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Specified species in gingival crevicular fluid predict bacterial diversity.

Asikainen S, Doğan B, Turgut Z, Paster BJ, Bodur A, Oscarsson J - PLoS ONE (2010)

Bottom Line: PLS regression analysis showed that species of genera including Campylobacter, Selenomonas, Porphyromonas, Catonella, Tannerella, Dialister, Peptostreptococcus, Streptococcus and Eubacterium had significant positive correlations and the number of teeth with low-grade attachment loss a significant negative correlation to species diversity in GCF samples.OPLS/O2PLS discriminant analysis revealed significant positive correlations to GCF sample group membership for species of genera Campylobacter, Leptotrichia, Prevotella, Dialister, Tannerella, Haemophilus, Fusobacterium, Eubacterium, and Actinomyces.Among a variety of detected species those traditionally classified as Gram-negative anaerobes growing in mature subgingival biofilms were the main predictors for species diversity in GCF samples as well as responsible for distinguishing GCF samples from PP samples.

View Article: PubMed Central - PubMed

Affiliation: Section of Oral Microbiology, Institute of Odontology, Umeå University, Umeå, Sweden. sirkka.asikainen@odont.umu.se

ABSTRACT

Background: Analysis of gingival crevicular fluid (GCF) samples may give information of unattached (planktonic) subgingival bacteria. Our study represents the first one targeting the identity of bacteria in GCF.

Methodology/principal findings: We determined bacterial species diversity in GCF samples of a group of periodontitis patients and delineated contributing bacterial and host-associated factors. Subgingival paper point (PP) samples from the same sites were taken for comparison. After DNA extraction, 16S rRNA genes were PCR amplified and DNA-DNA hybridization was performed using a microarray for over 300 bacterial species or groups. Altogether 133 species from 41 genera and 8 phyla were detected with 9 to 62 and 18 to 64 species in GCF and PP samples, respectively, per patient. Projection to latent structures by means of partial least squares (PLS) was applied to the multivariate data analysis. PLS regression analysis showed that species of genera including Campylobacter, Selenomonas, Porphyromonas, Catonella, Tannerella, Dialister, Peptostreptococcus, Streptococcus and Eubacterium had significant positive correlations and the number of teeth with low-grade attachment loss a significant negative correlation to species diversity in GCF samples. OPLS/O2PLS discriminant analysis revealed significant positive correlations to GCF sample group membership for species of genera Campylobacter, Leptotrichia, Prevotella, Dialister, Tannerella, Haemophilus, Fusobacterium, Eubacterium, and Actinomyces.

Conclusions/significance: Among a variety of detected species those traditionally classified as Gram-negative anaerobes growing in mature subgingival biofilms were the main predictors for species diversity in GCF samples as well as responsible for distinguishing GCF samples from PP samples. GCF bacteria may provide new prospects for studying dynamic properties of subgingival biofilms.

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Bacterial species/phylotypes responsible for the difference in bacterial composition between GCF samples and PP samples.Multivariate OPLS/2OPLS-DA model was generated for 2 classes. The score plot (Panel A) demonstrates the relationship between the 2 types of samples and between the patients. Capital letters are patient identifiers, “G” (black color) for GCF samples and “P” (red color) for PP samples. The X variables included 133 species/phylotypes identified by DNA microarray. The binary Y variable defines the group membership to GCF or PP samples. Ellipse: Hotelling T2 (0.95). PLS regression analysis (Panel B) indicated the extent each variable positively or negatively contributed to GCF sample membership and vice versa to PP sample membership. The coefficients are statistically significant when the error bars do not cross the 0 line. The model explained 99% of the variation of Y and, according to cross validation, predicted 70% of the variation of Y. X variables were scaled and centered and Y variables scaled. The confidence intervals (95%) were derived from jack knifing. GCF: gingival crevicular fluid; PP: paper point.
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pone-0013589-g006: Bacterial species/phylotypes responsible for the difference in bacterial composition between GCF samples and PP samples.Multivariate OPLS/2OPLS-DA model was generated for 2 classes. The score plot (Panel A) demonstrates the relationship between the 2 types of samples and between the patients. Capital letters are patient identifiers, “G” (black color) for GCF samples and “P” (red color) for PP samples. The X variables included 133 species/phylotypes identified by DNA microarray. The binary Y variable defines the group membership to GCF or PP samples. Ellipse: Hotelling T2 (0.95). PLS regression analysis (Panel B) indicated the extent each variable positively or negatively contributed to GCF sample membership and vice versa to PP sample membership. The coefficients are statistically significant when the error bars do not cross the 0 line. The model explained 99% of the variation of Y and, according to cross validation, predicted 70% of the variation of Y. X variables were scaled and centered and Y variables scaled. The confidence intervals (95%) were derived from jack knifing. GCF: gingival crevicular fluid; PP: paper point.

