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      Comparison of oral microbiota in tumor and non-tumor tissues of patients with oral squamous cell carcinoma

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          Abstract

          Background

          Bacterial infections have been linked to malignancies due to their ability to induce chronic inflammation. We investigated the association of oral bacteria in oral squamous cell carcinoma (OSCC/tumor) tissues and compared with adjacent non-tumor mucosa sampled 5 cm distant from the same patient ( n = 10). By using culture-independent 16S rRNA approaches, denaturing gradient gel electrophoresis (DGGE) and cloning and sequencing, we assessed the total bacterial diversity in these clinical samples.

          Results

          DGGE fingerprints showed variations in the band intensity profiles within non-tumor and tumor tissues of the same patient and among the two groups. The clonal analysis indicated that from a total of 1200 sequences characterized, 80 bacterial species/phylotypes were detected representing six phyla, Firmicutes, Bacteroidetes, Proteobacteria, Fusobacteria, Actinobacteria and uncultivated TM7 in non-tumor and tumor libraries. In combined library, 12 classes, 16 order, 26 families and 40 genera were observed. Bacterial species, Streptococcus sp. oral taxon 058, Peptostreptococcus stomatis, Streptococcus salivarius, Streptococcus gordonii, Gemella haemolysans, Gemella morbillorum, Johnsonella ignava and Streptococcus parasanguinis I were highly associated with tumor site where as Granulicatella adiacens was prevalent at non-tumor site. Streptococcus intermedius was present in 70% of both non-tumor and tumor sites.

          Conclusions

          The underlying changes in the bacterial diversity in the oral mucosal tissues from non-tumor and tumor sites of OSCC subjects indicated a shift in bacterial colonization. These most prevalent or unique bacterial species/phylotypes present in tumor tissues may be associated with OSCC and needs to be further investigated with a larger sample size.

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          Most cited references 74

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          Bellerophon: a program to detect chimeric sequences in multiple sequence alignments.

          Bellerophon is a program for detecting chimeric sequences in multiple sequence datasets by an adaption of partial treeing analysis. Bellerophon was specifically developed to detect 16S rRNA gene chimeras in PCR-clone libraries of environmental samples but can be applied to other nucleotide sequence alignments. Bellerophon is available as an interactive web server at http://foo.maths.uq.edu.au/~huber/bellerophon.pl
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            Defining the healthy "core microbiome" of oral microbial communities

            Background Most studies examining the commensal human oral microbiome are focused on disease or are limited in methodology. In order to diagnose and treat diseases at an early and reversible stage an in-depth definition of health is indispensible. The aim of this study therefore was to define the healthy oral microbiome using recent advances in sequencing technology (454 pyrosequencing). Results We sampled and sequenced microbiomes from several intraoral niches (dental surfaces, cheek, hard palate, tongue and saliva) in three healthy individuals. Within an individual oral cavity, we found over 3600 unique sequences, over 500 different OTUs or "species-level" phylotypes (sequences that clustered at 3% genetic difference) and 88 - 104 higher taxa (genus or more inclusive taxon). The predominant taxa belonged to Firmicutes (genus Streptococcus, family Veillonellaceae, genus Granulicatella), Proteobacteria (genus Neisseria, Haemophilus), Actinobacteria (genus Corynebacterium, Rothia, Actinomyces), Bacteroidetes (genus Prevotella, Capnocytophaga, Porphyromonas) and Fusobacteria (genus Fusobacterium). Each individual sample harboured on average 266 "species-level" phylotypes (SD 67; range 123 - 326) with cheek samples being the least diverse and the dental samples from approximal surfaces showing the highest diversity. Principal component analysis discriminated the profiles of the samples originating from shedding surfaces (mucosa of tongue, cheek and palate) from the samples that were obtained from solid surfaces (teeth). There was a large overlap in the higher taxa, "species-level" phylotypes and unique sequences among the three microbiomes: 84% of the higher taxa, 75% of the OTUs and 65% of the unique sequences were present in at least two of the three microbiomes. The three individuals shared 1660 of 6315 unique sequences. These 1660 sequences (the "core microbiome") contributed 66% of the reads. The overlapping OTUs contributed to 94% of the reads, while nearly all reads (99.8%) belonged to the shared higher taxa. Conclusions We obtained the first insight into the diversity and uniqueness of individual oral microbiomes at a resolution of next-generation sequencing. We showed that a major proportion of bacterial sequences of unrelated healthy individuals is identical, supporting the concept of a core microbiome at health.
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              NAST: a multiple sequence alignment server for comparative analysis of 16S rRNA genes

              Microbiologists conducting surveys of bacterial and archaeal diversity often require comparative alignments of thousands of 16S rRNA genes collected from a sample. The computational resources and bioinformatics expertise required to construct such an alignment has inhibited high-throughput analysis. It was hypothesized that an online tool could be developed to efficiently align thousands of 16S rRNA genes via the NAST (Nearest Alignment Space Termination) algorithm for creating multiple sequence alignments (MSA). The tool was implemented with a web-interface at . Each user-submitted sequence is compared with Greengenes' ‘Core Set’, comprising ∼10 000 aligned non-chimeric sequences representative of the currently recognized diversity among bacteria and archaea. User sequences are oriented and paired with their closest match in the Core Set to serve as a template for inserting gap characters. Non-16S data (sequence from vector or surrounding genomic regions) are conveniently removed in the returned alignment. From the resulting MSA, distance matrices can be calculated for diversity estimates and organisms can be classified by taxonomy. The ability to align and categorize large sequence sets using a simple interface has enabled researchers with various experience levels to obtain bacterial and archaeal community profiles.
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                Author and article information

                Journal
                BMC Microbiol
                BMC Microbiol
                BMC Microbiology
                BioMed Central
                1471-2180
                2012
                20 July 2012
                : 12
                : 144
                Affiliations
                [1 ]Department of Basic Sciences and Craniofacial Biology, New York University College of Dentistry, 345 E, 24th Street, Room 921B, New York, NY, 10010, USA
                [2 ]Department of Chemical and Biological Sciences, Polytechnic Institute of NYU, New York, NY, USA
                [3 ]Dental Service, Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
                [4 ]Laboratory of Epithelial Cancer Biology, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
                Article
                1471-2180-12-144
                10.1186/1471-2180-12-144
                3507910
                22817758
                Copyright ©2012 Pushalkar et al.; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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                Research Article

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