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18 pages, 369 KiB  
Review
The Oral Microbiota, Microbial Metabolites, and Immuno-Inflammatory Mechanisms in Cardiovascular Disease
by Zheng Wang, Robert C. Kaplan, Robert D. Burk and Qibin Qi
Int. J. Mol. Sci. 2024, 25(22), 12337; https://doi.org/10.3390/ijms252212337 - 17 Nov 2024
Viewed by 428
Abstract
Cardiovascular diseases (CVDs) remain a leading cause of global morbidity and mortality. Recent advancements in high-throughput omics techniques have enhanced our understanding of the human microbiome’s role in the development of CVDs. Although the relationship between the gut microbiome and CVDs has attracted [...] Read more.
Cardiovascular diseases (CVDs) remain a leading cause of global morbidity and mortality. Recent advancements in high-throughput omics techniques have enhanced our understanding of the human microbiome’s role in the development of CVDs. Although the relationship between the gut microbiome and CVDs has attracted considerable research attention and has been rapidly evolving in recent years, the role of the oral microbiome remains less understood, with most prior studies focusing on periodontitis-related pathogens. In this review, we summarized previously reported associations between the oral microbiome and CVD, highlighting known CVD-associated taxa such as Porphyromonas gingivalis, Fusobacterium nucleatum, and Aggregatibacter actinomycetemcomitans. We also discussed the interactions between the oral and gut microbes. The potential mechanisms by which the oral microbiota can influence CVD development include oral and systemic inflammation, immune responses, cytokine release, translocation of oral bacteria into the bloodstream, and the impact of microbial-related products such as microbial metabolites (e.g., short-chain fatty acids [SCFAs], trimethylamine oxide [TMAO], hydrogen sulfide [H2S], nitric oxide [NO]) and specific toxins (e.g., lipopolysaccharide [LPS], leukotoxin [LtxA]). The processes driven by these mechanisms may contribute to atherosclerosis, endothelial dysfunction, and other cardiovascular pathologies. Integrated multi-omics methodologies, along with large-scale longitudinal population studies and intervention studies, will facilitate a deeper understanding of the metabolic and functional roles of the oral microbiome in cardiovascular health. This fundamental knowledge will support the development of targeted interventions and effective therapies to prevent or reduce the progression from cardiovascular risk to clinical CVD events. Full article
(This article belongs to the Special Issue Microbial Omics)
15 pages, 1284 KiB  
Review
Exploring the Interconnection between Metabolic Dysfunction and Gut Microbiome Dysbiosis in Osteoarthritis: A Narrative Review
by Hui Li, Jihan Wang, Linjie Hao and Guilin Huang
Biomedicines 2024, 12(10), 2182; https://doi.org/10.3390/biomedicines12102182 - 25 Sep 2024
Viewed by 1039
Abstract
Osteoarthritis (OA) is a prevalent joint disorder and the most common form of arthritis, affecting approximately 500 million people worldwide, or about 7% of the global population. Its pathogenesis involves a complex interplay between metabolic dysfunction and gut microbiome (GM) alterations. This review [...] Read more.
Osteoarthritis (OA) is a prevalent joint disorder and the most common form of arthritis, affecting approximately 500 million people worldwide, or about 7% of the global population. Its pathogenesis involves a complex interplay between metabolic dysfunction and gut microbiome (GM) alterations. This review explores the relationship between metabolic disorders—such as obesity, diabetes, and dyslipidemia—and OA, highlighting their shared risk factors, including aging, sedentary lifestyle, and dietary habits. We further explore the role of GM dysbiosis in OA, elucidating how systemic inflammation, oxidative stress, and immune dysregulation driven by metabolic dysfunction and altered microbial metabolites contribute to OA progression. Additionally, the concept of “leaky gut syndrome” is discussed, illustrating how compromised gut barrier function exacerbates systemic and local joint inflammation. Therapeutic strategies targeting metabolic dysfunction and GM composition, including lifestyle interventions, pharmacological and non-pharmacological factors, and microbiota-targeted therapies, are reviewed for their potential to mitigate OA progression. Future research directions emphasize the importance of identifying novel biomarkers for OA risk and treatment response, adopting personalized treatment approaches, and integrating multiomics data to enhance our understanding of the metabolic–GM–OA connection and advance precision medicine in OA management. Full article
(This article belongs to the Special Issue Molecular Research on Osteoarthritis and Osteoporosis)
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<p>Interconnections between metabolic dysfunction and osteoarthritis (OA).</p>
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<p>Illustration of the mechanisms underlying the influence of gut dysbiosis on OA pathophysiology.</p>
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23 pages, 3866 KiB  
Article
Vertical Transfer of Maternal Gut Microbes to Offspring of Western Diet-Fed Dams Drives Reduced Levels of Tryptophan Metabolites and Postnatal Innate Immune Response
by Kameron Y. Sugino, Rachel C. Janssen, Rachel H. McMahan, Chelsea Zimmerman, Jacob E. Friedman and Karen R. Jonscher
Nutrients 2024, 16(12), 1808; https://doi.org/10.3390/nu16121808 - 8 Jun 2024
Cited by 1 | Viewed by 1439
Abstract
Maternal obesity and/or Western diet (WD) is associated with an increased risk of metabolic dysfunction-associated steatotic liver disease (MASLD) in offspring, driven, in part, by the dysregulation of the early life microbiome. Here, using a mouse model of WD-induced maternal obesity, we demonstrate [...] Read more.
