <span class="html-italic">p</span> &lt; 0.05, Fold change &gt; 1.2.</p>
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<p>KEGG classification of differentially expressed proteins between <span class="html-italic">M. javanica</span> and <span class="html-italic">M. pentadactyla.</span> (<b>a</b>) Increased enrichment (<span class="html-italic">M. javanica</span> compared with <span class="html-italic">M. pentadactyla</span>); (<b>b</b>) Decreased enrichment (<span class="html-italic">M. javanica</span> compared with <span class="html-italic">M. pentadactyla</span>). The numbers indicated the number of proteins significantly enriched in each term. The statistical significances were achieved using <span class="html-italic">t</span> test, with <span class="html-italic">p</span> &lt; 0.05, Fold change &gt; 1.2.</p>
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<p>Venn diagram of OTUs (<b>a</b>) and rarefaction curve (<b>b</b>) of the nasal microbiota in <span class="html-italic">M. javanica</span> and <span class="html-italic">M. pentadactyla</span>.</p>
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<p>Alpha (<b>a</b>) and beta (<b>b</b>) diversity analyses of nasal microbiota between <span class="html-italic">M. javanica</span> and <span class="html-italic">M. pentadactyla</span>.</p>
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<p>Composition and difference analysis of nasal microbiota between <span class="html-italic">M. javanica</span> and <span class="html-italic">M. pentadactyla</span>. (<b>a</b>) Composition of nasal microbiota at the phylum (<b>up</b>) and species (<b>down</b>) levels; (<b>b</b>) LEfSe analysis of bacteria with significantly different abundance in <span class="html-italic">M. javanica</span> and <span class="html-italic">M. pentadactyla</span>; (<b>c</b>) Significantly different bacteria in the nasal mucus between <span class="html-italic">M. javanica</span> and <span class="html-italic">M. pentadactyla</span>. Bars represented the results from four/five pangolins. Significant differences in the abundance of bacteria were indicated with an asterisk (<span class="html-italic">p</span> &lt; 0.05) or two asterisks (<span class="html-italic">p</span> &lt; 0.01).</p>
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<p>KEGG function forecast and difference analysis of nasal microbiota between <span class="html-italic">M. javanica</span> and <span class="html-italic">M. pentadactyla</span>. (<b>a</b>) KEGG function forecast of nasal microbiota at the species level with level two functional hierarchy; (<b>b</b>) Significantly different predicted functions between <span class="html-italic">M. javanica</span> and <span class="html-italic">M. pentadactyla</span>. Bars represented the results from four/five pangolins. Significant differences in functions were indicated with an asterisk (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Proteomics and microbiotas conjoint analysis of nasal mucus in <span class="html-italic">M. javanica</span> and <span class="html-italic">M. pentadactyla</span>. (<b>a</b>) The correlation between differentially expressed proteins and discrepant bacteria. Squares represented proteins of pangolins, circles represented bacteria in the nasal mucosa, and the volumes represented the abundance. The green lines represented positive correlation, red lines represented negative correlation, and the line thickness represented the levels of correlation. Coefficient of correlation, ≥0.6; <span class="html-italic">p</span>-value ≤ 0.05. (<b>b</b>) The correlation between significantly discrepant bacteria and complement proteins in <span class="html-italic">M. javanica</span> (<b>left</b>) and <span class="html-italic">M. pentadactyla</span> (<b>right</b>), respectively. The green boxes represented positive correlation; red boxes represented negative correlation. Significant differences were indicated with an asterisk (<span class="html-italic">p</span> &lt; 0.05) or two asterisks (<span class="html-italic">p</span> &lt; 0.01). (<b>c</b>) The correlation between significantly discrepant bacteria and iron transport proteins in <span class="html-italic">M. javanica</span> (<b>left</b>) and <span class="html-italic">M. pentadactyla</span> (<b>right</b>), respectively. The green boxes represented positive correlation; red boxes represented negative correlation. Significant differences were indicated with an asterisk (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Bioinformatics analysis of complement C5 in <span class="html-italic">M. javanica</span>, <span class="html-italic">M. pentadactyla</span>, and <span class="html-italic">H. sapiens</span>. (<b>a</b>) Serum complement C5 of <span class="html-italic">M. javanica</span> expressed significantly lower versus <span class="html-italic">M. pentadactyla</span> (<span class="html-italic">p</span> &lt; 0.05); (<b>b</b>) Protein sequence alignment of complement C5 (partial) and C5a; (<b>c</b>) Comparison of functional motifs of complement C5. The functional motifs were indicated in the named color boxes. (<b>d</b>) Comparison of 3D models of complement C5. The yellow sections in the box showed C5a in pangolins and humans. The parameters in the 3D model are: (<span class="html-italic">M. javanica</span>) GMQE: 0.74, QMEAN: 0.76; (<span class="html-italic">M. pentadactyla</span>) GMQE: 0.7, QMEAN: 0.71; (<span class="html-italic">H. sapiens</span>) GMQE: 0.69, QMEAN: 0.71.</p>
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<p>Gene amplification and prokaryotic recombinant protein expression of <span class="html-italic">M. javanica</span> and <span class="html-italic">M. pentadactyla</span>. (<b>a</b>) Amplification of C5a full-length from the cDNA of pangolins. M: marker, 1: C5a of <span class="html-italic">M. javanica</span>, C5a of <span class="html-italic">M. pentadactyla</span>; (<b>b</b>) SDS-PAGE analysis of C5a connected with a carrier. M: marker, 1: recombinant protein of C5a from <span class="html-italic">M. javanica</span> without induction; 2: recombinant protein of C5a from <span class="html-italic">M. javanica</span> with induction; 3: recombinant protein of C5a from <span class="html-italic">M. pentadactyla</span> without induction; 4: recombinant protein of C5a from <span class="html-italic">M. pentadactyla</span> with induction. The red arrows represent the C5a recombinant proteins of <span class="html-italic">M. javanica</span> and <span class="html-italic">M. pentadactyla</span>, respectively.</p>
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17 pages, 9416 KiB  
Article
Impact of Mild COVID-19 History on Oral-Gut Microbiota and Serum Metabolomics in Adult Patients with Crohn’s Disease: Potential Beneficial Effects
by Bingjie Xiang, Qi Zhang, Huibo Wu, Jue Lin, Zhaoyuan Xu, Min Zhang, Lixin Zhu, Jun Hu and Min Zhi
Biomedicines 2024, 12(9), 2103; https://doi.org/10.3390/biomedicines12092103 (registering DOI) - 14 Sep 2024
Viewed by 245
Abstract
The impact of coronavirus disease 2019 (COVID-19) history on Crohn’s disease (CD) is unknown. This investigation aimed to examine the effect of COVID-19 history on the disease course, oral-gut microbiota, and serum metabolomics in patients with CD. In this study, oral-gut microbiota and [...] Read more.
