Comparison of the oral microbiome in mouthwash and whole saliva samples

<div><p>Population-based epidemiologic studies can provide important insight regarding the role of the microbiome in human health and disease. Buccal cells samples using commercial mouthwash have been obtained in large prospective cohorts for the purpose of studying human genomic DNA. We aimed to better understand if these mouthwash samples are also a valid resource for the study of the oral microbiome. We collected one saliva sample and one Scope mouthwash sample from 10 healthy subjects. Bacterial 16S rRNA genes from both types of samples were amplified, sequenced, and assigned to bacterial taxa. We comprehensively compared these paired samples for bacterial community composition and individual taxonomic abundance. We found that mouthwash samples yielded similar amount of bacterial DNA as saliva samples (<i>p</i> from Student’s t-test for paired samples = 0.92). Additionally, the paired samples had similar within sample diversity (<i>p</i> from = 0.33 for richness, and <i>p</i> = 0.51 for Shannon index), and clustered as pairs for diversity when analyzed by unsupervised hierarchical cluster analysis. No significant difference was found in the paired samples with respect to the taxonomic abundance of major bacterial phyla, <i>Bacteroidetes</i>, <i>Firmicutes</i>, <i>Proteobacteria</i>, <i>Fusobacteria</i>, and <i>Actinobacteria</i> (FDR adjusted q values from Wilcoxin signed-rank test = 0.15, 0.15, 0.87, 1.00 and 0.15, respectively), and all identified genera, including genus <i>Streptococcus</i> (q = 0.21), <i>Prevotella</i> (q = 0.25), <i>Neisseria</i> (q = 0.37), <i>Veillonella</i> (q = 0.73), <i>Fusobacterium</i> (q = 0.19), and <i>Porphyromonas</i> (q = 0.60). These results show that mouthwash samples perform similarly to saliva samples for analysis of the oral microbiome. Mouthwash samples collected originally for analysis of human DNA are also a resource suitable for human microbiome research.</p></div

Correlation of the centered Log-Ratio (clr) transformed count of major bacteria phyla and genera in the paired mouthwash-saliva samples.

<p>Correlation of clr-transformed counts in mouthwash and saliva samples of major bacterial phyla (Panel A) and genera (Panel B). The x-axis represents the transformed counts in mouthwash samples, and the y-axis represents transformed counts in saliva samples. The straight line is the line of equality. All FDR adjusted q values from Wilcoxon signed-rank test for the comparison of the taxonomic abundance in paired samples were >0.05.</p

Alpha-diversity of oral bacterial communities in the paired mouthwash-saliva samples.

<p>Bar plots of number of observed OTUs (a) and Shannon Index (b) in paired mouthwash-saliva samples in 10 subjects. These indices were calculated for 500 iterations of rarefied OTU table with minimum sequencing depth of 38,400 among all study subjects, with the average over the iterations taken for each participant. No differences were found between mouthwash and saliva samples in α-diversity (<i>p</i> from paired t-test = 0.33 for richness, and 0.51 for Shannon index).</p

Beta-diversity of oral bacterial communities in the paired mouthwash-saliva samples.

<p>Hierarchical cluster analysis using JSD distance. AU (approximately unbiased) <i>p</i>-values, the unbiased bootstrap probability, ranged from 0.97 to 1.00 for all paired samples in hierarchical cluster analysis with number of 1,000 bootstrap replications. Cluster with AU ≥ 0.95 are considered to be strongly supported by data. S01-S10 indicate study subject 1 to 10. “M” indicates mouthwash sample and “S” indicates salivary sample.</p

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

<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/m²<i>vs</i>. <25 kg/m²) 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

A Mathematical Formulation and Solution of the CoPhMoRe Inverse Problem for Helically Wrapping Polymer Corona Phases on Cylindrical Substrates

Corona phase molecular recognition (CoPhMoRe) is a new technique that generates a nanoparticle-coupled polymer phase, capable of recognizing a specific molecule with high affinity and selectivity. CoPhMoRe has been successfully demonstrated using polymer wrapped single walled carbon nanotubes, resulting in molecular recognition complexes, to date, for dopamine, estradiol, riboflavin, and l-thyroxine, utilizing combinatorial library screening. A rational alternative design to this empirical library screening is to solve the mathematical formulation that we introduce as <i>the CoPhMoRe inverse problem</i>. This inverse problem seeks a linear function representing the position of monomers or functional groups along a polymer backbone that results in a 3-dimensional structure capable of recognizing a specific molecule when mapped to a nanoparticle surface. The potential solution space for such an inverse problem is infinite in general, but for the specific constraint of a helically wrapping polymer, mapped to a cylindrical nanoparticle, we show in this work that two types of inverse problems are exactly solvable. In one case, the polymer pitch and composition can be designed to allow for the specific binding of a small molecule analyte in the occluded space on the nanotube surface. In the other, a larger macromolecule can interact with a deformed helix, which partially conforms to it. A simplified, coarse-grained molecular model of a helically wrapping polymer demonstrates the inhomogeneous binding potential formed by a wrapping with a given pitch. Calculating the potential maps for various pitch values illustrates that there is an optimal pitch that enables the selective and specific binding of the target analyte. An additional coarse-grained model of a helical wrapping by a polymer consisting of alternating hydrophobic–hydrophilic segments demonstrates the resulting deformed helix corona around the nanotube, which forms accessible binding pockets between the hydrophilic loops. While these are the idealized forms of actual CoPhMoRe phases, the formation and solution of such inverse problems may serve to reduce the dimensionality of library screening for CoPhMoRe discoveries, as well as provide a theoretical basis for understanding certain types of CoPhMoRe recognition

Population Characteristics.

<p>¹All 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>²Race 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.

<p>Unweighted principal coordinate analysis plot of the first two principal coordinates categorized by BMI (<25 kg/m², ≥25 kg/m²) 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/m²; open circles) and overweight/obese (≥25 kg/m²; 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

Association between baseline serum α-tocopherol and γ-tocopherol and risk of prostate cancer, stratified by disease stage and grade, PLCO Study.

a<p>Odds ratios are based on unconditional logistic regression, adjusted for study center, serum cholesterol, serum β-carotene, age, time since initial screening, and year of blood draw.</p>b<p>Aggressive cases were defined as stage III or IV, or Gleason score ≥ 7.</p

PERMANOVA¹ analysis of personal factors with the unweighted UniFrac distance matrix.

<p>¹Adonis, 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>²BMI was categorized as normal weight (<25 kg/m²) versus overweight or obese (≥25 kg/m²).</p><p>³Total 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">¹</a> analysis of personal factors with the unweighted UniFrac distance matrix.</p