Maternal obesity and/or Western diet (WD) is associated with an increased risk of metabolic dysfunction-associated steatotic liver disease (MASLD) in offspring, driven, in part, by the dysregulation of the early life microbiome. Here, using a mouse model of WD-induced maternal obesity, we demonstrate that exposure to a disordered microbiome from WD-fed dams suppressed circulating levels of endogenous ligands of the aryl hydrocarbon receptor (AHR; indole, indole-3-acetate) and TMAO (a product of AHR-mediated transcription), as well as hepatic expression of Il10 (an AHR target), in offspring at 3 weeks of age. This signature was recapitulated by fecal microbial transfer from WD-fed pregnant dams to chow-fed germ-free (GF) lactating dams following parturition and was associated with a reduced abundance of Lactobacillus in GF offspring. Further, the expression of Il10 was downregulated in liver myeloid cells and in LPS-stimulated bone marrow-derived macrophages (BMDM) in adult offspring, suggestive of a hypo-responsive, or tolerant, innate immune response. BMDMs from adult mice lacking AHR in macrophages exhibited a similar tolerogenic response, including diminished expression of Il10. Overall, our study shows that exposure to maternal WD alters microbial metabolites in the offspring that affect AHR signaling, potentially contributing to innate immune hypo-responsiveness and progression of MASLD, highlighting the impact of early life gut dysbiosis on offspring metabolism. Further investigations are warranted to elucidate the complex interplay between maternal diet, gut microbial function, and the development of neonatal innate immune tolerance and potential therapeutic interventions targeting these pathways. Full article
(This article belongs to the Special Issue Nutrition and Immunity in Early Childhood)
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<p>Maternal WD increased hepatic macrophage infiltration, fibrosis genes, and macrophage immune tolerance in WD-challenged adult offspring. (<b>A</b>) Male offspring from CH- or WD-fed dams were weaned to the CH diet and then spent 9 weeks on the CH diet prior to a 4-week WD challenge (Study One/adults). (<b>B</b>) qPCR of inflammatory gene expression in liver tissue. (<b>C</b>) qPCR of fibrosis gene expression in liver tissue. The scale of <span class="html-italic">Timp1</span> results was reduced by a factor of six. (<b>D</b>) Representative plots showing flow cytometric gating of MerTK<sup>+</sup>/CD45<sup>+</sup> liver macrophage populations. Infiltrating cells (circled) are F4/80<sup>Lo</sup>/CD11b<sup>Hi</sup>. (<b>E</b>) Infiltrating macrophage population as a percentage of total macrophages. (<b>F</b>) Ly6C<sup>Hi</sup> macrophage as a percentage of infiltrating macrophage. (<b>G</b>) qPCR in BMDMs stimulated with 100 ng/mL LPS for 4 h or unstimulated (US) for <span class="html-italic">Il1b</span>, <span class="html-italic">Tnf</span>, and <span class="html-italic">Il10</span> expression. (<b>H</b>) qPCR of inflammatory gene expression in purified MerTK<sup>+</sup> liver macrophages. qPCR expression normalized to <span class="html-italic">18S</span> rRNA. Data are mean ± SEM. <span class="html-italic">n</span> = 3–4/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, <sup>#</sup> <span class="html-italic">p</span> &lt; 0.09 and &gt;0.05 by Student’s <span class="html-italic">t</span> test.</p>
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<p>Tryptophan-derived AHR ligands and carnitine metabolites are altered in conventionally raised weanlings exposed to maternal WD. (<b>A</b>) Model for conventionally raised postnatal day 21 (PND21) offspring from dams fed either CH or WD during gestation and lactation (Study One/weanlings). (<b>B</b>) Untargeted metabolomics PLS-DA with shaded circles showing 95% confidence interval., (<b>C</b>) variable importance in projection (VIP), and (<b>D</b>) heat map in conventional offspring. (<b>E</b>) Relative abundance of indole, indole-3-acetate (I3A), 2-octenoylcarnitine (CAR 8:1), and trimethylamine N-oxide (TMAO) in conventional weanlings. Western blot analysis of (<b>F</b>) AHR and (<b>G</b>) its downstream target CYP1A1, normalized to total protein (shown in <a href="#app1-nutrients-16-01808" class="html-app">Figure S2B</a>), using the Simple Western system. Data are mean ± SEM. <span class="html-italic">n</span> = 4 wCH-O, <span class="html-italic">n</span> = 4–6 wWD-O. * <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 by Student’s <span class="html-italic">t</span> test.</p>
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<p>Fecal transfer of maternal microbes reveals a maternal diet-mediated signature of circulating metabolites in the offspring of germ-free recipients. (<b>A</b>) Germ-free (GF) lactating dams (<span class="html-italic">n</span> = 2/group) were inoculated at postnatal day 4 (PND4) with microbes from conventional CH- or WD-fed dams at ~E16 via fecal microbiota transfer (FMT). Due to an abnormally small litter size (<span class="html-italic">n</span> = 2), one cage of CH pups was excluded from the data analysis. UHPLC-MS was used to measure metabolite abundance in serum from 3-week-old offspring from GF dams inoculated with CH-exposed or WD-exposed microbes (Study Two). (<b>B</b>) PLS-DA plot of GF offspring. Blue and green dots within the green oval denote two separate litters of GF offspring from WD-exposed microbes. (<b>C</b>) Heat map of top 25 metabolites from GF-CH-O and GF-WD-O groups with male and female offspring and the two GF-WD-O litters (205 and 904) noted. Large circles show 95% confidence interval. (<b>D</b>) Variable importance in projection (VIP) scores of metabolites driving the separation between diet groups. (<b>E</b>) Abundance of indole, indole-3-acetate (I3A), 2-octenoylcarnitine (CAR 8:1), and trimethylamine N-oxide (TMAO) in GF offspring. Representative Western blots of (<b>F</b>) AHR and (<b>G</b>) its downstream target CYP1A1 and their quantitation, normalized to total protein (shown in <a href="#app1-nutrients-16-01808" class="html-app">Figure S2D</a>), using the Simple Western system. Data are mean ± SEM, <span class="html-italic">n</span> = 8 GF-CH-O, <span class="html-italic">n</span> = 15–16 GF-WD-O. * <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001 by Student’s <span class="html-italic">t</span> test.</p>
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<p>Heatmap of significant LASSO associations between metabolites and microbes in GF offspring. Heatmap clustered by function and separated into two panels. Family.genus listed except for Clostridales.unclassified.unclassified shows Order.family.genus. Arrow indicates <span class="html-italic">Lactobacillus</span> association. <span class="html-italic">p</span> values are adjusted following FDR correction. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Maternal WD dampens the immune response in BMDMs in an AHR-dependent manner. (<b>A</b>) F1 heterozygous females AHR<sup>fl/−</sup> x LysMCre fed either CH or WD were crossed with CH-fed male AHR<sup>fl/fl</sup> mice to obtain F2 offspring: AHR WT (AHR<sup>fl/fl</sup> x LysMCre<sup>−</sup>) and AHR KD (AHR<sup>fl/fl</sup> x LysMCre<sup>+</sup> in which Cre-mediated recombination deleted AHR specifically in myeloid cells). Offspring were weaned to the CH diet and, at 12 weeks of age, either remained on the CH diet (unchallenged) or were challenged with 4 weeks of WD (Study Three). BMDMs were differentiated from unchallenged and WD-challenged offspring exposed to maternal CH or WD with wild-type AHR (AHR WT) or with macrophage knockdown of AHR (AHR KD). BMDMs were stimulated with 100 ng/mL LPS for 4 h. qPCR was used to measure mRNA expression of (<b>B</b>) <span class="html-italic">Cyp1b1</span>, (<b>C</b>) <span class="html-italic">Il10</span>, and (<b>D</b>) <span class="html-italic">Il6</span>. qPCR data were normalized to <span class="html-italic">Rn18s</span>, and each group was normalized to their respective no-treatment controls. Data are mean ± SEM. <span class="html-italic">n</span> = 4/group. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &gt; 0.01, **** <span class="html-italic">p</span> &gt; 0.0001, <sup>#</sup> <span class="html-italic">p</span> &lt; 0.09 and &gt;0.05 by Student’s <span class="html-italic">t</span> test. † <span class="html-italic">p</span> &gt; 0.01 versus AHR WT CH-CH (first bar) by Student’s <span class="html-italic">t</span> test.</p>
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23 pages, 1957 KiB  
Review
The Gut Connection: Exploring the Possibility of Implementing Gut Microbial Metabolites in Lymphoma Treatment
by Ahmad K. Al-Khazaleh, Dennis Chang, Gerald W. Münch and Deep Jyoti Bhuyan
Cancers 2024, 16(8), 1464; https://doi.org/10.3390/cancers16081464 - 11 Apr 2024
Cited by 3 | Viewed by 2110
Abstract
Recent research has implicated the gut microbiota in the development of lymphoma. Dysbiosis of the gut microbial community can disrupt the production of gut microbial metabolites, thereby impacting host physiology and potentially contributing to lymphoma. Dysbiosis-driven release of gut microbial metabolites such as [...] Read more.
