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Quorum-sensing dysbiotic shifts in the HIV-infected oral metabiome.

Brown RE, Ghannoum MA, Mukherjee PK, Gillevet PM, Sikaroodi M - PLoS ONE (2015)

Bottom Line: We implemented a Systems Biology approach using Correlation Difference Probability Network (CDPN) analysis to provide insights into the statistically significant functional differences between HIV-infected patients and uninfected individuals.CDPN highlights the taxa-metabolite-taxa differences between the cohorts that frequently capture quorum-sensing modifications that reflect communication disruptions in the dysbiotic HIV cohort.The results also highlight the significant role of cyclic mono and dipeptides as quorum-sensing (QS) mediators between oral bacteria and fungal genus.

View Article: PubMed Central - PubMed

Affiliation: School of Systems Biology, George Mason University, Prince William County, Fairfax, VA, United States of America.

ABSTRACT
We implemented a Systems Biology approach using Correlation Difference Probability Network (CDPN) analysis to provide insights into the statistically significant functional differences between HIV-infected patients and uninfected individuals. The analysis correlates bacterial microbiome ("bacteriome"), fungal microbiome ("mycobiome"), and metabolome data to model the underlying biological processes comprising the Human Oral Metabiome. CDPN highlights the taxa-metabolite-taxa differences between the cohorts that frequently capture quorum-sensing modifications that reflect communication disruptions in the dysbiotic HIV cohort. The results also highlight the significant role of cyclic mono and dipeptides as quorum-sensing (QS) mediators between oral bacteria and fungal genus. The developed CDPN approach allowed us to model the interactions of taxa and key metabolites, and hypothesize their possible contribution to the etiology of Oral Candidiasis (OC).

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Related in: MedlinePlus

Correlation networks for Control and HIV cohorts.The legend is common for figures A, B and D: The correlation network represents features that are linked with a correlation coefficient greater than 0.6 (negative or positive) and with a p value <0.05. Parallelograms represent bacterial taxa, ellipses represent fungi and diamond shapes represent metabolites. Red edges represent a negative correlation between connected nodes and blue edges present positive correlations. A: Control correlation network with r. >0.6 or r.< -0.6 and p value <0.05. B: correlation network with +.0.6 < rho <-0.6 and p value <0.05. C: Cumulative Distribution Function D: Is the difference network map of 1A and 1B. It depicts those nodes that have a correlation with 0.6 < rho <-0.6 in either the control or HIV cohort, but not both.
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pone.0123880.g001: Correlation networks for Control and HIV cohorts.The legend is common for figures A, B and D: The correlation network represents features that are linked with a correlation coefficient greater than 0.6 (negative or positive) and with a p value <0.05. Parallelograms represent bacterial taxa, ellipses represent fungi and diamond shapes represent metabolites. Red edges represent a negative correlation between connected nodes and blue edges present positive correlations. A: Control correlation network with r. >0.6 or r.< -0.6 and p value <0.05. B: correlation network with +.0.6 < rho <-0.6 and p value <0.05. C: Cumulative Distribution Function D: Is the difference network map of 1A and 1B. It depicts those nodes that have a correlation with 0.6 < rho <-0.6 in either the control or HIV cohort, but not both.

Mentions: We calculated Pearson correlations for the oral metabiome (metabolites, bacterial and fungal microbiome) that included 295 identifiable metabolites, fungi, and bacteria S2 Table. The analysis for the 295 features for both the Control cohort and the HIV cohort identified all significant correlations (rho >+0.6 or rho <-0.6). This subset of all correlations identified 2,681 Control correlations Fig 1A, and 4,240 HIV oral metabiome correlations Fig 1B. One fungus, Pichia, was only present in Control samples, and completely absent in the HIV samples.


Quorum-sensing dysbiotic shifts in the HIV-infected oral metabiome.

Brown RE, Ghannoum MA, Mukherjee PK, Gillevet PM, Sikaroodi M - PLoS ONE (2015)

Correlation networks for Control and HIV cohorts.The legend is common for figures A, B and D: The correlation network represents features that are linked with a correlation coefficient greater than 0.6 (negative or positive) and with a p value <0.05. Parallelograms represent bacterial taxa, ellipses represent fungi and diamond shapes represent metabolites. Red edges represent a negative correlation between connected nodes and blue edges present positive correlations. A: Control correlation network with r. >0.6 or r.< -0.6 and p value <0.05. B: correlation network with +.0.6 < rho <-0.6 and p value <0.05. C: Cumulative Distribution Function D: Is the difference network map of 1A and 1B. It depicts those nodes that have a correlation with 0.6 < rho <-0.6 in either the control or HIV cohort, but not both.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4401692&req=5

pone.0123880.g001: Correlation networks for Control and HIV cohorts.The legend is common for figures A, B and D: The correlation network represents features that are linked with a correlation coefficient greater than 0.6 (negative or positive) and with a p value <0.05. Parallelograms represent bacterial taxa, ellipses represent fungi and diamond shapes represent metabolites. Red edges represent a negative correlation between connected nodes and blue edges present positive correlations. A: Control correlation network with r. >0.6 or r.< -0.6 and p value <0.05. B: correlation network with +.0.6 < rho <-0.6 and p value <0.05. C: Cumulative Distribution Function D: Is the difference network map of 1A and 1B. It depicts those nodes that have a correlation with 0.6 < rho <-0.6 in either the control or HIV cohort, but not both.
Mentions: We calculated Pearson correlations for the oral metabiome (metabolites, bacterial and fungal microbiome) that included 295 identifiable metabolites, fungi, and bacteria S2 Table. The analysis for the 295 features for both the Control cohort and the HIV cohort identified all significant correlations (rho >+0.6 or rho <-0.6). This subset of all correlations identified 2,681 Control correlations Fig 1A, and 4,240 HIV oral metabiome correlations Fig 1B. One fungus, Pichia, was only present in Control samples, and completely absent in the HIV samples.

Bottom Line: We implemented a Systems Biology approach using Correlation Difference Probability Network (CDPN) analysis to provide insights into the statistically significant functional differences between HIV-infected patients and uninfected individuals.CDPN highlights the taxa-metabolite-taxa differences between the cohorts that frequently capture quorum-sensing modifications that reflect communication disruptions in the dysbiotic HIV cohort.The results also highlight the significant role of cyclic mono and dipeptides as quorum-sensing (QS) mediators between oral bacteria and fungal genus.

View Article: PubMed Central - PubMed

Affiliation: School of Systems Biology, George Mason University, Prince William County, Fairfax, VA, United States of America.

ABSTRACT
We implemented a Systems Biology approach using Correlation Difference Probability Network (CDPN) analysis to provide insights into the statistically significant functional differences between HIV-infected patients and uninfected individuals. The analysis correlates bacterial microbiome ("bacteriome"), fungal microbiome ("mycobiome"), and metabolome data to model the underlying biological processes comprising the Human Oral Metabiome. CDPN highlights the taxa-metabolite-taxa differences between the cohorts that frequently capture quorum-sensing modifications that reflect communication disruptions in the dysbiotic HIV cohort. The results also highlight the significant role of cyclic mono and dipeptides as quorum-sensing (QS) mediators between oral bacteria and fungal genus. The developed CDPN approach allowed us to model the interactions of taxa and key metabolites, and hypothesize their possible contribution to the etiology of Oral Candidiasis (OC).

Show MeSH
Related in: MedlinePlus