+1 Recommend
0 collections
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Study of inter- and intra-individual variations in the salivary microbiota

      Read this article at

          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.



          Oral bacterial communities contain species that promote health and others that have been implicated in oral and/or systemic diseases. Culture-independent approaches provide the best means to assess the diversity of oral bacteria because most of them remain uncultivable.


          The salivary microbiota from five adults was analyzed at three time-points by means of the 454 pyrosequencing technology. The V1-V3 region of the bacterial 16S rRNA genes was amplified by PCR using saliva lysates and broad-range primers. The bar-coded PCR products were pooled and sequenced unidirectionally to cover the V3 hypervariable region. Of 50,708 obtained sequences, 31,860 passed the quality control. Non-bacterial sequences (2.2%) were removed leaving 31,170 reads. Samples were dominated by seven major phyla: members of Firmicutes, Proteobacteria, Actinobacteria, Bacteroidetes and candidate division TM7 were identified in all samples; Fusobacteria and Spirochaetes were identified in all individuals, but not at all time-points. The dataset was represented by 3,011 distinct sequences (100%-ID phylotypes) of ~215 nucleotides and 583 phylotypes defined at ≥97% identity (97%-ID phylotypes). We compared saliva samples from different individuals in terms of the phylogeny of their microbial communities. Based on the presence and absence of phylotypes defined at 100% or 97% identity thresholds, samples from each subject formed separate clusters. Among individual taxa, phylum Bacteroidetes and order Clostridiales (Firmicutes) were the best indicators of intraindividual similarity of the salivary flora over time. Fifteen out of 81 genera constituted 73 to 94% of the total sequences present in different samples. Of these, 8 were shared by all time points of all individuals, while 15-25 genera were present in all three time-points of different individuals. Representatives of the class Sphingobacteria, order Sphingobacteriales and family Clostridiaceae were found only in one subject.


          The salivary microbial community appeared to be stable over at least 5 days, allowing for subject-specific grouping using UniFrac. Inclusion of all available samples from more distant time points (up to 29 days) confirmed this observation. Samples taken at closer time intervals were not necessarily more similar than samples obtained across longer sampling times. These results point to the persistence of subject-specific taxa whose frequency fluctuates between the time points. Genus Gemella, identified in all time-points of all individuals, was not defined as a core-microbiome genus in previous studies of salivary bacterial communities. Human oral microbiome studies are still in their infancy and larger-scale projects are required to better define individual and universal oral microbiome core.

          Related collections

          Most cited references 30

          • Record: found
          • Abstract: found
          • Article: not found

          MUSCLE: multiple sequence alignment with high accuracy and high throughput.

           Robert Edgar (2004)
          We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.
            • Record: found
            • Abstract: found
            • Article: not found

            Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy.

            The Ribosomal Database Project (RDP) Classifier, a naïve Bayesian classifier, can rapidly and accurately classify bacterial 16S rRNA sequences into the new higher-order taxonomy proposed in Bergey's Taxonomic Outline of the Prokaryotes (2nd ed., release 5.0, Springer-Verlag, New York, NY, 2004). It provides taxonomic assignments from domain to genus, with confidence estimates for each assignment. The majority of classifications (98%) were of high estimated confidence (> or = 95%) and high accuracy (98%). In addition to being tested with the corpus of 5,014 type strain sequences from Bergey's outline, the RDP Classifier was tested with a corpus of 23,095 rRNA sequences as assigned by the NCBI into their alternative higher-order taxonomy. The results from leave-one-out testing on both corpora show that the overall accuracies at all levels of confidence for near-full-length and 400-base segments were 89% or above down to the genus level, and the majority of the classification errors appear to be due to anomalies in the current taxonomies. For shorter rRNA segments, such as those that might be generated by pyrosequencing, the error rate varied greatly over the length of the 16S rRNA gene, with segments around the V2 and V4 variable regions giving the lowest error rates. The RDP Classifier is suitable both for the analysis of single rRNA sequences and for the analysis of libraries of thousands of sequences. Another related tool, RDP Library Compare, was developed to facilitate microbial-community comparison based on 16S rRNA gene sequence libraries. It combines the RDP Classifier with a statistical test to flag taxa differentially represented between samples. The RDP Classifier and RDP Library Compare are available online at
              • Record: found
              • Abstract: not found
              • Article: not found

              MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform.

              A multiple sequence alignment program, MAFFT, has been developed. The CPU time is drastically reduced as compared with existing methods. MAFFT includes two novel techniques. (i) Homo logous regions are rapidly identified by the fast Fourier transform (FFT), in which an amino acid sequence is converted to a sequence composed of volume and polarity values of each amino acid residue. (ii) We propose a simplified scoring system that performs well for reducing CPU time and increasing the accuracy of alignments even for sequences having large insertions or extensions as well as distantly related sequences of similar length. Two different heuristics, the progressive method (FFT-NS-2) and the iterative refinement method (FFT-NS-i), are implemented in MAFFT. The performances of FFT-NS-2 and FFT-NS-i were compared with other methods by computer simulations and benchmark tests; the CPU time of FFT-NS-2 is drastically reduced as compared with CLUSTALW with comparable accuracy. FFT-NS-i is over 100 times faster than T-COFFEE, when the number of input sequences exceeds 60, without sacrificing the accuracy.

                Author and article information

                [1 ]Genomic Research Laboratory, Geneva University Hospitals, Rue Gabrielle-Perret-Gentil 4, CH-1211 Geneva 14, Switzerland
                BMC Genomics
                BMC Genomics
                BioMed Central
                28 September 2010
                : 11
                : 523
                Copyright ©2010 Lazarevic et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Research Article



                Comment on this article