Recent research has implicated the gut microbiota in the development of lymphoma. Dysbiosis of the gut microbial community can disrupt the production of gut microbial metabolites, thereby impacting host physiology and potentially contributing to lymphoma. Dysbiosis-driven release of gut microbial metabolites such as lipopolysaccharides can promote chronic inflammation, potentially elevating the risk of lymphoma. In contrast, gut microbial metabolites, such as short-chain fatty acids, have shown promise in preclinical studies by promoting regulatory T-cell function, suppressing inflammation, and potentially preventing lymphoma. Another metabolite, urolithin A, exhibited immunomodulatory and antiproliferative properties against lymphoma cell lines in vitro. While research on the role of gut microbial metabolites in lymphoma is limited, this article emphasizes the need to comprehend their significance, including therapeutic applications, molecular mechanisms of action, and interactions with standard chemotherapies. The article also suggests promising directions for future research in this emerging field of connection between lymphoma and gut microbiome. Full article
(This article belongs to the Section Cancer Drug Development)
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<p>Key differences between Hodgkin’s lymphoma and non-Hodgkin’s lymphoma.</p>
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<p>The interaction between short-chain fatty acids (SCFA) and lymphoma.</p>
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<p>The key effect of urolithin A and urolithin B against T-cell lymphoma and B-cell lymphoma is through the NF-κB signaling pathway.</p>
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<p>Chemical structure of urolithins (<b>A</b>–<b>D</b>).</p>
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12 pages, 1482 KiB  
Article
A New Biomarker Profiling Strategy for Gut Microbiome Research: Valid Association of Metabolites to Metabolism of Microbiota Detected by Non-Targeted Metabolomics in Human Urine
by Sijia Zheng, Lina Zhou, Miriam Hoene, Andreas Peter, Andreas L. Birkenfeld, Cora Weigert, Xinyu Liu, Xinjie Zhao, Guowang Xu and Rainer Lehmann
Metabolites 2023, 13(10), 1061; https://doi.org/10.3390/metabo13101061 - 9 Oct 2023
Viewed by 1924
Abstract
The gut microbiome is of tremendous relevance to human health and disease, so it is a hot topic of omics-driven biomedical research. However, a valid identification of gut microbiota-associated molecules in human blood or urine is difficult to achieve. We hypothesize that bowel [...] Read more.
The gut microbiome is of tremendous relevance to human health and disease, so it is a hot topic of omics-driven biomedical research. However, a valid identification of gut microbiota-associated molecules in human blood or urine is difficult to achieve. We hypothesize that bowel evacuation is an easy-to-use approach to reveal such metabolites. A non-targeted and modifying group-assisted metabolomics approach (covering 40 types of modifications) was applied to investigate urine samples collected in two independent experiments at various time points before and after laxative use. Fasting over the same time period served as the control condition. As a result, depletion of the fecal microbiome significantly affected the levels of 331 metabolite ions in urine, including 100 modified metabolites. Dominating modifications were glucuronidations, carboxylations, sulfations, adenine conjugations, butyrylations, malonylations, and acetylations. A total of 32 compounds, including common, but also unexpected fecal microbiota-associated metabolites, were annotated. The applied strategy has potential to generate a microbiome-associated metabolite map (M3) of urine from healthy humans, and presumably also other body fluids. Comparative analyses of M3 vs. disease-related metabolite profiles, or therapy-dependent changes may open promising perspectives for human gut microbiome research and diagnostics beyond analyzing feces. Full article
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<p>(<b>A</b>) Time courses of levels of exemplarily selected metabolites in human urine over a 10-day period before and during a laxative-induced bowel evacuation, and after starting refeeding. The red rectangles mark the 48 h period without food consumption and the dash dotted lines on the x-axes separate the different days. In total, 40 urine samples were collected (1st and 2nd morning urine, as well as spot urine) throughout the whole day (sample numbers are provided on the <span class="html-italic">x</span>-axes). The experiment was conducted as self-experiment from one male individual. (<b>B</b>) Control experiment, i.e., 9-day time courses of the levels of the same metabolites before and during a 48 h fasting period, and after starting refeeding. The fasting period is marked by red rectangle. In total, 30 urine samples were collected all day from the same individual. The <span class="html-italic">x</span>-axes show the different days and the <span class="html-italic">y</span>-axes the relative peak responses in arbitrary units.</p>
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<p>(<b>A</b>) Scheme of the experimental design and sample collection time points. (<b>B</b>) Metabolite levels in human urine collected at four time points before and after a bowel evacuation including 24 h fasting period (n = 6, black lines), and only fasting for 24 h (n = 3, blue lines). Time point 1: 1st morning urine; time point 2: 2nd morning urine, collected directly before the start of the bowel evacuation; time point 3: collected 10 h after bowel evacuation; time point 4: collected 12 h after bowel evacuation. Bars represent mean ± SD; the student’s <span class="html-italic">t</span>-test between 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>(<b>A</b>) Heat map of 310 metabolites in human urine showing significantly decreased metabolite levels after laxative-induced bowel evacuation (n = 6) in comparison to only fasting (n = 3). (<b>B</b>) Significantly increased levels of 21 metabolites after bowel evacuation (n = 6) in comparison to only fasting (n = 3). A significant difference was defined as <span class="html-italic">p</span> &lt; 0.05 in a two-tailed unpaired t test comparing relative responses at time point 3 (10 h after bowel evacuation versus the only fasting group at the same time point). In the heat map, each urinary metabolite is represented by a single column. Rows represent different individuals. Black is the intensity at time point 1, green labels show decreased signal intensities, and red labels show increased signal intensities.</p>
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23 pages, 2275 KiB  
Article
Integrated Microbiota and Metabolite Changes following Rice Bran Intake during Murine Inflammatory Colitis-Associated Colon Cancer and in Colorectal Cancer Survivors
by Annika M. Weber, Hend Ibrahim, Bridget A. Baxter, Robin Kumar, Akhilendra K. Maurya, Dileep Kumar, Rajesh Agarwal, Komal Raina and Elizabeth P. Ryan
Cancers 2023, 15(8), 2231; https://doi.org/10.3390/cancers15082231 - 10 Apr 2023
Cited by 3 | Viewed by 3515
Abstract
Dietary rice bran-mediated inhibition of colon carcinogenesis was demonstrated previously for carcinogen-induced rodent models via multiple anti-cancer mechanisms. This study investigated the role of dietary rice bran-mediated changes to fecal microbiota and metabolites over the time course of colon carcinogenesis and compared murine [...] Read more.