Mentions: A multivariate data analysis approach, quantitative OPLS/O2PLS-DA modeling, was applied to further examine differences in the bacterial species/phylotype composition between GCF and PP samples. In the model generated for 2 classes, all 133 bacterial species/phylotypes found in the GCF and PP samples were used as X variables (Figure 6). The model explained 99% of the variation related to the 2 sample groups. The score plot shows that the model clearly separated the GCF and PP samples (Figure 6A). Regression coefficients >0.02 or <−0.02 delineated the most important species/phylotypes (N = 34 and N = 38, respectively) responsible for the difference between GCF and PP sample groups (Figure 6B), the positive coefficient values signifying GCF samples and the negative values, PP samples. Among the species significantly contributing to the sample group separation, species/taxonomic groups traditionally classified to Gram-negative species were more frequent in GCF (10/12 [83%]) than PP samples (4/15 [27%]) (Figure 6B). The species/phylotypes with significant positive regression coefficients, thus representing the strongest contributors to GCF sample membership, included members of 5 phyla: Bacteroidetes (Prevotella nigrescens, Tannerella forsythia, Bacteroidetes G1 sp X083_ot272_X17, Bacteroidetes G1 sp X083_ot272_AA81), Proteobacteria (Campylobacter rectus/concisus, C. concisus, Haemophilus sp BJ095), Fusobacteria (Fusobacterium nucleatum ss polymorphum, Leptotrichia hofstadii FAC5_ot224_AA58), Firmicutes (Dialister pneumosintes, Peptostreptococcaceae[11][G-7] BB142 sp_ot081) and Actinobacteria (Actinomyces Cluster I). Species/phylotypes or groups with significant negative regression coefficients, thus representing the strongest contributors to PP sample membership, also belonged to 5 phyla: Firmicutes (Streptococcus Cluster III, S. intermedius/anginosus, S. anginosus/intermedius, S. intermedius/constellatus, S. parasanguinis, S. cristatus/sp BM035 ot058, Eubacterium yurii, Eubacterium saphenum, Eubacterium brachy, Pseudoramibacter alactolyticus and Shuttleworthia satelles), Proteobacteria (Kingella oralis), Fusobacteria (L. hofstadii FAC5_ot224_Y55), Bacteroidetes (Bacteroidetes sp clone AU126_ot274) and Spirochaetes (Treponema sp AU076 ot242) (Figure 5B).


Specified species in gingival crevicular fluid predict bacterial diversity.

Asikainen S, Doğan B, Turgut Z, Paster BJ, Bodur A, Oscarsson J - PLoS ONE (2010)

Bacterial species/phylotypes responsible for the difference in bacterial composition between GCF samples and PP samples.Multivariate OPLS/2OPLS-DA model was generated for 2 classes. The score plot (Panel A) demonstrates the relationship between the 2 types of samples and between the patients. Capital letters are patient identifiers, “G” (black color) for GCF samples and “P” (red color) for PP samples. The X variables included 133 species/phylotypes identified by DNA microarray. The binary Y variable defines the group membership to GCF or PP samples. Ellipse: Hotelling T2 (0.95). PLS regression analysis (Panel B) indicated the extent each variable positively or negatively contributed to GCF sample membership and vice versa to PP sample membership. The coefficients are statistically significant when the error bars do not cross the 0 line. The model explained 99% of the variation of Y and, according to cross validation, predicted 70% of the variation of Y. X variables were scaled and centered and Y variables scaled. The confidence intervals (95%) were derived from jack knifing. GCF: gingival crevicular fluid; PP: paper point.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2963608&req=5

