16 research outputs found

    Gut microbiota, fusobacteria, and colorectal cancer

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    The gut microbiota has emerged as an environmental contributor to colorectal cancer (CRC) in both animal models and human studies. It is now generally accepted that bacteria are ubiquitous colonizers of all exposed human body surfaces, including the entire alimentary tract (5). Recently, the concept that a normal bacterial microbiota is essential for the development of inflammation-induced carcinoma has emerged from studies of well-known colonic bacterial microbiota. This review explores the evidence for a role of fusobacteria, an anaerobic gram-negative bacterium that has repeatedly been detected at colorectal tumor sites in higher abundance than surrounding histologically normal tissue. Mechanistic studies provide insight on the interplay between fusobacteria, other gut microbiota, barrier functions, and host responses. Studies have shown that fusobacteria activate host inflammatory responses designed to protect against pathogens that promote tumor growth. We discuss how future research identifying the pathophysiology underlying fusobacteria colon colonization during colorectal cancer may lead to new therapeutic targets for cancer. Furthermore, disease-protective strategies suppressing tumor development by targeting the local tumor environment via bacteria represent another exciting avenue for researchers and are highlighted in this review

    Sex, Body Mass Index, and Dietary Fiber Intake Influence the Human Gut Microbiome

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    <div><p>Increasing evidence suggests that the composition of the human gut microbiome is important in the etiology of human diseases; however, the personal factors that influence the gut microbiome composition are poorly characterized. Animal models point to sex hormone-related differentials in microbiome composition. In this study, we investigated the relationship of sex, body mass index (BMI) and dietary fiber intake with the gut microbiome in 82 humans. We sequenced fecal 16S rRNA genes by 454 FLX technology, then clustered and classified the reads to microbial genomes using the QIIME pipeline. Relationships of sex, BMI, and fiber intake with overall gut microbiome composition and specific taxon abundances were assessed by permutational MANOVA and multivariate logistic regression, respectively. We found that sex was associated with the gut microbiome composition overall (p=0.001). The gut microbiome in women was characterized by a lower abundance of Bacteroidetes (p=0.03). BMI (>25 kg/m<sup>2</sup><i>vs</i>. <25 kg/m<sup>2</sup>) was associated with the gut microbiome composition overall (p=0.05), and this relationship was strong in women (p=0.03) but not in men (p=0.29). Fiber from beans and from fruits and vegetables were associated, respectively, with greater abundance of Actinobacteria (p=0.006 and false discovery rate adjusted q=0.05) and Clostridia (p=0.009 and false discovery rate adjusted q=0.09). Our findings suggest that sex, BMI, and dietary fiber contribute to shaping the gut microbiome in humans. Better understanding of these relationships may have significant implications for gastrointestinal health and disease prevention.</p></div

    Gut microbiome according to BMI in women and men separately.

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    <p>Unweighted principal coordinate analysis plot of the first two principal coordinates categorized by BMI (<25 kg/m<sup>2</sup>, ≥25 kg/m<sup>2</sup>) in (A) women and (B) men. Ellipses were added to plots using the R package, latticeExtra (R version 2.15.3). Alpha rarefaction plots of Shannon diversity indices grouped by normal weight (<25 kg/m<sup>2</sup>; open circles) and overweight/obese (≥25 kg/m<sup>2</sup>; red circles) status for women (C) and for men (D). Statistical significance was assessed by non-parametric Monte Carlo permutations (QIIME). (E) Relative abundance of Firmicures and Bacteroidetes. Mann-Whitney-Wilcoxon test was used to test for overall differences using SAS software (version 9.3).</p

    Population Characteristics.

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    <p><sup>1</sup>All characteristics were compared by sex using either Chi square or Mann-Whitney-Wilcoxon tests. All analyses were carried out using SAS software (version 9.3).</p><p><sup>2</sup>Race was grouped as White and Other for Chi square test.</p><p>Population Characteristics.</p

    PERMANOVA<sup>1</sup> analysis of personal factors with the unweighted UniFrac distance matrix.