Dietary rice bran-mediated inhibition of colon carcinogenesis was demonstrated previously for carcinogen-induced rodent models via multiple anti-cancer mechanisms. This study investigated the role of dietary rice bran-mediated changes to fecal microbiota and metabolites over the time course of colon carcinogenesis and compared murine fecal metabolites to human stool metabolic profiles following rice bran consumption by colorectal cancer survivors (NCT01929122). Forty adult male BALB/c mice were subjected to azoxymethane (AOM)/dextran sodium sulfate (DSS)-induced colitis-associated colon carcinogenesis and randomized to control AIN93M (n = 20) or diets containing 10% w/w heat-stabilized rice bran (n = 20). Feces were serially collected for 16S rRNA amplicon sequencing and non-targeted metabolomics. Fecal microbiota richness and diversity was increased in mice and humans with dietary rice bran treatment. Key drivers of differential bacterial abundances from rice bran intake in mice included Akkermansia, Lactococcus, Lachnospiraceae, and Eubacterium xylanophilum. Murine fecal metabolomics revealed 592 biochemical identities with notable changes to fatty acids, phenolics, and vitamins. Monoacylglycerols, dihydroferulate, 2-hydroxyhippurate (salicylurate), ferulic acid 4-sulfate, and vitamin B6 and E isomers significantly differed between rice bran- and control-fed mice. The kinetics of murine metabolic changes by the host and gut microbiome following rice bran consumption complemented changes observed in humans for apigenin, N-acetylhistamine, and ethylmalonate in feces. Increased enterolactone abundance is a novel diet-driven microbial metabolite fecal biomarker following rice bran consumption in mice and humans from this study. Dietary rice bran bioactivity via gut microbiome metabolism in mice and humans contributes to protection against colorectal cancer. The findings from this study provide compelling support for rice bran in clinical and public health guidelines for colorectal cancer prevention and control. Full article
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Graphical abstract
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<p>Experimental design for rice bran intervention in mice and humans for CRC control and prevention. (<b>A</b>) Six-week-old Balb/c mice were fed an AIN-93M pellet diet and acclimatized for one week. All mice then received a single intraperitoneal injection of 10 mg/kg body weight of azoxymethane (AOM) in saline. Seven days after AOM injection, mice were subjected to 2% dextran sodium sulfate (DSS) in drinking water for five days. Mice were then randomized and switched to either the rice bran (<span class="html-italic">n</span> = 20) diet group or maintained on the control AIN-93M pellet (<span class="html-italic">n</span> = 20) diet. Feces were collected from mice in both groups at baseline, and 2, 6, 10, and 14 weeks. (<b>B</b>) Human dietary rice bran intervention trial in CRC survivors (NCT01929122). Participants were provided daily meals and snacks with rice bran (<span class="html-italic">n</span> = 9) or no rice bran (<span class="html-italic">n</span> = 10) for 4 weeks. Stool samples were collected at baseline and week 4.</p>
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<p>Time-dependent fecal microbiota changes following control and rice bran intake in AOM/DSS-treated mice. (<b>A</b>) Unweighted UniFrac PCoA plot of the fecal microbiome separation by diet: rice bran in blue, control in orange. (<b>B</b>) Phylum-level changes in rice bran and control groups at baseline, and 2, 6, 10, and 14 weeks. (<b>C</b>) Genus-level log2 fold differences in the rice bran group compared to the control at week 10 and week 14. (<b>D</b>) ANCOM volcano plots, centered log ratio (clr) with W test statistic, and differentially abundant features between diet types. Statistically significant features as calculated by ANCOM are labelled.</p>
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<p>Fecal metabolite profiles modified by rice bran intake in AOM/DSS-treated mice. (<b>A</b>) PCA demonstrates segregation of the baseline to the 2- to 14-week time points. Stool metabolite mean relative scaled abundance (MSRA) fold difference with statistical significance between mice fed rice bran and the control at 14 weeks (<span class="html-italic">p</span> &lt; 0.05) and clustered into chemical classifications as (<b>B</b>) phytochemicals, (<b>C</b>) amino acids and peptides, and (<b>D</b>) carbohydrates and energy. (<b>E</b>) Pearson correlation heatmap depicting selected gut microbiota and fecal metabolites in AOM/DSS mice at baseline, week 6, week 10, and week 14, for both the control and rice bran groups.</p>
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<p>Median-scaled relative abundances (MSRA) for human fecal metabolites from the Beans/Bran Enriching Nutritional Eating For Intestinal Health Trial (BENEFIT) study at 4 weeks compared to the baseline are depicted (<b>left</b>). MSRAs for murine fecal metabolites from the rice bran group at baseline, and 2, 6, 10, and 14 weeks (<b>right</b>). (<b>A</b>) N-acetylhistamine, (<b>B</b>) beta-hydroxyisovalerate, (<b>C</b>) ethylmalonate, (<b>D</b>) N-acetylmethionine sulfoxide, (<b>E</b>) gamma-glutamylphenylalanine, (<b>F</b>) p-cresol sulfate, (<b>G</b>) apigenin, and (<b>H</b>) enterolactone (* = <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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23 pages, 1154 KiB  
Review
Gut-Derived Metabolite, Trimethylamine-N-oxide (TMAO) in Cardio-Metabolic Diseases: Detection, Mechanism, and Potential Therapeutics
by Meyammai Shanmugham, Sophie Bellanger and Chen Huei Leo
Pharmaceuticals 2023, 16(4), 504; https://doi.org/10.3390/ph16040504 - 28 Mar 2023
Cited by 25 | Viewed by 10084
Abstract
Trimethylamine N-oxide (TMAO) is a biologically active gut microbiome-derived dietary metabolite. Recent studies have shown that high circulating plasma TMAO levels are closely associated with diseases such as atherosclerosis and hypertension, and metabolic disorders such as diabetes and hyperlipidemia, contributing to endothelial dysfunction. [...] Read more.
