Evaluation of Oral Cavity DNA Extraction Methods on Bacterial and Fungal Microbiota

Abstract

The objective of this study was to evaluate the most effective method of DNA extraction of oral mouthwash samples for use in microbiome studies that utilize next generation sequencing (NGS). Eight enzymatic and mechanical DNA extraction methods were tested. Extracted DNA was amplified using barcoded primers targeting the V6 variable region of the bacterial 16S rRNA gene and the ITS1 region of the fungal ribosomal gene cluster and sequenced using the Illumina NGS platform. Sequenced reads were analyzed using QIIME and R. The eight methods yielded significantly different quantities of DNA (p < 0.001), with the phenol-chloroform extraction method producing the highest total yield. There were no significant differences in observed bacterial or fungal Shannon diversity (p = 0.64, p = 0.93 respectively) by extraction method. Bray-Curtis beta-diversity did not demonstrate statistically significant differences between the eight extraction methods based on bacterial (R2 = 0.086, p = 1.00) and fungal (R2 = 0.039, p = 1.00) assays. No differences were seen between methods with or without bead-beating. These data indicate that choice of DNA extraction method affect total DNA recovery without significantly affecting the observed microbiome.

Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1

DNA Quantitation for each isolation method. DNA concentrations (ng/μl) of six oral samples were calculated after eight different DNA extraction methods described in Table 1 and corresponding to the categories shown on the x-axis. All methods used the same starting quantities of sample and final volumes were equal; concentrations are proportional to total DNA recovered. Statistical analyses of the differences in DNA amounts recovered are shown in Table 2.

Figure 2

Heat Map of Bacterial and Fungal Species. (A) Bacterial heatmap. The top 20 bacterial OTUs for six oral samples processed by eight different extraction methods were used to construct a heatmap. OTUs were classified to species or lowest possible taxonomic level. Heatmap shows that samples cluster by patient (SampleID, 2nd row), not extraction method (Method, 1st row). (B) Fungal Heatmap. The top 20 fungal OTUs were used to construct a heatmap for the same samples described in panel A. Fungal OTUs were classified to species or lowest possible taxonomic level. Clustering demonstrated predominant grouping by individual (SampleID, 2nd row) vs. method of extraction (Method, 1st row). Legends to the left of the figures indicate color scheme for log transformed OTU abundance, method and sample in descending order.

Figure 3

Comparison of Fungal and Bacterial Shannon Alpha Diversity Measures. Shannon alpha diversity box plots of bacterial and fungal community composition based on variance in species evenness is shown for samples (panels A and B) and by methods (panels C and D). Significant variance is observed in bacterial sample evenness, p 

Figure 4

Beta-diversity Visualized Using Non-metric Multidimensional Scaling (NMDS) Plot With Bray-Curtis Dissimilarity Distances. NMDS plots on rank order Bray-Curtis distances were used to assess significance in bacterial and fungal community composition between individuals (panels A and B) and methods (panels C and D). Plot ellipses represent the 95% confidence regions for group clusters. Clustering by sample is highly significant for bacterial R2 = 0.80 p < 0.001 (panel A) and fungal communities R2 = 0.84 p < 0.001 (panel B) communities. DNA isolation method did not exhibit significant clustering in either bacterial R2 = 0.086 p = 0.996 (panel C) or fungal communities R2 = 0.039 p = 1.00 (panel D). Significance was determined using PERMANOVA analyses.