The impact of coronavirus disease 2019 (COVID-19) history on Crohn’s disease (CD) is unknown. This investigation aimed to examine the effect of COVID-19 history on the disease course, oral-gut microbiota, and serum metabolomics in patients with CD. In this study, oral-gut microbiota and serum metabolomic profiles in 30 patients with CD and a history of mild COVID-19 (positive group, PG), 30 patients with CD without COVID-19 history (negative group, NG), and 60 healthy controls (HC) were assessed using 16S rDNA sequencing and targeted metabolomics. During follow-up, the CD activity index showed a stronger decrease in the PG than in the NG (p = 0.0496). PG patients demonstrated higher α-diversity and distinct β-diversity clustering in both salivary and fecal microbiota compared to NG and HC individuals. Notably, the gut microbiota composition in the PG patients showed a significantly greater similarity to that of HC than NG individuals. The interaction between oral and intestinal microbiota in the PG was reduced. Moreover, serum metabolome analysis revealed significantly increased anti-inflammatory metabolites, including short-chain fatty acids and N-Acetylserotonin, among PG patients; meanwhile, inflammation-related metabolites such as arachidonic acid were significantly reduced in this group. Our data suggest that the gut microbiota mediates a potential beneficial effect of a mild COVID-19 history in CD patients. Full article
(This article belongs to the Section Microbiology in Human Health and Disease)
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<p>Recovery of clinical activities and microbial community. (<b>A</b>) CDAI changes from the initial enrollment to the 6-month follow-up. (<b>B</b>,<b>C</b>) Differences of α-diversities. (<b>D</b>,<b>E</b>) β-diversities calculated using UniFrac-based unweighted principal coordinate analysis (PCoA). (<b>F</b>) Relative abundance of bacterial phyla. (<b>G</b>) Bray Curtis distance of gut microbiota between HC and NG or PG. CDAI, Crohn’s disease activity index; HC, healthy control; NG, negative group; PG, positive group. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Relative abundance of genera <span class="html-italic">Bifidobacterium</span> (<b>A</b>), <span class="html-italic">Akkermansia</span> (<b>B</b>), <span class="html-italic">Faecalibacterium</span> (<b>C</b>), <span class="html-italic">Klebsiella</span> (<b>D</b>)<span class="html-italic">,</span> and <span class="html-italic">Veillonella</span> (<b>E</b>) in three groups.</p>
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<p>Interaction between oral and gut microbiota. (<b>A</b>) Venn diagram illustrating ASVs of oral and gut microbiota in HC. (<b>B</b>) Spearman’s correlation network between oral and gut microbiota in HC. (<b>C</b>) Venn diagram illustrating ASVs of oral and gut microbiota in NG. (<b>D</b>) Spearman’s correlation network between oral and gut microbiota in NG. (<b>E</b>) Venn diagram illustrating ASVs of oral and gut microbiota in PG. (<b>F</b>) Spearman’s correlation network between oral and gut microbiota in PG. The red circle represents fecal microbiota, and the blue circle represents salivary microbiota. The size of the circles represents the quantity of significant correlation relationships. The red line represents positive correlation. The blue line represents negative correlation. F_, fecal microbiota; S_, salivary microbiota; HC, healthy control; NG, negative group; PG, positive group; ASV, amplicon sequence variants.</p>
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<p>Serum metabolite composition. (<b>A</b>) Relative abundance of each metabolite class in the negative and positive groups; * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01. (<b>B</b>) Significantly different metabolites (n = 43); log2FC &gt; 0 represents an increase in the PG group, while a negative value indicates a decrease. PG, positive group.</p>
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<p>Boxplot of serum arachidonic acid (<b>A</b>), aspartic acid (<b>B</b>), serine (<b>C</b>), pyroglutamic acid (<b>D</b>), N−Acetylserotonin (<b>E</b>), and acetic acid (<b>F</b>) in the NG and PG. NG, negative group; PG, positive group. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Interaction between oral-gut microbiota and serum metabolites. (<b>A</b>) Spearman’s correlation network between gut microbiota and serum metabolites in PG. (<b>B</b>) Spearman’s correlation network between oral microbiota and serum metabolites in PG. The red circle represents fecal or salivary microbiota, and the blue circle represents serum metabolites. The size of the circles represents the number of significant correlation relationships. The red line represents positive correlation, and the blue line represents negative correlation. F_, fecal microbiota; S_, salivary microbiota; PG, positive group.</p>
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17 pages, 3031 KiB  
Article
Functional Muffins Exert Bifidogenic Effects along with Highly Product-Specific Effects on the Human Gut Microbiota Ex Vivo
by Stef Deyaert, Jonas Poppe, Lam Dai Vu, Aurélien Baudot, Sarah Bubeck, Thomas Bayne, Kiran Krishnan, Morgan Giusto, Samuel Moltz and Pieter Van den Abbeele
Metabolites 2024, 14(9), 497; https://doi.org/10.3390/metabo14090497 (registering DOI) - 14 Sep 2024
Viewed by 191
Abstract
GoodBiome™ Foods are functional foods containing a probiotic (Bacillus subtilis HU58™) and prebiotics (mainly inulin). Their effects on the human gut microbiota were assessed using ex vivo SIFR® technology, which has been validated to provide clinically predictive insights. GoodBiome™ Foods (BBM/LCM/OSM) [...] Read more.