Trimethylamine N-oxide (TMAO) is a biologically active gut microbiome-derived dietary metabolite. Recent studies have shown that high circulating plasma TMAO levels are closely associated with diseases such as atherosclerosis and hypertension, and metabolic disorders such as diabetes and hyperlipidemia, contributing to endothelial dysfunction. There is a growing interest to understand the mechanisms underlying TMAO-induced endothelial dysfunction in cardio-metabolic diseases. Endothelial dysfunction mediated by TMAO is mainly driven by inflammation and oxidative stress, which includes: (1) activation of foam cells; (2) upregulation of cytokines and adhesion molecules; (3) increased production of reactive oxygen species (ROS); (4) platelet hyperreactivity; and (5) reduced vascular tone. In this review, we summarize the potential roles of TMAO in inducing endothelial dysfunction and the mechanisms leading to the pathogenesis and progression of associated disease conditions. We also discuss the potential therapeutic strategies for the treatment of TMAO-induced endothelial dysfunction in cardio-metabolic diseases. Full article
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<p>Biochemical pathways involved in the formation of TMAO. TMAO is synthesized from dietary precursors after the action of the gut microbiota and flavin-containing monooxygenases, mainly the FMO3 enzyme in the liver. Increased plasma TMAO levels are associated with biological pathways that trigger endothelial dysfunction and lead to cardiovascular complications.</p>
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<p>Proposed mechanisms of action in TMAO-induced cardio-metabolic diseases. Increased circulating levels of TMAO cause various processes within the endothelial cells, contributing to the pathogenesis of endothelial dysfunction and atherosclerosis.</p>
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17 pages, 2263 KiB  
Article
Association between the Changes in Trimethylamine N-Oxide-Related Metabolites and Prognosis of Patients with Acute Myocardial Infarction: A Prospective Study
by Nan Li, Ying Wang, Jinying Zhou, Runzhen Chen, Jiannan Li, Xiaoxiao Zhao, Peng Zhou, Chen Liu, Yi Chen, Li Song, Hanjun Zhao, Hongbing Yan and Shaodi Yan
J. Cardiovasc. Dev. Dis. 2022, 9(11), 380; https://doi.org/10.3390/jcdd9110380 - 4 Nov 2022
Cited by 8 | Viewed by 1878
Abstract
This study aimed to investigate the association between changes in levels of trimethylamine N-oxide (TMAO) and its precursors and the prognosis of patients with acute myocardial infarction (AMI). Patients diagnosed with AMI were prospectively enrolled at Fuwai Hospital between March 2017 and January [...] Read more.
This study aimed to investigate the association between changes in levels of trimethylamine N-oxide (TMAO) and its precursors and the prognosis of patients with acute myocardial infarction (AMI). Patients diagnosed with AMI were prospectively enrolled at Fuwai Hospital between March 2017 and January 2020. TMAO, betaine, choline, and L-carnitine were measured in 1203 patients at their initial admission and 509 patients at their follow-up of one month. Major adverse cardiovascular events (MACE), a composite of all-cause death, recurrence of MI, rehospitalization caused by HF, ischemic stroke, and any revascularization, were followed up. A decision tree by TMAO levels implicated that compared to those with low levels at admission, patients with high TMAO levels at both time points showed an increased risk of MACE (adjusted hazard ratio (HR) 1.59, 95% confidence interval (CI): 1.03–2.46; p = 0.034), while patients with high TMAO levels at admission and low levels at follow-up exhibited a similar MACE risk (adjusted HR 1.20, 95% CI: 0.69–2.06; p = 0.520). Patients with high choline levels at admission and follow-up showed an elevated MACE risk compared to those with low levels at both time points (HR 1.55, 95% CI: 1.03–2.34; p = 0.034). Repeated assessment of TMAO and choline levels helps to identify the dynamic risk of cardiovascular events. Full article
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<p>Kaplan—curve for cumulative event-free survival in groups stratified by TMAO tertile levels at enrollment. (<b>A</b>) major adverse cardiovascular event, (<b>B</b>) all-cause death, (<b>C</b>) myocardial infarction, (<b>D</b>) rehospitalization caused by heart failure; (<b>E</b>) stroke; (<b>F</b>) revascularization. TMAO, trimethylamine-N-oxide.</p>
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<p>Forest plot of hazard ratios for major adverse cardiovascular events in groups according to trimethylamine N-oxide (TMAO) (<b>A</b>,<b>B</b>) and choline (<b>C</b>,<b>D</b>) levels at enrollment (V1) and follow-up visit (V2). Patients with available TMAO and choline levels of V1 and V2 were divided into four groups according to the median levels of each visit (TMAO: 6.7 µmol/L and 12.7 µmol/L, choline: 1.2 µmol/L and 1.7 µmol/L for V1 and V2, respectively). L/L, low V1 and low V2; L/H, low V1 and high V2; H/L, high V1 and low V2; H/H, high V1 and high V2. Cox proportional hazards regression was used to compare the risk of major adverse cardiovascular events among the four groups of patients using L/L as the reference on each occasion [(<b>A</b>,<b>C</b>) unadjusted, (<b>B</b>) adjusted with age, hypertension, diabetes, peripheral artery disease, chronic kidney disease, and previous history of stroke and MI, Killip II-IV, the Global Registry of Acute Coronary Events risk score, multiple vessels disease, percutaneous coronary intervention, and the peak value of cardiac troponin I and N-terminal pro-B-type natriuretic peptide during hospitalization, as well as estimated glomerular filtration rate and left ventricular ejection fraction at V2; (<b>D</b>) adjusted with these factors and TMAO levels at V2].</p>
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<p>Decision tree of risk stratification for major adverse cardiovascular events (MACE) using combined measurements at enrollment (V1) and follow-up visit (V2) for trimethylamine N-oxide (TMAO) (<b>A</b>). Kaplan–Meier curve for cumulative MACE-free survival in groups generated by decision tree (<b>B</b>). Decision tree using plasma TMAO level at V1 as the initial classifier, followed by plasma TMAO level at V2 enables effective selection of low- and high-risk groups of patients and increased cumulative event risk in Group 3 compared to Group 1. The number of events is shown below. Data are presented as adjusted hazard ratio (HR) and 95% confidence interval (CI). The adjusted factors included age, hypertension, diabetes, peripheral artery disease, chronic kidney disease, previous history of stroke and MI, Killip II-IV, the Global Registry of Acute Coronary Events risk score, multiple vessels disease, percutaneous coronary intervention, and the peak value of cardiac troponin I and N-terminal pro-B-type natriuretic peptide during hospitalization, as well as estimated glomerular filtration rate and left ventricular ejection fraction at V2.</p>
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24 pages, 6637 KiB  
Review
Role of Probiotics in the Management of COVID-19: A Computational Perspective
by Quang Vo Nguyen, Li Chuin Chong, Yan-Yan Hor, Lee-Ching Lew, Irfan A. Rather and Sy-Bing Choi
Nutrients 2022, 14(2), 274; https://doi.org/10.3390/nu14020274 - 10 Jan 2022
Cited by 40 | Viewed by 11482
Abstract
Coronavirus disease 2019 (COVID-19) was declared a pandemic at the beginning of 2020, causing millions of deaths worldwide. Millions of vaccine doses have been administered worldwide; however, outbreaks continue. Probiotics are known to restore a stable gut microbiota by regulating innate and adaptive [...] Read more.
