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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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Interrelations between bacterial and host-associated variables and correlation to bacterial species/phylotype diversity in GCF samples.A multivariate PLS modeling was used for data analysis. Loading scatter plot (Panel A) displays the correlation structure of the variables (X variables: N = 166; Y variable: Number of different bacterial species/phylotypes in GCF samples). A number code was given for each bacterial taxon in their alphabetical order (Panel A) (the key is shown in online Supporting information Table S1). PLS regression coefficient plot (Panel B) identified X variables with statistically significant correlation to Y. The coefficients (>0.02 or <−0.02 are shown) are statistically significant when the error bars do not cross the 0 line. The model explained 98% and, according to cross validation, predicted 79% of the variation in Y. Observed values vs. predicted values R2 = 0.975 (Panel C). Capital letters are patient identifiers (Panel C). X variables were scaled and centered and Y variable scaled. The confidence intervals were derived from jack knifing. GCF: gingival crevicular fluid.
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pone-0013589-g002: Interrelations between bacterial and host-associated variables and correlation to bacterial species/phylotype diversity in GCF samples.A multivariate PLS modeling was used for data analysis. Loading scatter plot (Panel A) displays the correlation structure of the variables (X variables: N = 166; Y variable: Number of different bacterial species/phylotypes in GCF samples). A number code was given for each bacterial taxon in their alphabetical order (Panel A) (the key is shown in online Supporting information Table S1). PLS regression coefficient plot (Panel B) identified X variables with statistically significant correlation to Y. The coefficients (>0.02 or <−0.02 are shown) are statistically significant when the error bars do not cross the 0 line. The model explained 98% and, according to cross validation, predicted 79% of the variation in Y. Observed values vs. predicted values R2 = 0.975 (Panel C). Capital letters are patient identifiers (Panel C). X variables were scaled and centered and Y variable scaled. The confidence intervals were derived from jack knifing. GCF: gingival crevicular fluid.

Mentions: A phylogenetic analysis of the bacterial species identified in GCF samples revealed 8 bacterial phyla/candidate phyla, Firmicutes, Bacteroidetes, Fusobacteria, Proteobacteria, Actinobacteria, Spirochaetes, Synergistetes and TM7 (Figure 1). The number of different bacterial species (species diversity) in GCF samples ranged from 9 to 62 (mean 33.7, SD 15.3) per patient. In order to delineate which of the bacterial and host-associated study variables contributed to the species diversity per sample, we generated a multivariate PLS (Projection to latent structures by means of partial least squares) model using 166 X variables (133 bacterial species and 33 host-associated variables) and one Y variable (number of different species per sample) (Figure 2A, B, C). The loading scatter plot (Figure 2A) gives an overview of all variables; the position of each X variable shows its relationship to other X variables but also to the Y variable. The bacterial species are shown as numbers for which the key is given in online Supporting information, Table S1. The observed vs predicted values for the number of different species per sample demonstrated a good fit for the model (R2 = 0.975). According to cross validation the model predicted 79% of the variation in Y.


Specified species in gingival crevicular fluid predict bacterial diversity.

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

Interrelations between bacterial and host-associated variables and correlation to bacterial species/phylotype diversity in GCF samples.A multivariate PLS modeling was used for data analysis. Loading scatter plot (Panel A) displays the correlation structure of the variables (X variables: N = 166; Y variable: Number of different bacterial species/phylotypes in GCF samples). A number code was given for each bacterial taxon in their alphabetical order (Panel A) (the key is shown in online Supporting information Table S1). PLS regression coefficient plot (Panel B) identified X variables with statistically significant correlation to Y. The coefficients (>0.02 or <−0.02 are shown) are statistically significant when the error bars do not cross the 0 line. The model explained 98% and, according to cross validation, predicted 79% of the variation in Y. Observed values vs. predicted values R2 = 0.975 (Panel C). Capital letters are patient identifiers (Panel C). X variables were scaled and centered and Y variable scaled. The confidence intervals were derived from jack knifing. GCF: gingival crevicular fluid.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2963608&req=5

pone-0013589-g002: Interrelations between bacterial and host-associated variables and correlation to bacterial species/phylotype diversity in GCF samples.A multivariate PLS modeling was used for data analysis. Loading scatter plot (Panel A) displays the correlation structure of the variables (X variables: N = 166; Y variable: Number of different bacterial species/phylotypes in GCF samples). A number code was given for each bacterial taxon in their alphabetical order (Panel A) (the key is shown in online Supporting information Table S1). PLS regression coefficient plot (Panel B) identified X variables with statistically significant correlation to Y. The coefficients (>0.02 or <−0.02 are shown) are statistically significant when the error bars do not cross the 0 line. The model explained 98% and, according to cross validation, predicted 79% of the variation in Y. Observed values vs. predicted values R2 = 0.975 (Panel C). Capital letters are patient identifiers (Panel C). X variables were scaled and centered and Y variable scaled. The confidence intervals were derived from jack knifing. GCF: gingival crevicular fluid.
Mentions: A phylogenetic analysis of the bacterial species identified in GCF samples revealed 8 bacterial phyla/candidate phyla, Firmicutes, Bacteroidetes, Fusobacteria, Proteobacteria, Actinobacteria, Spirochaetes, Synergistetes and TM7 (Figure 1). The number of different bacterial species (species diversity) in GCF samples ranged from 9 to 62 (mean 33.7, SD 15.3) per patient. In order to delineate which of the bacterial and host-associated study variables contributed to the species diversity per sample, we generated a multivariate PLS (Projection to latent structures by means of partial least squares) model using 166 X variables (133 bacterial species and 33 host-associated variables) and one Y variable (number of different species per sample) (Figure 2A, B, C). The loading scatter plot (Figure 2A) gives an overview of all variables; the position of each X variable shows its relationship to other X variables but also to the Y variable. The bacterial species are shown as numbers for which the key is given in online Supporting information, Table S1. The observed vs predicted values for the number of different species per sample demonstrated a good fit for the model (R2 = 0.975). According to cross validation the model predicted 79% of the variation in Y.

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