GoodBiome™ Foods are functional foods containing a probiotic (Bacillus subtilis HU58™) and prebiotics (mainly inulin). Their effects on the human gut microbiota were assessed using ex vivo SIFR® technology, which has been validated to provide clinically predictive insights. GoodBiome™ Foods (BBM/LCM/OSM) were subjected to oral, gastric, and small intestinal digestion/absorption, after which their impact on the gut microbiome of four adults was assessed (n = 3). All GoodBiome™ Foods boosted health-related SCFA acetate (+13.1/14.1/13.8 mM for BBM/LCM/OSM), propionate (particularly OSM; +7.4/7.5/8.9 mM for BBM/LCM/OSM) and butyrate (particularly BBM; +2.6/2.1/1.4 mM for BBM/LCM/OSM). This is related to the increase in Bifidobacterium species (B. catenulatum, B. adolescentis, B. pseudocatenulatum), Coprococcus catus and Bacteroidetes members (Bacteroides caccae, Phocaeicola dorei, P. massiliensis), likely mediated via inulin. Further, the potent propionogenic potential of OSM related to increased Bacteroidetes members known to ferment oats (s key ingredient of OSM), while the butyrogenic potential of BBM related to a specific increase in Anaerobutyricum hallii, a butyrate producer specialized in the fermentation of erythritol (key ingredient of BBM). In addition, OSM/BBM suppressed the pathogen Clostridioides difficile, potentially due to inclusion of HU58™ in GoodBiome™ Foods. Finally, all products enhanced a spectrum of metabolites well beyond SCFA, including vitamins (B3/B6), essential amino acids, and health-related metabolites such as indole-3-propionic acid. Overall, the addition of specific ingredients to complex foods was shown to specifically modulate the gut microbiome, potentially contributing to health benefits. Noticeably, our findings contradict a recent in vitro study, underscoring the critical role of employing a physiologically relevant digestion/absorption procedure for a more accurate evaluation of the microbiome-modulating potential of complex foods. Full article
(This article belongs to the Special Issue Natural Metabolites on Gut Microbiome Modulation)
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<p><b>Schematic overview of the study design using ex vivo SIFR<sup>®</sup> technology.</b> (<b>a</b>) Reactor design using the ex vivo SIFR<sup>®</sup> technology to evaluate the impact of GoodBiome<sup>TM</sup> Foods against an unsupplemented parallel control (NSC = no substrate control). (<b>b</b>) Timeline and analysis at different timepoints.</p>
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<p><b>The fecal microbiota covered clinically relevant interpersonal differences.</b> Abundances (%) of the key families (top 15), as quantified via shallow shotgun sequencing, in the fecal microbiota of each of the four human adults that provided a fecal donation for the current SIFR<sup>®</sup> study.</p>
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<p><b>GoodBiome™ Foods exerted marked effects on microbial metabolic activity over time.</b> The effects on (<b>A</b>) pH, (<b>B</b>) gas production, (<b>C</b>) total SCFA, (<b>D</b>) acetate, (<b>E</b>) propionate, (<b>F</b>) butyrate, (<b>G</b>) valerate, and (<b>H</b>) bCFA were compared for GoodBiome™ Foods versus an unsupplemented control (NSC) at 6 h, 24 h, 30 h, and 48 h after the initiation of colonic incubation. Data were presented as means across simulations for four individual donors (n = 3 per donor). The statistical significance of the treatment effects for the test products vs. NSC within each timepoint can be found in <a href="#app1-metabolites-14-00497" class="html-app">Figures S2 and S3</a>.</p>
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<p><b>GoodBiome™ Foods exerted significant impact on microbial composition at phylum level.</b> Samples were collected 30 h after the colonic incubations were initiated. Data were expressed as average absolute levels (cells/mL) of each phylum across simulations for four individual donors (n = 3 per donor). The statistical significance of the potential treatment effects within each comparison was determined via Benjamani–Hochberg post hoc testing. Significant changes (<span class="html-italic">p</span><sub>adjusted</sub> &lt; 0.05) were indicated with asterisks.</p>
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<p><b>GoodBiome™ Foods exerted significant impact on microbial composition at species level.</b> The bar charts were generated for species that were significantly (FDR = 0.05) affected by any of the treatments at 30 h, expressed as log2fold change (treatment/NSC), averaged across four human adults (n = 3 per donor). Purple and red bars indicated significant/consistent decreases and increases, respectively. Notable health- or disease-related taxa are highlighted in a gray box.</p>