Coronavirus disease 2019 (COVID-19) was declared a pandemic at the beginning of 2020, causing millions of deaths worldwide. Millions of vaccine doses have been administered worldwide; however, outbreaks continue. Probiotics are known to restore a stable gut microbiota by regulating innate and adaptive immunity within the gut, demonstrating the possibility that they may be used to combat COVID-19 because of several pieces of evidence suggesting that COVID-19 has an adverse impact on gut microbiota dysbiosis. Thus, probiotics and their metabolites with known antiviral properties may be used as an adjunctive treatment to combat COVID-19. Several clinical trials have revealed the efficacy of probiotics and their metabolites in treating patients with SARS-CoV-2. However, its molecular mechanism has not been unraveled. The availability of abundant data resources and computational methods has significantly changed research finding molecular insights between probiotics and COVID-19. This review highlights computational approaches involving microbiome-based approaches and ensemble-driven docking approaches, as well as a case study proving the effects of probiotic metabolites on SARS-CoV-2. Full article
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<p>Probiotics strains against respiratory (influenza A virus H1N1, H3N2, and respiratory syncytial virus) and gastrointestinal viruses (rotavirus). The figure represents some examples of different strains of <span class="html-italic">Lactobacillus</span> and <span class="html-italic">Bifidobacterium</span> studied for the antiviral effects against viruses.</p>
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<p>Dysbiosis of gut and lung in COVID-19 patients. In the lung of SARS-CoV-2 infected patients, <span class="html-italic">Acinetobacter</span>, <span class="html-italic">Chryseobacterium</span>, <span class="html-italic">Burkhoderia</span>, <span class="html-italic">Brevudimonas</span>, and <span class="html-italic">Sphingobium</span> were prevalent [<a href="#B74-nutrients-14-00274" class="html-bibr">74</a>]. The gut microbiota of COVID-19 patients was also altered, with the decrease of <span class="html-italic">Bacteroides</span> [<a href="#B69-nutrients-14-00274" class="html-bibr">69</a>], <span class="html-italic">Bifidobacterium</span> [<a href="#B68-nutrients-14-00274" class="html-bibr">68</a>], <span class="html-italic">Eubacterium rectale</span> [<a href="#B68-nutrients-14-00274" class="html-bibr">68</a>], <span class="html-italic">Faecalibacterium prausnitzii</span> [<a href="#B73-nutrients-14-00274" class="html-bibr">73</a>], <span class="html-italic">Lachnospiraceae</span> [<a href="#B69-nutrients-14-00274" class="html-bibr">69</a>], <span class="html-italic">Parabacteroides</span> [<a href="#B69-nutrients-14-00274" class="html-bibr">69</a>], and the increase of <span class="html-italic">Clostridium hathewayi</span> [<a href="#B71-nutrients-14-00274" class="html-bibr">71</a>], <span class="html-italic">Clostridium ramosum</span> [<a href="#B71-nutrients-14-00274" class="html-bibr">71</a>], <span class="html-italic">Collinsella</span> [<a href="#B69-nutrients-14-00274" class="html-bibr">69</a>], <span class="html-italic">Coprobacillus</span> [<a href="#B71-nutrients-14-00274" class="html-bibr">71</a>], <span class="html-italic">Morganella</span> [<a href="#B69-nutrients-14-00274" class="html-bibr">69</a>], <span class="html-italic">Streptococcus</span> [<a href="#B69-nutrients-14-00274" class="html-bibr">69</a>].</p>
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<p>The structures of possible viral and host protein targets could be inhibited by probiotic metabolites to prevent SARS-CoV-2. Angiotensin-converting enzyme 2 (ACE2), which locates on host cells, is the primary cell entry receptor for SARS-CoV-2 [<a href="#B174-nutrients-14-00274" class="html-bibr">174</a>]; transmembrane protease serine 2 (TMPRSS2), which facilitate viral activation, is a cell surface protein expressed in the respiratory and GI tract [<a href="#B175-nutrients-14-00274" class="html-bibr">175</a>]. SARS-CoV-2 requires both ACE2 and TMPRSS2 for entry into cells [<a href="#B176-nutrients-14-00274" class="html-bibr">176</a>]. Spike (S) protein involves mainly in the receptor recognition and viral entry of SARS-CoV-2 [<a href="#B177-nutrients-14-00274" class="html-bibr">177</a>]; Papain-like proteinase (PL<sup>pro</sup>) has an essential role in viral polyprotein cleavage and maturation [<a href="#B178-nutrients-14-00274" class="html-bibr">178</a>]; 3C-like main protease (3CL<sup>pro</sup>) plays a key role in control viral replication [<a href="#B179-nutrients-14-00274" class="html-bibr">179</a>]; RNA-dependent RNA polymerase (RdRp), a viral enzyme, involves in viral RNA replication in host cells [<a href="#B180-nutrients-14-00274" class="html-bibr">180</a>]; Nsp13 is a helicase requiring adenosine triphosphate (ATP) to translocate and unwind SARS-CoV-2 RNA [<a href="#B181-nutrients-14-00274" class="html-bibr">181</a>].</p>
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<p>Schematic representation for the reconstruction of PlnE and PlnF. The sequence of PlnE and PlnF were separately modeled using SWISS-MODEL server and AlphaFold Colab, followed by superimposing predicted structures with PlnE template (PDB ID: 2JUI) and PlnF template (PDB ID: 2RLW). The SWISS-MODEL predicted structures were chosen for further modeling due to the lower RMSD value with the templates compared to AlphaFold Colab predicted structures. A homology modeling approach was used to rebuild PlnE and PlnF as a single structure using MODELLER v10.1. The best structure was used to dock against SARS-CoV-2 helicase nsp13 using the protein-protein docking approach.</p>
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<p>Molecular docking of PlnEF towards SARS-CoV-2 helicase nsp13. PlnE (orange) and PlnF (yellow) were modeled as a single structure using MODELLER v10.1. (<b>a</b>) PlnEF was potentially bound toward the incision of the ssRNA and ATP binding site. (<b>b</b>) ssRNA and ATP binding are red (Ser485, Lys146, Lys139, Tyr180, His230, Tyr198, Arg212, Pro335, Arg339, Asn516) and violet (Glu537, Arg567, Arg443, His290, Arg442, Asn265, Gly439, Lys288), respectively.</p>
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16 pages, 712 KiB  
Review
The Effects of Pro-, Pre-, and Synbiotics on Muscle Wasting, a Systematic Review—Gut Permeability as Potential Treatment Target
by Sandra J. van Krimpen, Fleur A. C. Jansen, Veerle L. Ottenheim, Clara Belzer, Miranda van der Ende and Klaske van Norren
Nutrients 2021, 13(4), 1115; https://doi.org/10.3390/nu13041115 - 29 Mar 2021
Cited by 32 | Viewed by 5777
Abstract
Muscle wasting is a frequently observed, inflammation-driven condition in aging and disease, known as sarcopenia and cachexia. Current treatment strategies target the muscle directly and are often not able to reverse the process. Because a reduced gut function is related to systemic inflammation, [...] Read more.
