The saliva microbiome profiles are minimally affected by collection method or DNA extraction protocols

Yenkai Lim, Makrina Totsika, Mark Morrison, Chamindie Punyadeera, Yenkai Lim, Makrina Totsika, Mark Morrison, Chamindie Punyadeera

Abstract

Saliva has attracted attention as a diagnostic fluid due to the association of oral microbiota with systemic diseases. However, the lack of standardised methods for saliva collection has led to the slow uptake of saliva in microbiome research. The aim of this study was to systematically evaluate the potential effects on salivary microbiome profiles using different methods of saliva collection, storage and gDNA extraction. Three types of saliva fractions were collected from healthy individuals with or without the gDNA stabilising buffer. Subsequently, three types of gDNA extraction methods were evaluated to determine the gDNA extraction efficiencies from saliva samples. The purity of total bacterial gDNA was evaluated using the ratio of human β-globin to bacterial 16S rRNA PCR while 16S rRNA gene amplicon sequencing was carried out to identify the bacterial profiles present in these samples. The quantity and quality of extracted gDNA were similar among all three gDNA extraction methods and there were no statistically significant differences in the bacterial profiles among different saliva fractions at the genus-level of taxonomic classification. In conclusion, saliva sampling, processing and gDNA preparation do not have major influence on microbiome profiles.

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
Study design overview. From Fig. 1a, Maxwell® 16 LEV Blood Kit was found to be the most efficient bacterial gDNA extraction method when spit samples were collected in 50 mL sterile Falcon tube. Hence, OMNIgene and other salivary bacterial gDNA extraction methods were excluded from the second part of the study (Fig. 1b).
Figure 2
Figure 2
Extracted salivary gDNA from different saliva collection methods. Scatter plots for the average quantity and quality of the extracted gDNA triplicates from each collection and extraction method (a., b. Maxwell® 16 LEV blood DNA kit; c., d. in-house phenol-chloroform extraction and e., f. QIAamp DNA Microbiome Kit).
Figure 3
Figure 3
Extracted salivary gDNA from different bacterial gDNA extraction methods. Scatter plots for the average quantity and quality of the extracted gDNA triplicates from each collection (a., b. spit from 50 mL Falcon tube and c., d. OMNIgene) and extraction (MW represents Maxwell® 16 LEV blood DNA kit; PC represents in-house phenol-chloroform extraction and QM represents QIAamp DNA Microbiome Kit) method. Significant differences are denoted with *=p 

Figure 4

Extracted salivary gDNA from different…

Figure 4

Extracted salivary gDNA from different saliva fractions. ( a ) Scatter plots for…

Figure 4
Extracted salivary gDNA from different saliva fractions. (a) Scatter plots for the quantity and quality of the extracted bacterial gDNA from each saliva fraction. Significant differences are denoted with *=p < 0.05, **=p < 0.01, ***=p < 0.001, ****=p < 0.0001 respectively. (b) Bacterial 16S rRNA and human β-globin qPCR Ct means distribution trend for the extracted gDNA from spit, drool and oral rinse.

Figure 5

Rarefaction curve for observed operational…

Figure 5

Rarefaction curve for observed operational taxonomic units against sequences per sample for spit,…

Figure 5
Rarefaction curve for observed operational taxonomic units against sequences per sample for spit, drool and oral rinse.

Figure 6

Beta-diversity of salivary microbiome. Weighted…

Figure 6

Beta-diversity of salivary microbiome. Weighted ( a ) and unweighted ( b )…

Figure 6
Beta-diversity of salivary microbiome. Weighted (a) and unweighted (b) PCoA plot for spit, drool and oral rinse samples with respective adjacent plots emphasizing on the subjects in different representing colours.