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<p><b>The GoodBiome™ Foods exerted significant impact on taxa that are potentially relevant for human health.</b> Violin plots, expressed as log2fold change (treatment/NSC), were presented for four individual human adults (n = 3). The data were presented for (<b>A</b>) <span class="html-italic">Clostridiodes difficile</span> (<b>B</b>) <span class="html-italic">Bifidobacteriaceae</span>, (<b>C</b>) <span class="html-italic">Anaerobutyricum hallii</span>, (<b>D</b>) <span class="html-italic">Bacteroidaceae</span>, <span class="html-italic">Bacteroidales_u_f</span>, and <span class="html-italic">Tannerellaceae</span>. For (<b>B</b>–<b>D</b>), Pearson correlation analysis demonstrated significant positive correlations (<span class="html-italic">p</span> &lt; 0.05) between the absolute levels of these taxa (cells/mL) and the concentration (mM) of the most relevant SCFA related to these taxa, i.e., (<b>A</b>) acetate, (<b>B</b>) butyrate, and (<b>C</b>) propionate.</p>
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<p><b>The GoodBiome™ Foods exerted significant impact on the production of microbial metabolites, well beyond SCFA.</b> The bars were generated for metabolites that were significantly (FDR = 0.05) affected by any of the treatments, expressed as log2fold change (treatment/NSC), averaged across four human adults (n = 3 per test subject). Purple and red bars indicated significant decreases and increases, respectively.</p>
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13 pages, 1439 KiB  
Article
Shotgun Analysis of Gut Microbiota with Body Composition and Lipid Characteristics in Crohn’s Disease
by Péter Bacsur, Tamás Resál, Bernadett Farkas, Boldizsár Jójárt, Zoltán Gyuris, Gábor Jaksa, Lajos Pintér, Bertalan Takács, Sára Pál, Attila Gácser, Kata Judit Szántó, Mariann Rutka, Renáta Bor, Anna Fábián, Klaudia Farkas, József Maléth, Zoltán Szepes, Tamás Molnár and Anita Bálint
Biomedicines 2024, 12(9), 2100; https://doi.org/10.3390/biomedicines12092100 (registering DOI) - 14 Sep 2024
Viewed by 180
Abstract
Alterations to intestinal microbiota are assumed to occur in the pathogenesis of inflammatory bowel disease (IBD). This study aims to analyze the association of fecal microbiota composition, body composition, and lipid characteristics in patients with Crohn’s disease (CD). In our cross-sectional study, patients [...] Read more.
Alterations to intestinal microbiota are assumed to occur in the pathogenesis of inflammatory bowel disease (IBD). This study aims to analyze the association of fecal microbiota composition, body composition, and lipid characteristics in patients with Crohn’s disease (CD). In our cross-sectional study, patients with CD were enrolled and blood and fecal samples were collected. Clinical and endoscopic disease activity and body composition were assessed and laboratory tests were made. Fecal bacterial composition was analyzed using the shotgun method. Microbiota alterations based on obesity, lipid parameters, and disease characteristics were analyzed. In this study, 27 patients with CD were analyzed, of which 37.0% were obese based on visceral fat area (VFA). Beta diversities were higher in non-obese patients (p < 0.001), but relative abundances did not differ. C. innocuum had a higher abundance at a high cholesterol level than Bacillota (p = 0.001, p = 0.0034). Adlercreutzia, B. longum, and Blautia alterations were correlated with triglyceride levels. Higher Clostridia (p = 0.009) and B. schinkii (p = 0.032) and lower Lactobacillus (p = 0.035) were connected to high VFA. Disease activity was coupled with dysbiotic elements. Microbiota alterations in obesity highlight the importance of gut microbiota in diseases with a similar inflammatory background and project therapeutic options. Full article
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<p>Bray–Curtis distances between samples in the obese vs. nonobese groups using visceral fat area as a grouping factor. The relative abundances of obese and nonobese patients (based on visceral fat area) did not differ between cohorts. However, non-obese participants had significantly higher distances (****: <span class="html-italic">p</span> &gt; 0.001).</p>
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<p>Principal coordinate analysis of obese and non-obese samples (based on visceral fat area) showed separated dots.</p>
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<p>Higher visceral fat area was associated with increased abundances of class <span class="html-italic">Clostridia</span> (<span class="html-italic">p</span> = 0.009).</p>
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<p>Prior intestinal resection was associated with decreased abundance of <span class="html-italic">Bacteroidales</span> (<span class="html-italic">p</span> = 0.021).</p>
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22 pages, 3939 KiB  
Article
Investigating the Effect of Bacilli and Lactic Acid Bacteria on Water Quality, Growth, Survival, Immune Response, and Intestinal Microbiota of Cultured Litopenaeus vannamei
by Ana Sofía Vega-Carranza, Ruth Escamilla-Montes, Jesús Arturo Fierro-Coronado, Genaro Diarte-Plata, Xianwu Guo, Cipriano García-Gutiérrez and Antonio Luna-González
Animals 2024, 14(18), 2676; https://doi.org/10.3390/ani14182676 (registering DOI) - 14 Sep 2024
Viewed by 207
Abstract
Shrimp is one of the most important aquaculture industries. Therefore, we determined the effect of nitrifying-probiotic bacteria on water quality, growth, survival, immune response, and intestinal microbiota of Litopenaeus vannamei cultured without water exchange. In vitro, only Bacillus licheniformis used total ammonia nitrogen [...] Read more.
