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

Partial Spearman correlation coefficients for associations between % urinary As metabolites and urinary creatinine, cystatin C, and eGFR, adjusting for sex and age (and recruitment year in the total sample only).

a<p>r (p-value), all such values.</p><p>Partial Spearman correlation coefficients for associations between % urinary As metabolites and urinary creatinine, cystatin C, and eGFR, adjusting for sex and age (and recruitment year in the total sample only).</p

Characteristics of the study samples.

a<p>Chi-square and Wilcoxon's rank-sum tests were used to test for between sample differences in categorical and continuous variables, respectively.</p>b<p>2001 sample N = 366, 2003 sample N = 109.</p>c<p>%DMA, %MMA, and %InAs, the proportion of total urinary arsenic excreted as dimethylarsinic acid, monomethylarsonic acid, and inorganic arsenic, respectively.</p>d<p>2003 sample N = 109.</p>e<p>tHcys, total homocysteine.</p>f<p>Defined as trace protein or greater in urine by dipstick test.</p>g<p>2001 sample N = 365.</p>h<p>2001 sample N = 344.</p><p>Characteristics of the study samples.</p

Prevalence of chronic kidney disease (CKD) stages by gender.

<p>Prevalence of chronic kidney disease (CKD) stages by gender.</p

Arsenic metabolism and creatine synthesis.

<p>(A) Guanadinoacetate methyltransferase (GAMT) and phosphatidyl ethanolamine methyltransferase (PEMT), which catalyze the synthesis of creatine (Cr) and phosphatidylcholine, are the major consumers of S-adenosylmethionine (SAM). Arsenic methyltransferase (AS3MT) uses quantitatively much less SAM. (B) In the first, and rate-limiting, step of Cr biosynthesis, guanadinoacetate (GAA) is formed in the kidney by arginine:glycine amidinotransferase (AGAT). Dietary creatine (e.g. primarily from meat) leads to pre-translational inhibition of AGAT, thereby inhibiting endogenous creatine biosynthesis. GAA is transported to the liver, where it is methylated by GAMT to generate Cr, with SAM as the methyl donor. SAM also serves as the methyl donor for the methylation of trivalent inorganic arsenic (InAs^III) to monomethylarsonic acid (MMA^V), and for the methylation of monomethylaronous acid (MMA^III) to dimethylarsinic acid (DMA^V). The by-product of these methylation reactions is S-adenosylhomocysteine (SAH). Creatine, whether derived from endogenous biosynthesis or dietary sources, is transported to tissues with high energy requirements such as skeletal muscle, heart, and brain, where it is phosphorylated to phosphoryl-creatine (PCr). PCr is used for the regeneration of ATP during intensive exercise. Creatine and PCr are converted non-enzymatically at a constant rate to creatinine (Crn), which is then excreted in the urine. Image credit: Brandilyn A. Peters.</p

Logistic and linear regression models using uCrn and eGFR to predict %uAs metabolites.

a<p>We examined confounding or mediation of associations between uCrn and %As metabolites by using nested models, with and without control for eGFR; Model 1 parameters are log(age), sex, current smoking, log(total uAs), log(uCrn), and recruitment year (in total sample only); Model 2 parameters are log(age), sex, current smoking, log(total uAs), log(uCrn), eGFR, and recruitment year (in total sample only).</p>b<p>Generalized R².</p>c<p>Probability modeled is %uInAs >12.2 (total sample: %uInAs ≤12.2 N = 168, %uInAs >12.2 N = 310; 2001 sample: %uInAs ≤12.2 N = 123, %uInAs >12.2 N = 245; 2003 sample: %uInAs ≤12.2 N = 45, %uInAs >12.2 N = 65).</p><p>Logistic and linear regression models using uCrn and eGFR to predict %uAs metabolites.</p

Linear regression models using log(total urinary As in µg/L), log(uAs metabolites in µg/L), or log(water As in µg/L) to predict eGFR.

a<p>Adjusted for log(age), sex, current smoking, log(urinary creatinine), and recruitment year (total sample only).</p>b<p>Adjusted for log(age), sex, current smoking, and recruitment year (total sample only).</p><p>Linear regression models using log(total urinary As in µg/L), log(uAs metabolites in µg/L), or log(water As in µg/L) to predict eGFR.</p