| Primary information |
|---|
| SALID | SAL_21657 |
| Biomarker name | Capnocytophaga sputigena |
| Biomarker Type | NA |
| Sampling Method | Age >27 |
| Collection Method | Unstimulated saliva was collected by allowing saliva to flow freely for 5 min over a polypropylene tube. Saliva samples were immediately centrifuged at 6000 g and pellets were stored at -80 degree C until processed further |
| Analysis Method | Sequencing of 16S ribosomal RNA gene amplicons |
| Collection Site | Whole Saliva |
| Disease Category | Healthy |
| Disease/Condition | Healthy |
| Disease Subtype | NA |
| Fold Change/ Concentration | NA |
| Up/Downregulated | NA |
| Exosomal | NA |
| Organism | Homo sapiens |
| PMID | 22520388 |
| Year of Publication | 2012 |
| Biomarker ID | 1019 |
| Biomarker Category | Microbe |
| Sequence | NG_050141.1 |
| Title of study | Using high throughput sequencing to explore the biodiversity in oral bacterial communities |
| Abstract of study | High throughput sequencing of 16S ribosomal RNA gene amplicons is a cost-effective method for characterization of oral bacterial communities. However, before undertaking large-scale studies, it is necessary to understand the technique-associated limitations and intrinsic variability of the oral ecosystem. In this work we evaluated bias in species representation using an in vitro-assembled mock community of oral bacteria. We then characterized the bacterial communities in saliva and buccal mucosa of five healthy subjects to investigate the power of high throughput sequencing in revealing their diversity and biogeography patterns. Mock community analysis showed primer and DNA isolation biases and an overestimation of diversity that was reduced after eliminating singleton operational taxonomic units (OTUs). Sequencing of salivary and mucosal communities found a total of 455 OTUs (0.3% dissimilarity) with only 78 of these present in all subjects. We demonstrate that this variability was partly the result of incomplete richness coverage even at great sequencing depths, and so comparing communities by their structure was more effective than comparisons based solely on membership. With respect to oral biogeography, we found inter-subject variability in community structure was lower than site differences between salivary and mucosal communities within subjects. These differences were evident at very low sequencing depths and were mostly caused by the abundance of Streptococcus mitis and Gemella haemolysans in mucosa. In summary, we present an experimental and data analysis framework that will facilitate design and interpretation of pyrosequencing-based studies. Despite challenges associated with this technique, we demonstrate its power for evaluation of oral diversity and biogeography patterns. |