Shrimp is one of the most important aquaculture industries. Therefore, we determined the effect of nitrifying-probiotic bacteria on water quality, growth, survival, immune response, and intestinal microbiota of Litopenaeus vannamei cultured without water exchange. In vitro, only Bacillus licheniformis used total ammonia nitrogen (TAN), nitrites, and nitrates since nitrogen bubbles were produced. TAN decreased significantly in the treatments with B. licheniformis and Pediococcus pentosaceus and Leuconostoc mesenteroides, but no differences were observed in nitrites. Nitrates were significantly higher in the treatments with bacteria. The final weight was higher only with bacilli and bacilli and LAB treatments. The survival of shrimp in the bacterial treatments increased significantly, and superoxide anion increased significantly only in lactic acid bacteria (LAB) treatment. The activity of phenoloxidase decreased significantly in the treatments with bacteria compared to the control. Shrimp treated with bacilli in the water showed lower species richness. The gut bacterial community after treatments was significantly different from that of the control. Linoleic acid metabolism was positively correlated with final weight and superoxide anion, whereas quorum sensing was correlated with survival. Thus, bacilli and LAB in the water of hyperintensive culture systems act as heterotrophic nitrifers, modulate the intestinal microbiota and immune response, and improve the growth and survival of shrimp. This is the first report on P. pentosaceus and L. mesenteroides identified as nitrifying bacteria. Full article
(This article belongs to the Special Issue The Application of Probiotics for Sustainable Aquaculture)
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<p>Concentration of TAN, nitrites, and nitrates on Days 7 (<b>A</b>), 15 (<b>B</b>), and 30 (<b>C</b>) in the shrimp culture system without water exchange and treated with bacteria. Treatments: (I) Control without bacteria in the water; (II) bacilli in the water; (III) LAB in the water; (IV) bacilli + LAB in the water. Data are mean ± SD. Different letters indicate significant differences.</p>
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<p>Survival of shrimp cultured without water exchange and treated with bacteria. Treatments: (I) Control without bacteria in the water; (II) bacilli in the water; (III) LAB in the water; (IV) bacilli + LAB in the water. Data are mean ± SD. Different letters indicate significant differences.</p>
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<p>Total hemocyte count in <span class="html-italic">L. vannamei</span> cultured without water exchange and treated with bacteria. Treatments: (I) Control without bacteria in the water; (II) bacilli in the water; (III) LAB in the water; (IV) bacilli + LAB in the water. Data are mean ± SD.</p>
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<p>Superoxide anion in hemocytes of <span class="html-italic">L. vannamei</span> cultured without water exchange and treated with bacteria. Treatments: (I) Control without bacteria in the water; (II) bacilli in the water; (III) LAB in the water; (IV) bacilli + LAB in the water. Data are mean ± SD. Different letters indicate significant differences.</p>
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<p>Phenoloxidase activity (absorbance) in hemolymph of <span class="html-italic">L. vannamei</span> cultured without water exchange and treated with bacteria. Treatments: (I) Control without bacteria in the water; (II) bacilli in the water; (III) LAB in the water; (IV) bacilli + LAB in the water. Data are mean ± SD. Different letters indicate significant differences.</p>
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<p>Venn analysis of the bacterial communities in the shrimp intestine at the OTUs level. Treatments: (I) Control without bacteria in the water; (II) bacilli in the water; (III) LAB in the water; (IV) bacilli + LAB in the water.</p>
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<p>Most abundant bacterial phyla (%) in the shrimp intestine. Treatments: (I) Control without bacteria in the water; (II) bacilli in the water; (III) LAB in the water; (IV) bacilli + LAB in the water. Patescibacteria phylum (* no significant differences [<span class="html-italic">p</span> &gt; 0.05], ** significant differences [<span class="html-italic">p</span> &lt; 0.05]). The analysis was done with Shaman.</p>
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<p>Most abundant bacterial genera (%) in the shrimp intestines. Treatments: (I) Control without bacteria in the water; (II) bacilli in the water; (III) LAB in the water; (IV) bacilli + LAB in the water. Different letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05). The analysis was done with Shaman.</p>
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<p>Beta diversity of intestinal microbiota of <span class="html-italic">L. vannamei</span> at the genus level using non-metric multidimensional scaling based on Jaccard distances in MicrobiomeAnalyst. Treatments: (I) Control without bacteria in the water; (II) bacilli in the water; (III) LAB in the water; (IV) bacilli + LAB in the water. ANOSIM test, <span class="html-italic">p</span> &lt; 0.008.</p>
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<p>Correlation among linoleic acid metabolism of intestinal bacteria of <span class="html-italic">L. vannamei</span> and immune and productive variables. Spearman correlation analysis.</p>
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<p>Correlation among quorum sensing of intestinal bacteria of <span class="html-italic">L. vannamei</span> and immune and productive variables. Spearman correlation analysis.</p>
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14 pages, 2997 KiB  
Article
Lactic Acid Bacterial Fermentation of Esterified Agave Fructans in Simulated Physicochemical Colon Conditions for Local Delivery of Encapsulated Drugs
by Carmen Miramontes-Corona, Abraham Cetina-Corona, María Esther Macías-Rodríguez, Alfredo Escalante, Rosa Isela Corona-González and Guillermo Toriz
Fermentation 2024, 10(9), 478; https://doi.org/10.3390/fermentation10090478 (registering DOI) - 14 Sep 2024
Viewed by 179
Abstract
Understanding drug release in the colon is fundamental to developing efficient treatments for colon-related diseases, while unraveling the relationship between the colonic microbiota and excipients is crucial to unveiling the effect of biomaterials on the release of drugs. In this contribution, the bio-release [...] Read more.