Figure 7

Salivary microbiome genera redundancy analysis…

Figure 7

Salivary microbiome genera redundancy analysis on different ( a ) saliva fractions and…

Figure 7
Salivary microbiome genera redundancy analysis on different (a) saliva fractions and (b) subjects.
All figures (7)
Figure 4
Figure 4
Extracted salivary gDNA from different saliva fractions. (a) Scatter plots for the quantity and quality of the extracted bacterial gDNA from each saliva fraction. Significant differences are denoted with *=p < 0.05, **=p < 0.01, ***=p < 0.001, ****=p < 0.0001 respectively. (b) Bacterial 16S rRNA and human β-globin qPCR Ct means distribution trend for the extracted gDNA from spit, drool and oral rinse.
Figure 5
Figure 5
Rarefaction curve for observed operational taxonomic units against sequences per sample for spit, drool and oral rinse.
Figure 6
Figure 6
Beta-diversity of salivary microbiome. Weighted (a) and unweighted (b) PCoA plot for spit, drool and oral rinse samples with respective adjacent plots emphasizing on the subjects in different representing colours.
Figure 7
Figure 7
Salivary microbiome genera redundancy analysis on different (a) saliva fractions and (b) subjects.

References

    1. na Head and Neck Cancer Biomarkers Detected in Saliva. Cancer Biol Ther4, 6–12 (2005).
    1. Pepe MS, et al. Phases of Biomarker Development for Early Detection of Cancer. J Natl Cancer Inst. 2001;93:1054–1061. doi: 10.1093/jnci/93.14.1054.
    1. Lucs AV, Saltman B, Chung CH, Steinberg BM, Schwartz DL. Opportunities and challenges facing biomarker development for personalized head and neck cancer treatment. Head Neck. 2013;35:294–306. doi: 10.1002/hed.21975.
    1. Bandhakavi S, Stone MD, Onsongo G, Van Riper SK, Griffin TJ. A dynamic range compression and three-dimensional peptide fractionation analysis platform expands proteome coverage and the diagnostic potential of whole saliva. J. Proteome Res. 2009;8:5590–5600. doi: 10.1021/pr900675w.
    1. Iorgulescu G. Saliva between normal and pathological. Important factors in determining systemic and oral health. J Med Life. 2009;2:303–307.
    1. Pfaffe T, Cooper-White J, Beyerlein P, Kostner K, Punyadeera C. Diagnostic potential of saliva: current state and future applications. Clin Chem. 2011;57:675–687. doi: 10.1373/clinchem.2010.153767.
    1. Malamud D. Saliva as a diagnostic fluid. Dent Clin North Am. 2011;55:159–178. doi: 10.1016/j.cden.2010.08.004.
    1. Chai RC, et al. A pilot study to compare the detection of HPV-16 biomarkers in salivary oral rinses with tumour p16INK4a expression in head and neck squamous cell carcinoma patients. BMC Cancer. 2016;16:1–8. doi: 10.1186/s12885-016-2217-1.
    1. Lim Y, et al. Salivary DNA methylation panel to diagnose HPV-positive and HPV-negative head and neck cancers. BMC Cancer. 2016;16:1–12. doi: 10.1186/s12885-015-2026-y.
    1. Ovchinnikov DA, et al. DNA Methylation at the Novel CpG Sites in the Promoter of MED15/PCQAP Gene as a Biomarker for Head and Neck Cancers. Biomark Insights. 2014;9:53–60.
    1. Ovchinnikov DA, et al. Tumor-suppressor Gene Promoter Hypermethylation in Saliva of Head and Neck Cancer Patients. Transl Oncol. 2012;5:321–326. doi: 10.1593/tlo.12232.
    1. Salazar C, et al. A novel saliva-based microRNA biomarker panel to detect head and neck cancers. Cell Oncol (Dordr) 2014;37:331–338. doi: 10.1007/s13402-014-0188-2.
    1. Zhang, X. et al. Quantification of D-dimer levels in human saliva. Bioanalysis5 (2013).
    1. Zhang X, Dimeski G, Punyadeera C. Validation of an immunoassay to measure plasminogen-activator inhibitor-1 concentrations in human saliva. Biochem Med. 2014;24:258–265. doi: 10.11613/BM.2014.028.