Muscle wasting is a frequently observed, inflammation-driven condition in aging and disease, known as sarcopenia and cachexia. Current treatment strategies target the muscle directly and are often not able to reverse the process. Because a reduced gut function is related to systemic inflammation, this might be an indirect target to ameliorate muscle wasting, by administering pro-, pre-, and synbiotics. Therefore, this review aimed to study the potential of pro-, pre-, and synbiotics to treat muscle wasting and to elucidate which metabolites and mechanisms affect the organ crosstalk in cachexia. Overall, the literature shows that Lactobacillus species pluralis (spp.) and possibly other genera, such as Bifidobacterium, can ameliorate muscle wasting in mouse models. The beneficial effects of Lactobacillus spp. supplementation may be attributed to its potential to improve microbiome balance and to its reported capacity to reduce gut permeability. A subsequent literature search revealed that the reduction of a high gut permeability coincided with improved muscle mass or strength, which shows an association between gut permeability and muscle mass. A possible working mechanism is proposed, involving lactate, butyrate, and reduced inflammation in gut–brain–muscle crosstalk. Thus, reducing gut permeability via Lactobacillus spp. supplementation could be a potential treatment strategy for muscle wasting. Full article
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<p>Flow diagram of (<b>A</b>) the identified, screened, and included studies on the effect of pro-, pre-, and synbiotics on muscle wasting [<a href="#B12-nutrients-13-01115" class="html-bibr">12</a>,<a href="#B13-nutrients-13-01115" class="html-bibr">13</a>,<a href="#B14-nutrients-13-01115" class="html-bibr">14</a>,<a href="#B15-nutrients-13-01115" class="html-bibr">15</a>,<a href="#B16-nutrients-13-01115" class="html-bibr">16</a>,<a href="#B17-nutrients-13-01115" class="html-bibr">17</a>,<a href="#B18-nutrients-13-01115" class="html-bibr">18</a>,<a href="#B19-nutrients-13-01115" class="html-bibr">19</a>,<a href="#B20-nutrients-13-01115" class="html-bibr">20</a>,<a href="#B21-nutrients-13-01115" class="html-bibr">21</a>] and (<b>B</b>) on the relationship between gut permeability and muscle wasting [<a href="#B15-nutrients-13-01115" class="html-bibr">15</a>,<a href="#B16-nutrients-13-01115" class="html-bibr">16</a>,<a href="#B20-nutrients-13-01115" class="html-bibr">20</a>,<a href="#B22-nutrients-13-01115" class="html-bibr">22</a>,<a href="#B23-nutrients-13-01115" class="html-bibr">23</a>,<a href="#B24-nutrients-13-01115" class="html-bibr">24</a>,<a href="#B25-nutrients-13-01115" class="html-bibr">25</a>,<a href="#B26-nutrients-13-01115" class="html-bibr">26</a>].</p>
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<p>Hypothesis on the mechanism behind the reported effects of probiotics on muscle wasting, involving the organ crosstalk during cancer cachexia. On the left-hand side, the situation when gut permeability is high is illustrated. On the right-hand side, the effect of probiotics is shown. The probiotics inhibit gut permeability and thus improve gut function, reduce inflammation, and consequently ameliorate muscle wasting. LPS: lipopolysaccharide; SCFA: short-chain fatty acid</p>
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1583 KiB  
Article
Absorption, Metabolism and Excretion of Cranberry (Poly)phenols in Humans: A Dose Response Study and Assessment of Inter-Individual Variability
by Rodrigo P. Feliciano, Charlotte E. Mills, Geoffrey Istas, Christian Heiss and Ana Rodriguez-Mateos
Nutrients 2017, 9(3), 268; https://doi.org/10.3390/nu9030268 - 11 Mar 2017
Cited by 88 | Viewed by 9349
Abstract
The beneficial health effects of cranberries have been attributed to their (poly)phenol content. Recent studies have investigated the absorption, metabolism and excretion of cranberry (poly)phenols; however, little is known about whether they follow a dose response in vivo at different levels of intake. [...] Read more.
The beneficial health effects of cranberries have been attributed to their (poly)phenol content. Recent studies have investigated the absorption, metabolism and excretion of cranberry (poly)phenols; however, little is known about whether they follow a dose response in vivo at different levels of intake. An acute double-blind randomized controlled trial in 10 healthy men with cranberry juices containing 409, 787, 1238, 1534 and 1910 mg total (poly)phenols was performed. Blood and urine were analyzed by UPLC-Q-TOF-MS. Sixty metabolites were identified in plasma and urine including cinnamic acids, dihydrocinnamic, flavonols, benzoic acids, phenylacetic acids, benzaldehydes, valerolactones, hippuric acids, catechols, and pyrogallols. Total plasma, but not excreted urinary (poly)phenol metabolites, exhibited a linear dose response (r2 = 0.74, p < 0.05), driven by caffeic acid 4-O-ß-d-glucuronide, quercetin-3-O-ß-d-glucuronide, ferulic acid 4-O-ß-d-glucuronide, 2,5-dihydroxybenzoic acid, 2,4-dihydroxybenzoic acid, ferulic acid, caffeic acid 3-O-ß-d-glucuronide, sinapic acid, ferulic acid 4-O-sulfate, 3-hydroxybenzoic acid, syringic acid, vanillic acid-4-O-sulfate, (4R)-5-(3′-hydroxyphenyl)-γ-valerolactone-4′-O-sulfate, 4-methylgallic acid-3-O-sulfate, and isoferulic acid 3-O-sulfate (all r2 ≥ 0.89, p < 0.05). Inter-individual variability of the plasma metabolite concentration was broad and dependent on the metabolite. Herein, we show that specific plasma (poly)phenol metabolites are linearly related to the amount of (poly)phenols consumed in cranberry juice. The large inter-individual variation in metabolite profile may be due to variations in the gut microbiome. Full article
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<p>Linear regression analysis between (poly)phenol amount (mg) contained in the cranberry juice interventions and the average area under the curve (AUC (nM*h)) and maximum concentration (C<sub>max</sub>) (nM). Data presented as mean ± standard error of the mean.</p>