Understanding drug release in the colon is fundamental to developing efficient treatments for colon-related diseases, while unraveling the relationship between the colonic microbiota and excipients is crucial to unveiling the effect of biomaterials on the release of drugs. In this contribution, the bio-release of ibuprofen (encapsulated in acetylated and palmitoylated agave fructans) was evaluated by fermentation with lactic acid bacteria in simulated physicochemical (pH and temperature) colon conditions. It was observed that the size of the acyl chain (1 in acetyl and 15 in palmitoyl) was critical both in the growth of the microorganisms and in the release of the drug. For example, both the bacterial growth and the release of ibuprofen were more favored with acetylated fructan microspheres. Among the microorganisms evaluated, Bifidobacterium adolescentis and Lactobacillus brevis showed great potential as probiotics useful to release drugs from modified fructans. The production of short-chain fatty acids (lactic, acetic, and propionic acids) in the course of fermentations was also determined, since such molecules have a positive effect both on colon-related diseases and on the regulation of the intestinal microbiota. It was found that a higher concentration of acetate is related to a lower growth of bacteria and less release of ibuprofen. Full article
(This article belongs to the Special Issue Fermentation: 10th Anniversary)
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<p>(<b>A</b>) A model of agave fructan according to [<a href="#B33-fermentation-10-00478" class="html-bibr">33</a>]; in native fructan, R is OH; in palmitoylated fructan, 4 Rs are substituted with palmitoyl moieties; for acetylated fructan, about 62 Rs should be acetyl groups. (<b>B</b>) <sup>1</sup>H NMR spectra: (<b>a</b>) native fructan analyzed in D<sub>2</sub>O; H-C* denotes the proton at the anomeric carbon in glucose (<b>b</b>) palmitoylated fructan obtained in CDCl<sub>3</sub>; (<b>c</b>) acetylated fructans analyzed in <sup>d6</sup>DMSO.</p>
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<p>Scanning Electron Micrographs of (<b>a</b>) acetylated (7920X) and (<b>b</b>) palmitoylated fructan (1750X).</p>
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<p>Growth profile of strains and their consortium obtained by optical density of (<b>a</b>) acetylated fructan microspheres and (<b>b</b>) palmitoylated fructan microspheres: <span class="html-italic">B. adolescentis</span> (●); <span class="html-italic">Weisella paramesenteroides</span> (◆); <span class="html-italic">Enterococcus mundtii</span> (◼); <span class="html-italic">Lactobacillus brevis</span> (▲); <span class="html-italic">consortium</span> (∗).</p>
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<p>Percentage of ibuprofen release as function of fermentation time from (<b>a</b>) acetylated and (<b>b</b>) palmitoylated fructan microspheres with <span class="html-italic">B. adolescentis</span> (●); <span class="html-italic">Weisella paramesenteroides</span> (◆); <span class="html-italic">Enterococcus mundtii</span> (◼); <span class="html-italic">Lactobacillus brevis</span> (▲); and <span class="html-italic">consortium</span> (∗).</p>
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<p>Production of SCFA (g/L) by lactic acid bacteria at 48 h of fermentation: (<b>a</b>) acetylated fructan microspheres and (<b>b</b>) palmitoylated fructan microspheres, (■) lactic acid, (□) acetic acid, and (<span class="html-fig-inline" id="fermentation-10-00478-i001"><img alt="Fermentation 10 00478 i001" src="/fermentation/fermentation-10-00478/article_deploy/html/images/fermentation-10-00478-i001.png"/></span>) propionic acid. BA: <span class="html-italic">B. adolescentis</span>; WP: <span class="html-italic">Weisella paramesenteroides</span>; EL: <span class="html-italic">Enterococcus mundtii</span>; LB: <span class="html-italic">Lactobacillus brevis</span>; C: <span class="html-italic">consortium</span>.</p>
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<p>Phylogenetic tree for identification of <span class="html-italic">Weissella paramesenteroides</span> (Jal1), <span class="html-italic">Enterococcus mundtii</span> (BT5inv), and <span class="html-italic">Lactobacillus brevis</span> (Col18) isolated from carposphere of tomato. The red boxes indicate the identified strains in the phylogenetic tree.</p>
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21 pages, 5832 KiB  
Article
Effects of Dietary Inclusion of Saccharina latissima and Ulva lactuca on Growth Performance and Gut Health in Growing Rabbits
by Sabela Al-Soufi, Ana Paula Losada, Marta López-Alonso, Alejandra Cardelle-Cobas, Azucena Mora, Alexandre Lamas, Rosario Panadero, Marta Miranda, Antonio Muíños, Eugenio Cegarra and Javier García
Agriculture 2024, 14(9), 1605; https://doi.org/10.3390/agriculture14091605 (registering DOI) - 14 Sep 2024
Viewed by 245
Abstract
Rabbit meat production faces challenges due to the prevalence of gastrointestinal diseases in rabbits, exacerbated by restrictions on antibiotic use in European animal production. Marine macroalgae, rich in bioactive compounds such as soluble polysaccharides, represent promising solutions to this problem. However, research on [...] Read more.
Rabbit meat production faces challenges due to the prevalence of gastrointestinal diseases in rabbits, exacerbated by restrictions on antibiotic use in European animal production. Marine macroalgae, rich in bioactive compounds such as soluble polysaccharides, represent promising solutions to this problem. However, research on the effects of macroalgae and the underlying mechanisms in rabbits is limited, especially in commercial settings. This study aimed to evaluate the impact of Saccharina latissima (dehydrated) and Ulva lactuca (dehydrated and hydrolyzed extract) on rabbit on growth performance and gut health in a commercial farm context. A total of 96 litters (8 rabbits/litter) of crossbred rabbits weaned at 33 days of age were randomly assigned to 4 experimental groups (control, Saccharina latissima dehydrated, Ulva lactuca dehydrated and Ulva lactuca hydrolyzed extract; 24 replicates/treatment) and monitored from weaning to slaughter at 61 days of age. The key indicators of gut health were assessed 14 days post-weaning by counting coccidia, isolating specific microflora and examining histological samples. Additionally, the relevant intestinal markers (microbiome composition, mucin content and gene expression related to immune response and tight junction proteins) were determined in order to elucidate the potential mechanisms involved. The inclusion of macroalgae in the diet did not influence growth performance of the animals. S. latissima had a positive effect in reducing coccidia counts (p = 0.10) and improving mucosal morphology (p < 0.001), which can possibly be attributed to modulation of the microbiota and improved mucosal functionality. Ulva lactuca had a favorable effect on gut tight junction proteins (p < 0.001), enhancing intestinal barrier function. These findings suggest the potential of macroalgae to modify the intestinal microbiome by reducing the presence of inflammatory bacteria. Further research is warranted to elucidate the mechanisms involved and optimize macroalgae supplementation in rabbit nutrition for enhanced gut health. Full article