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    <p><sup>1</sup>Adonis, which uses permutational multivariate analysis of variance (PERMANOVA), was used to test statistical significances of association of overall composition with personal factors. All analyses were carried out using the QIIME pipeline.</p><p><sup>2</sup>BMI was categorized as normal weight (<25 kg/m<sup>2</sup>) versus overweight or obese (≥25 kg/m<sup>2</sup>).</p><p><sup>3</sup>Total and specific sources of dietary fiber were categorized as low (quartiles 1–3) versus high (quartile 4) intake.</p><p>PERMANOVA<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0124599#t002fn001" target="_blank"><sup>1</sup></a> analysis of personal factors with the unweighted UniFrac distance matrix.</p

    Gut microbiome according to sex.

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    <p>(A) Unweighted principal coordinate analysis plot of the first two principal coordinates categorized by sex. Ellipses were added to plots using the R package, latticeExtra (R version 2.15.3). (B) Relative abundance of the three major phyla. Mann-Whitney-Wilcoxon test was used to test for overall differences using SAS software (version 9.3). Nominal p-values are listed below each phylum.</p

    Diversified Microbiota of Meconium Is Affected by Maternal Diabetes Status

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    <div><p>Objectives</p><p>This study was aimed to assess the diversity of the meconium microbiome and determine if the bacterial community is affected by maternal diabetes status.</p><p>Methods</p><p>The first intestinal discharge (meconium) was collected from 23 newborns stratified by maternal diabetes status: 4 mothers had pre-gestational type 2 diabetes mellitus (DM) including one mother with dizygotic twins, 5 developed gestational diabetes mellitus (GDM) and 13 had no diabetes. The meconium microbiome was profiled using multi-barcode 16S rRNA sequencing followed by taxonomic assignment and diversity analysis.</p><p>Results</p><p>All meconium samples were not sterile and contained diversified microbiota. Compared with adult feces, the meconium showed a lower species diversity, higher sample-to-sample variation, and enrichment of <i>Proteobacteria</i> and reduction of <i>Bacteroidetes</i>. Among the meconium samples, the taxonomy analyses suggested that the overall bacterial content significantly differed by maternal diabetes status, with the microbiome of the DM group showing higher alpha-diversity than that of no-diabetes or GDM groups. No global difference was found between babies delivered vaginally versus via Cesarean-section. Regression analysis showed that the most robust predictor for the meconium microbiota composition was the maternal diabetes status that preceded pregnancy. Specifically, <i>Bacteroidetes</i> (phyla) and <i>Parabacteriodes</i> (genus) were enriched in the meconium in the DM group compared to the no-diabetes group.</p><p>Conclusions</p><p>Our study provides evidence that meconium contains diversified microbiota and is not affected by the mode of delivery. It also suggests that the meconium microbiome of infants born to mothers with DM is enriched for the same bacterial taxa as those reported in the fecal microbiome of adult DM patients.</p></div

    List of OTUs that showed significant difference between neonates from mothers with different diabetes states.

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    <p>HC(healthy) group was combined with both healthy and subclinical groups. *unadjusted p-value from WMW-test between HC and DM(type 2 diabetes) group; **unadjusted p-value from WMW-test between HC and GDM(gestational diabetes) group.</p

    Adjusted odds ratios for gastric precancerous lesions in relation to selected periodontal pathogen DNA levels and stratified by periodontal disease severity<sup>1</sup>.

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    <p>PD = Pocket Depth, CAL = Clinical Attachment Loss.</p>1<p>Cut-points for periodontal disease severity were determined at the median.</p>2<p>ORs estimated in relation to a standard deviation increase in the log-transformed bacterial DNA levels and adjusted for gender, age, race, smoking status, educational attainment, BMI, and <i>H. pylori</i> status.</p>3<p>Cumulative bacterial burden is a summary measure estimated by standardizing each bacterial DNA level (dividing each person-specific value by the log-transformed population standard deviation) and summing these values across all four species.</p>4<p>P-value for the cross-product term of each log-transformed bacterial DNA level and each periodontal index entered as continuous variables.</p
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