    1. Wong DT. Salivary extracellular noncoding RNA: emerging biomarkers for molecular diagnostics. Clin Ther. 2015;37:540–551. doi: 10.1016/j.clinthera.2015.02.017.
    1. Nunes LA, Mussavira S, Bindhu OS. Clinical and diagnostic utility of saliva as a non-invasive diagnostic fluid: a systematic review. Biochem Med (Zagreb) 2015;25:177–192. doi: 10.11613/BM.2015.018.
    1. Guerrero-Preston, R. et al. 16S rRNA amplicon sequencing identifies microbiota associated with oral cancer, Human Papilloma Virus infection and surgical treatment. Oncotarget (2016).
    1. Weidlich P, Cimões R, Pannuti CM, Oppermann RV. Association between periodontal diseases and systemic diseases. Braz Oral Res. 2008;22:32–43. doi: 10.1590/S1806-83242008000500006.
    1. Shacter, E. & Weitzman, S. A. Chronic inflammation and cancer. Oncology (Williston Park)16, 217–226, 229; discussion 230–212 (2002).
    1. Vogelmann R, Amieva MR. The role of bacterial pathogens in cancer. Curr Opin Microbiol. 2007;10:76–81. doi: 10.1016/j.mib.2006.12.004.
    1. Chen M, et al. The impact of different DNA extraction methods on the analysis of microbial diversity of oral saliva from healthy youths by polymerase chain reaction-denaturing gradient gel electrophoresis. JDS. 2016;11:54–58.
    1. Jenkinson HF. Beyond the oral microbiome. Environ Microbiol. 2011;13:3077–3087. doi: 10.1111/j.1462-2920.2011.02573.x.
    1. Shanahan ER, Zhong L, Talley NJ, Morrison M, Holtmann G. Characterisation of the gastrointestinal mucosa-associated microbiota: a novel technique to prevent cross-contamination during endoscopic procedures. Aliment Pharmacol Ther. 2016;43:1186–1196. doi: 10.1111/apt.13622.
    1. Haas BJ, et al. Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons. Genome Res. 2011;21:494–504. doi: 10.1101/gr.112730.110.
    1. McDonald D, et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 2012;6:610–618. doi: 10.1038/ismej.2011.139.
    1. Takeshita T, et al. Bacterial diversity in saliva and oral health-related conditions: the Hisayama Study. Sci Rep. 2016;6:22164. doi: 10.1038/srep22164.
    1. Dewhirst FE, et al. The human oral microbiome. J Bacteriol. 2010;192:5002–5017. doi: 10.1128/JB.00542-10.
    1. Lazarevic V, Gaia N, Girard M, Francois P, Schrenzel J. Comparison of DNA extraction methods in analysis of salivary bacterial communities. PLoS One. 2013;8:e67699. doi: 10.1371/journal.pone.0067699.
    1. Sohrabi M, et al. The yield and quality of cellular and bacterial DNA extracts from human oral rinse samples are variably affected by the cell lysis methodology. J Microbiol Methods. 2016;122:64–72. doi: 10.1016/j.mimet.2016.01.013.
    1. Vesty A, Biswas K, Taylor MW, Gear K, Douglas RG. Evaluating the Impact of DNA Extraction Method on the Representation of Human Oral Bacterial and Fungal Communities. PLoS ONE. 2017;12:e0169877. doi: 10.1371/journal.pone.0169877.
    1. Hull MW, Chow AW. Indigenous microflora and innate immunity of the head and neck. Infect Dis Clin North Am. 2007;21:265–282. doi: 10.1016/j.idc.2007.03.015.
    1. Taylan I, et al. Comparison of the Surface and Core Bacteria in Tonsillar and Adenoid Tissue With Beta-Lactamase Production. Indian J Otolaryngol Head Neck Surg. 2011;63:223–228. doi: 10.1007/s12070-011-0265-z.
    1. Danser MM, Gómez SM, Van der Weijden GA. Tongue coating and tongue brushing: a literature review. Int J Dent Hyg. 2003;1:151–158. doi: 10.1034/j.1601-5037.2003.00034.x.
    1. Bik EM, et al. Bacterial diversity in the oral cavity of ten healthy individuals. The ISME J. 2010;4:962–974. doi: 10.1038/ismej.2010.30.
    1. Ozga, A.T. et al. Oral microbiome diversity among Cheyenne and Arapaho individuals from Oklahoma. Am J Phys Anthropol (2016).

Source: PubMed

3
購読する