(This article belongs to the Section Farm Animal Production)
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<p>Sections of ileal mucosa of rabbits at 47 d of age, stained with hematoxylin-eosin (<b>A</b>) and periodic acid–Schiff, PAS (<b>B</b>). (<b>A</b>) The epithelium of the villi has a scalloped appearance. The apical edge is eroded, and remnants of partially digested cellular and food debris appear in the lumen. The epithelial lining shows multiple developmental stages of coccidia (arrows), which are intensely marked by PAS staining ((<b>B</b>), arrows). Bars: 50 µm (<b>A</b>) and 100 µm (<b>B</b>).</p>
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<p>Mucosa of the ileocecal valve of rabbits at age 47 days. Numerous coccobacilli are observed adhering to the epithelial surface of the enterocytes, both at the apical portion of the villi and in deeper regions (arrow 1). In some areas, the epithelial cells remain intact; however, in others, the enterocyte exhibit a cuboidal, swollen or deformed appearance (arrow 2), resulting in the epithelial border having a scalloped appearance (<b>A</b>). In these regions, the lamina propria exhibits a mixed population of heterophils, lymphocytes and plasma cells ((<b>B</b>), arrow 3). Bars: 50 µm (<b>A</b>,<b>B</b>).</p>
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<p>Significant differences in colon microbiota between experimental groups of rabbits at 47 days of age. (<b>A</b>) Phylum Cyanobacteria. (<b>B</b>) Phylum TM7. (<b>C</b>) Family YS2. (<b>D</b>) Family TM7-F16. (<b>E</b>) Family Cryomorphaceae. (<b>F</b>) Genus <span class="html-italic">Fluviicula</span>. SL: dehydrated <span class="html-italic">S. latissima</span>. U: dehydrated <span class="html-italic">Ulva</span> spp. EU: hydrolyzed aqueous extract of <span class="html-italic">Ulva lactuca</span>.</p>
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<p>Gene expression in cecal appendix mucosa of rabbits at 47 days of age, of interleukins (IL-6, IL-10) and TNF-α in experimental diets. SL: dehydrated <span class="html-italic">S. latissima</span><b>.</b> U: dehydrated <span class="html-italic">Ulva</span> spp. EU: extract of <span class="html-italic">Ulva lactuca</span>.</p>
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<p>Expression in cecal appendix mucosa of tight junction proteins in relation to experimental diets. SL: dehydrated <span class="html-italic">S. latissima.</span> U: dehydrated <span class="html-italic">Ulva</span> spp. EU: hydrolyzed aqueous extract of <span class="html-italic">Ulva lactuca.</span> Different letters indicate statistically significant differences.</p>
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31 pages, 3407 KiB  
Review
Glucose Metabolism-Modifying Natural Materials for Potential Feed Additive Development
by Wei-Chih Lin, Boon-Chin Hoe, Xianming Li, Daizheng Lian and Xiaowei Zeng
Pharmaceutics 2024, 16(9), 1208; https://doi.org/10.3390/pharmaceutics16091208 - 13 Sep 2024
Viewed by 269
Abstract
Glucose, a primary energy source derived from animals’ feed ration, is crucial for their growth, production performance, and health. However, challenges such as metabolic stress, oxidative stress, inflammation, and gut microbiota disruption during animal production practices can potentially impair animal glucose metabolism pathways. [...] Read more.
Glucose, a primary energy source derived from animals’ feed ration, is crucial for their growth, production performance, and health. However, challenges such as metabolic stress, oxidative stress, inflammation, and gut microbiota disruption during animal production practices can potentially impair animal glucose metabolism pathways. Phytochemicals, probiotics, prebiotics, and trace minerals are known to change the molecular pathway of insulin-dependent glucose metabolism and improve glucose uptake in rodent and cell models. These compounds, commonly used as animal feed additives, have been well studied for their ability to promote various aspects of growth and health. However, their specific effects on glucose uptake modulation have not been thoroughly explored. This article focuses on glucose metabolism is on discovering alternative non-pharmacological treatments for diabetes in humans, which could have significant implications for developing feed additives that enhance animal performance by promoting insulin-dependent glucose metabolism. This article also aims to provide information about natural materials that impact glucose uptake and to explore their potential use as non-antibiotic feed additives to promote animal health and production. Further exploration of this topic and the materials involved could provide a basis for new product development and innovation in animal nutrition. Full article
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<p>Pathway of insulin-dependent glucose uptake and AMPK activation-induced translocation of GLUT4 vesicles. Pointed arrows represent activation or translocation in the signaling pathways. The “P” symbol with a circle represents a phosphorylation event. After insulin binds to the IR and triggers tyrosine autophosphorylation, the IRSs are activated to promote the PI3K/AKT pathway, eventually inducing the translocation of GLUT4 vesicles to the cell membranes and facilitating glucose uptake.</p>
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<p>Putative mechanisms by which ROS interferes with insulin-dependent glucose uptake. Pointed arrows represent activation or translocation in the signaling pathways. Line-headed arrows indicate inhibition of the signaling process. The “P” symbol with a circle represents a phosphorylation event. ROS directly inhibits the expression of PI3K/AKB and the translocation of GLUT4. ROS also causes protein misfolding, which can directly inflict IR. The cellular damage can induce the production of TNF-α, which inhibits the phosphorylation of IR. ER stress caused by ROS facilitates the ubiquitination of IRS via the JNK pathway, inhibiting the downstream signaling.</p>
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<p>Potential crosstalk between oxidative stress and inflammation through phytochemicals in poultry [<a href="#B75-pharmaceutics-16-01208" class="html-bibr">75</a>]. Copyright@2019, Animal Bioscience, Seoul, Republic of Korea.</p>
