44 research outputs found

    Nested PCR Biases in Interpreting Microbial Community Structure in 16S rRNA Gene Sequence Datasets

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    <div><p>Background</p><p>Sequencing of the PCR-amplified 16S rRNA gene has become a common approach to microbial community investigations in the fields of human health and environmental sciences. This approach, however, is difficult when the amount of DNA is too low to be amplified by standard PCR. Nested PCR can be employed as it can amplify samples with DNA concentration several-fold lower than standard PCR. However, potential biases with nested PCRs that could affect measurement of community structure have received little attention.</p><p>Results</p><p>In this study, we used 17 DNAs extracted from vaginal swabs and 12 DNAs extracted from stool samples to study the influence of nested PCR amplification of the 16S rRNA gene on the estimation of microbial community structure using Illumina MiSeq sequencing. Nested and standard PCR methods were compared on alpha- and beta-diversity metrics and relative abundances of bacterial genera. The effects of number of cycles in the first round of PCR (10 vs. 20) and microbial diversity (relatively low in vagina vs. high in stool) were also investigated. Vaginal swab samples showed no significant difference in alpha diversity or community structure between nested PCR and standard PCR (one round of 40 cycles). Stool samples showed significant differences in alpha diversity (except Shannon’s index) and relative abundance of 13 genera between nested PCR with 20 cycles in the first round and standard PCR (P<0.01), but not between nested PCR with 10 cycles in the first round and standard PCR. Operational taxonomic units (OTUs) that had low relative abundance (sum of relative abundance <0.167) accounted for most of the distortion (>27% of total OTUs in stool).</p><p>Conclusions</p><p>Nested PCR introduced bias in estimated diversity and community structure. The bias was more significant for communities with relatively higher diversity and when more cycles were applied in the first round of PCR. We conclude that nested PCR could be used when standard PCR does not work. However, rare taxa detected by nested PCR should be validated by other technologies.</p></div

    Clusters of stool samples based on bacterial genus relative abundance.

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    <p>Heatmaps were based on the hierarchical clustering solution (Bray-Curtis) distance metric and average clustering method. Row represents different sample ID (The number before the period is the subject ID; The text after the period is the PCR method used.). Columns represent the predominant bacterial genera with mean relative abundance of 0.01 or greater. The colors in the heatmaps represent the relative abundance of each genus, as indicated in the upper left corner of each panel.</p

    The details for nested and standard PCR design and number of successful PCR reactions in vagina and stool samples.

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    <p>Note, Primer pair 1 is 515F and 1492R; primer pair 2 is the tagged 515F and 806R.</p><p>The details for nested and standard PCR design and number of successful PCR reactions in vagina and stool samples.</p

    Summary of number of OTUs which were detected by only either nested PCRs or standard PCR controls when compared with each other for the matched samples.

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    <p>Summary of number of OTUs which were detected by only either nested PCRs or standard PCR controls when compared with each other for the matched samples.</p

    Principal coordinates analysis (PCoA) of weighted UniFrac distance.

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    <p>Proportion of variance explained by each axis is denoted in the corresponding axis labels. Each symbol (designated by the combination of color and shape) represents each subject with the open symbols for the nested PCRs and the closed symbols for the standard PCRs. For example, the blue circles represent subject 1 with open blue ones for two nested PCR results and closed blue ones for three standard PCR results.</p

    Associations of β-glucuronidase and β-glucosidase mean activity levels with demographic and questionnaire data of 51 study volunteers.

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    a<p>Beta values estimate the increase in log<sub>e</sub> of enzymatic activity (IU/100 mg fecal protein) per unit increase in the independent variable.</p>b<p>BMI models were adjusted for gender and age.</p

    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

    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

    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

    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
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