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<p>Box and whisker plot of (a) Maximal concentration in plasma (Cmax) and (b) Area under the curve (AUC) of the plasma concentration over time of individual plasma metabolites after 787 mg total (poly)phenol from cranberry juice. Data are presented as median plus upper and minus lower quartiles. Whiskers represent the maximum and minimum values obtained. The coefficient of variation expressed as % is indicated above of the box and whisker plot for each metabolite. Legend. 1. <span class="html-italic">o</span>-coumaric acid, 2. 2-hydroxyhippuric acid, 3. Chlorogenic acid, 4. Dihydro Isoferulic acid 3-<span class="html-italic">O</span>-ß-<span class="html-small-caps">d</span>-Glucuronide, 5. 3,4-dihydroxybenzaldehyde, 6. Kaempferol-3-glucuronide, 7. Caffeic acid, 8. 3-hydroxyhippuric acid, 9. 2,4-dihydroxybenzoic acid, 10. Dihydro ferulic acid 4-<span class="html-italic">O</span>-sulfate, 11. m-coumaric acid, 12. Syringic acid, 13. kaempferol, 14. Homovanillic acid sulfate, 15. Protocatechuic acid, 16. t-cinnamic acid, 17. sinapic acid, 18. Caffeic Acid 3-ß-<span class="html-small-caps">d</span>-Glucuronide, 19. 3-hydroxybenzoic acid, 20. Dihydroferulic acid, 21. Pyrogallol-<span class="html-italic">O</span>-1-sulfate, 22. 4-hydroxybenzoic acid, 23. <span class="html-italic">trans</span>-ferulic acid, 24. 2-Methylpyrogallol-<span class="html-italic">O</span>-sulfate, 25. Isoferulic acid 3-<span class="html-italic">O</span>-sulfate, 26. 4-hydroxybenzaldehyde, 27. dihydrocaffeic acid, 28. 1-Methylpyrogallol-<span class="html-italic">O</span>-sulfate, 29. DihydroIsoferulic acid 3-<span class="html-italic">O</span>-sulfate, 30. Dihydro Ferulic Acid 4-<span class="html-italic">O</span>-ß-<span class="html-small-caps">d</span>-Glucuronide, 31. Dihydro Caffeic Acid 3-<span class="html-italic">O</span>-Sulfate, 32. Caffeic Acid 4-ß-<span class="html-small-caps">d</span>-Glucuronide, 33. Dihydro Caffeic Acid 3-<span class="html-italic">O</span>-ß-<span class="html-small-caps">d</span>-Glucuronide, 34. p-coumaric acid, 35. Isovanillic acid, 36. 2-hydroxybenzoic acid, 37. Pyrogallol-<span class="html-italic">O</span>-2-sulfate, 38. 4-hydroxyhippuric acid, 39. 4-Methylgallic-3-<span class="html-italic">O</span>-sulfate, 40. Quercetin-glucuronide, 41. 2,5-dihydroxybenzoic acid, 42. Homovanillic acid, 43. 3-hydroxyphenyl acetic acid, 44. (4R)-5-(3′,4′-Dihydroxyphenyl)-gamma-valerolactone-4′-<span class="html-italic">O</span>-sulfate, 45. vanillic acid, 46. Ferulic acid 4-<span class="html-italic">O</span>-glucuronide, 47. 3,4-dihydroxyphenyl acetic acid, 48. 4-hydroxyphenyl acetic acid, 49. benzoic acid, 50. Isoferulic Acid 3-<span class="html-italic">O</span>-ß-<span class="html-small-caps">d</span>-Glucuronide, 51. 4-Methylcatechol-<span class="html-italic">O</span>-sulfate, 52. Vanillic acid-4-<span class="html-italic">O</span>-sulfate, 53. isoferulic acid, 54. Ferulic Acid 4-<span class="html-italic">O</span>-Sulfate, 55. phenylacetic acid, 56. 2,3-dihydroxybenzoic acid, 57. Alfa-hydroxyhippuric acid, 58. Catechol-<span class="html-italic">O</span>-sulfate, 59. Hippuric acid.</p>
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<p>Box and whisker plot of (a) Maximal concentration in plasma (Cmax) and (b) Area under the curve (AUC) of the plasma concentration over time of individual plasma metabolites after 787 mg total (poly)phenol from cranberry juice. Data are presented as median plus upper and minus lower quartiles. Whiskers represent the maximum and minimum values obtained. The coefficient of variation expressed as % is indicated above of the box and whisker plot for each metabolite. Legend. 1. <span class="html-italic">o</span>-coumaric acid, 2. 2-hydroxyhippuric acid, 3. Chlorogenic acid, 4. Dihydro Isoferulic acid 3-<span class="html-italic">O</span>-ß-<span class="html-small-caps">d</span>-Glucuronide, 5. 3,4-dihydroxybenzaldehyde, 6. Kaempferol-3-glucuronide, 7. Caffeic acid, 8. 3-hydroxyhippuric acid, 9. 2,4-dihydroxybenzoic acid, 10. Dihydro ferulic acid 4-<span class="html-italic">O</span>-sulfate, 11. m-coumaric acid, 12. Syringic acid, 13. kaempferol, 14. Homovanillic acid sulfate, 15. Protocatechuic acid, 16. t-cinnamic acid, 17. sinapic acid, 18. Caffeic Acid 3-ß-<span class="html-small-caps">d</span>-Glucuronide, 19. 3-hydroxybenzoic acid, 20. Dihydroferulic acid, 21. Pyrogallol-<span class="html-italic">O</span>-1-sulfate, 22. 4-hydroxybenzoic acid, 23. <span class="html-italic">trans</span>-ferulic acid, 24. 2-Methylpyrogallol-<span class="html-italic">O</span>-sulfate, 25. Isoferulic acid 3-<span class="html-italic">O</span>-sulfate, 26. 4-hydroxybenzaldehyde, 27. dihydrocaffeic acid, 28. 1-Methylpyrogallol-<span class="html-italic">O</span>-sulfate, 29. DihydroIsoferulic acid 3-<span class="html-italic">O</span>-sulfate, 30. Dihydro Ferulic Acid 4-<span class="html-italic">O</span>-ß-<span class="html-small-caps">d</span>-Glucuronide, 31. Dihydro Caffeic Acid 3-<span class="html-italic">O</span>-Sulfate, 32. Caffeic Acid 4-ß-<span class="html-small-caps">d</span>-Glucuronide, 33. Dihydro Caffeic Acid 3-<span class="html-italic">O</span>-ß-<span class="html-small-caps">d</span>-Glucuronide, 34. p-coumaric acid, 35. Isovanillic acid, 36. 2-hydroxybenzoic acid, 37. Pyrogallol-<span class="html-italic">O</span>-2-sulfate, 38. 4-hydroxyhippuric acid, 39. 4-Methylgallic-3-<span class="html-italic">O</span>-sulfate, 40. Quercetin-glucuronide, 41. 2,5-dihydroxybenzoic acid, 42. Homovanillic acid, 43. 3-hydroxyphenyl acetic acid, 44. (4R)-5-(3′,4′-Dihydroxyphenyl)-gamma-valerolactone-4′-<span class="html-italic">O</span>-sulfate, 45. vanillic acid, 46. Ferulic acid 4-<span class="html-italic">O</span>-glucuronide, 47. 3,4-dihydroxyphenyl acetic acid, 48. 4-hydroxyphenyl acetic acid, 49. benzoic acid, 50. Isoferulic Acid 3-<span class="html-italic">O</span>-ß-<span class="html-small-caps">d</span>-Glucuronide, 51. 4-Methylcatechol-<span class="html-italic">O</span>-sulfate, 52. Vanillic acid-4-<span class="html-italic">O</span>-sulfate, 53. isoferulic acid, 54. Ferulic Acid 4-<span class="html-italic">O</span>-Sulfate, 55. phenylacetic acid, 56. 2,3-dihydroxybenzoic acid, 57. Alfa-hydroxyhippuric acid, 58. Catechol-<span class="html-italic">O</span>-sulfate, 59. Hippuric acid.</p>
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<p>Percentage urinary recovery of (poly)phenol metabolites after consumption of cranberry juice with varying amounts of total (poly)phenols. Data are presented as median plus upper and minus lower quartiles. Whiskers represent the maximum and minimum values obtained. Means were compared using one-way ANOVA and Tukey’s test (* <span class="html-italic">p &lt;</span> 0.05 and *** <span class="html-italic">p &lt;</span> 0.001).</p>
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