pone-0013589-g006: Bacterial species/phylotypes responsible for the difference in bacterial composition between GCF samples and PP samples.Multivariate OPLS/2OPLS-DA model was generated for 2 classes. The score plot (Panel A) demonstrates the relationship between the 2 types of samples and between the patients. Capital letters are patient identifiers, “G” (black color) for GCF samples and “P” (red color) for PP samples. The X variables included 133 species/phylotypes identified by DNA microarray. The binary Y variable defines the group membership to GCF or PP samples. Ellipse: Hotelling T2 (0.95). PLS regression analysis (Panel B) indicated the extent each variable positively or negatively contributed to GCF sample membership and vice versa to PP sample membership. The coefficients are statistically significant when the error bars do not cross the 0 line. The model explained 99% of the variation of Y and, according to cross validation, predicted 70% of the variation of Y. X variables were scaled and centered and Y variables scaled. The confidence intervals (95%) were derived from jack knifing. GCF: gingival crevicular fluid; PP: paper point.
Mentions: A multivariate data analysis approach, quantitative OPLS/O2PLS-DA modeling, was applied to further examine differences in the bacterial species/phylotype composition between GCF and PP samples. In the model generated for 2 classes, all 133 bacterial species/phylotypes found in the GCF and PP samples were used as X variables (Figure 6). The model explained 99% of the variation related to the 2 sample groups. The score plot shows that the model clearly separated the GCF and PP samples (Figure 6A). Regression coefficients >0.02 or <−0.02 delineated the most important species/phylotypes (N = 34 and N = 38, respectively) responsible for the difference between GCF and PP sample groups (Figure 6B), the positive coefficient values signifying GCF samples and the negative values, PP samples. Among the species significantly contributing to the sample group separation, species/taxonomic groups traditionally classified to Gram-negative species were more frequent in GCF (10/12 [83%]) than PP samples (4/15 [27%]) (Figure 6B). The species/phylotypes with significant positive regression coefficients, thus representing the strongest contributors to GCF sample membership, included members of 5 phyla: Bacteroidetes (Prevotella nigrescens, Tannerella forsythia, Bacteroidetes G1 sp X083_ot272_X17, Bacteroidetes G1 sp X083_ot272_AA81), Proteobacteria (Campylobacter rectus/concisus, C. concisus, Haemophilus sp BJ095), Fusobacteria (Fusobacterium nucleatum ss polymorphum, Leptotrichia hofstadii FAC5_ot224_AA58), Firmicutes (Dialister pneumosintes, Peptostreptococcaceae[11][G-7] BB142 sp_ot081) and Actinobacteria (Actinomyces Cluster I). Species/phylotypes or groups with significant negative regression coefficients, thus representing the strongest contributors to PP sample membership, also belonged to 5 phyla: Firmicutes (Streptococcus Cluster III, S. intermedius/anginosus, S. anginosus/intermedius, S. intermedius/constellatus, S. parasanguinis, S. cristatus/sp BM035 ot058, Eubacterium yurii, Eubacterium saphenum, Eubacterium brachy, Pseudoramibacter alactolyticus and Shuttleworthia satelles), Proteobacteria (Kingella oralis), Fusobacteria (L. hofstadii FAC5_ot224_Y55), Bacteroidetes (Bacteroidetes sp clone AU126_ot274) and Spirochaetes (Treponema sp AU076 ot242) (Figure 5B).

Bottom Line: PLS regression analysis showed that species of genera including Campylobacter, Selenomonas, Porphyromonas, Catonella, Tannerella, Dialister, Peptostreptococcus, Streptococcus and Eubacterium had significant positive correlations and the number of teeth with low-grade attachment loss a significant negative correlation to species diversity in GCF samples.OPLS/O2PLS discriminant analysis revealed significant positive correlations to GCF sample group membership for species of genera Campylobacter, Leptotrichia, Prevotella, Dialister, Tannerella, Haemophilus, Fusobacterium, Eubacterium, and Actinomyces.Among a variety of detected species those traditionally classified as Gram-negative anaerobes growing in mature subgingival biofilms were the main predictors for species diversity in GCF samples as well as responsible for distinguishing GCF samples from PP samples.

View Article: PubMed Central - PubMed

Affiliation: Section of Oral Microbiology, Institute of Odontology, Umeå University, Umeå, Sweden. sirkka.asikainen@odont.umu.se

ABSTRACT

Background: Analysis of gingival crevicular fluid (GCF) samples may give information of unattached (planktonic) subgingival bacteria. Our study represents the first one targeting the identity of bacteria in GCF.

Methodology/principal findings: We determined bacterial species diversity in GCF samples of a group of periodontitis patients and delineated contributing bacterial and host-associated factors. Subgingival paper point (PP) samples from the same sites were taken for comparison. After DNA extraction, 16S rRNA genes were PCR amplified and DNA-DNA hybridization was performed using a microarray for over 300 bacterial species or groups. Altogether 133 species from 41 genera and 8 phyla were detected with 9 to 62 and 18 to 64 species in GCF and PP samples, respectively, per patient. Projection to latent structures by means of partial least squares (PLS) was applied to the multivariate data analysis. PLS regression analysis showed that species of genera including Campylobacter, Selenomonas, Porphyromonas, Catonella, Tannerella, Dialister, Peptostreptococcus, Streptococcus and Eubacterium had significant positive correlations and the number of teeth with low-grade attachment loss a significant negative correlation to species diversity in GCF samples. OPLS/O2PLS discriminant analysis revealed significant positive correlations to GCF sample group membership for species of genera Campylobacter, Leptotrichia, Prevotella, Dialister, Tannerella, Haemophilus, Fusobacterium, Eubacterium, and Actinomyces.

Conclusions/significance: Among a variety of detected species those traditionally classified as Gram-negative anaerobes growing in mature subgingival biofilms were the main predictors for species diversity in GCF samples as well as responsible for distinguishing GCF samples from PP samples. GCF bacteria may provide new prospects for studying dynamic properties of subgingival biofilms.

Show MeSH
Related in: MedlinePlus