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<p>Mechanisms of phytochemicals to modulate glucose metabolism. Upward arrows (↑) and downward arrows (↓) represent the upregulation and downregulation of specific molecules, respectively. The antioxidant and anti-inflammatory effects of phytochemicals prevent the potential inhibition of insulin-dependent metabolism by oxidative stresses. Also, phytochemicals can directly stimulate the expressions of IRSs, PI3K/AKT, and AMPK, eventually facilitating GLUT4 translocation.</p>
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<p>Mechanisms of probiotics and prebiotics on the modulation of insulin-dependent glucose metabolism. Upward arrows (↑) and downward arrows (↓) represent the upregulation and downregulation of specific molecules, respectively. Probiotics and prebiotics can work independently or synergistically to modulate the intestinal microbiome and facilitate the production of SCFAs. The SCFAs then stimulate the GPR 41 and 43, promote pro-insulin GLP-1, and enhance host insulin secretion. Individually, prebiotics can directly promote the IRS/PI3K/AKT pathway and stimulate GLUT4 translocation. On the other hand, probiotics directly stimulate PI3K/AKT/GLUT4 with their metabolites, such as surfactin. Furthermore, some probiotics, after successful colonization in the intestinal environment, can exert their benefits through SCFA production.</p>
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<p>Mechanisms of Se and Cr<sup>3+</sup> on the modulation of insulin-dependent glucose metabolism. These two trace elements can reduce cellular ER stress via their antioxidant effects. Upward arrows (↑) and downward arrows (↓) represent the upregulation and downregulation of specific molecules, respectively. The reduced ER stress prevents IRSs from ubiquitination, which preserves the downstream signaling cascade of insulin-dependent glucose uptake. Se and Cr<sup>3+</sup> have been reported to promote the activation of serine and threonine kinases with subsequent IRS phosphorylation. Se and Cr<sup>3+</sup> have the ability to respectively stimulate GLP-1 for increased insulin production or activate AMPK-facilitated GLUT4 translocation.</p>
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<p>The importance of gut microbial metabolism in regulating insulin sensitivity in humans and mice [<a href="#B210-pharmaceutics-16-01208" class="html-bibr">210</a>]. Upward arrows (↑) and downward arrows (↓) represent the upregulation and downregulation of specific molecules, respectively. Question mark (?) indicates the hypothesized modulation effects. Copyright@2024, Nature, London, UK.</p>
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<p>Flow chart showing how functional non-antibiotic feed additives support animal health and preserve good product quality.</p>
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10 pages, 1300 KiB  
Article
Moringa Reduces Glucose Levels and Alters Wolbachia Abundance in Drosophila melanogaster
by Michaela Schaffer, D’Andre Grant, Katherine Berge and Nana Yaw Darko Ankrah
Microbiol. Res. 2024, 15(3), 1870-1879; https://doi.org/10.3390/microbiolres15030125 - 13 Sep 2024
Viewed by 268
Abstract
Moringa oleifera Lam. (moringa) is a plant native to India, used as a nutritional and medicinal supplement in many cultures around the world. Moringa has been linked to maintaining metabolic homeostasis and is often marketed as a weight loss supplement and a potential [...] Read more.
Moringa oleifera Lam. (moringa) is a plant native to India, used as a nutritional and medicinal supplement in many cultures around the world. Moringa has been linked to maintaining metabolic homeostasis and is often marketed as a weight loss supplement and a potential remedy for diseases such as diabetes. Here, we investigate how moringa, a ‘superfood’ with predicted protective effects against chronic diseases such as diabetes, influences the nutritional physiology and microbiome composition of the fruit fly Drosophila melanogaster. We administered moringa as a dietary supplement to Drosophila, and quantified key nutritional indices: glucose, triacylglyceride, and protein levels, and fly weight. We showed that dietary moringa supplementation significantly reduced fly glucose levels by up to ~30% and resulted in substantial restructuring of Drosophila microbiota composition, altering both gut and intracellular bacterial populations. The effect of moringa on fly glucose levels is specific because other nutritional indices, namely, triacylglyceride and protein levels and fly weight, were not significantly affected by dietary moringa supplementation. This study highlights the importance of moringa as a modulator of host glucose metabolism. Full article
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<p>Impact of moringa diet supplementation on the composition and diversity of the bacterial community of <span class="html-italic">D. melanogaster</span>. (<b>A</b>) Relative abundance of bacterial orders present in <span class="html-italic">D. melanogaster</span> feeding on moringa versus the control diet. (<b>B</b>) Alpha diversity measurements for different treatment groups. Significantly different (<span class="html-italic">p</span> &lt; 0.05) samples by Tukey’s HSD post hoc test are indicated by different letters. For each boxplot, the center line displays the median, and the lower and upper hinges correspond to the 25th and 75th percentiles. Moringa and control diets are represented by green and brown boxes, respectively.</p>
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<p>Response of gut and intracellular microbial communities to moringa supplementation. (<b>A</b>) Change to <span class="html-italic">Acetobacter</span> levels on a moringa diet. (<b>B</b>) Change to <span class="html-italic">Wolbachia</span> levels on a moringa diet. (<b>C</b>) Viable bacteria count on MRS agar plates. Significantly different (<span class="html-italic">p</span> &lt; 0.05) samples by Tukey’s HSD post hoc test are indicated by different letters. For each boxplot, the center line displays the median, and the lower and upper hinges correspond to the 25th and 75th percentiles. Moringa and control diets are represented by green and brown boxes respectively.</p>
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<p>Effect of dietary supplementation with moringa on <span class="html-italic">Drosophila</span> nutritional indices (<b>A</b>–<b>C</b>) and weight (<b>D</b>). Significantly different (<span class="html-italic">p</span> &lt; 0.05) samples by Tukey’s HSD post hoc test are indicated by different letters. For each boxplot, the center line displays the median, and the lower and upper hinges correspond to the 25th and 75th percentiles. Moringa and control diets are represented by green and brown boxes, respectively.</p>
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