Long-term dynamics of the human oral microbiome during clinical disease progression

Abstract Background Oral microbiome dysbiosis is linked to overt inflammation of tooth-supporting tissues, leading to periodontitis, an oral condition that can cause tooth and bone loss. Microbiome dysbiosis has been described as a disruption in the symbiotic microbiota composition’s stability that...

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Autores principales:	Ana Duran-Pinedo, Jose Solbiati, Flavia Teles, Ricardo Teles, Yanping Zang, Jorge Frias-Lopez
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/abd635cab3a44a2c83eb4ba6f7cbec01
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spelling	oai:doaj.org-article:abd635cab3a44a2c83eb4ba6f7cbec012021-11-08T11:17:43ZLong-term dynamics of the human oral microbiome during clinical disease progression10.1186/s12915-021-01169-z1741-7007https://doaj.org/article/abd635cab3a44a2c83eb4ba6f7cbec012021-11-01T00:00:00Zhttps://doi.org/10.1186/s12915-021-01169-zhttps://doaj.org/toc/1741-7007Abstract Background Oral microbiome dysbiosis is linked to overt inflammation of tooth-supporting tissues, leading to periodontitis, an oral condition that can cause tooth and bone loss. Microbiome dysbiosis has been described as a disruption in the symbiotic microbiota composition’s stability that could adversely affect the host’s health status. However, the precise microbiome dynamics that lead to dysbiosis and the progression of the disease are largely unknown. The objective of our study was to investigate the long-term dynamics of periodontitis progression and its connection to dysbiosis. Results We studied three different teeth groups: sites that showed disease progression, sites that remained stable during the study, and sites that exhibited a cyclic deepening followed by spontaneous recovery. Time-series analysis revealed that communities followed a characteristic succession of bacteria clusters. Stable and fluctuating sites showed high asynchrony in the communities (i.e., different species responding dissimilarly through time) and a reordering of the communities where directional changes dominated (i.e., sample distance increases over time) in the stable sites but not in the fluctuating sites. Progressing sites exhibited low asynchrony and convergence (i.e., samples distance decreases over time). Moreover, new species were more likely to be recruited in stable samples if a close relative was not recruited previously. In contrast, progressing and fluctuating sites followed a neutral recruitment model, indicating that competition between closely related species is a significant component of species-species interactions in stable samples. Finally, periodontal treatment did not select similar communities but stabilized α-diversity, centered the abundance of different clusters to the mean, and increased community rearrangement. Conclusions Here, we show that ecological principles can define dysbiosis and explain the evolution and outcomes of specific microbial communities of the oral microbiome in periodontitis progression. All sites showed an ecological succession in community composition. Stable sites were characterized by high asynchrony, a reordering of the communities where directional changes dominated, and new species were more likely to be recruited if a close relative was not recruited previously. Progressing sites were characterized by low asynchrony, community convergence, and a neutral model of recruitment. Finally, fluctuating sites were characterized by high asynchrony, community convergence, and a neutral recruitment model.Ana Duran-PinedoJose SolbiatiFlavia TelesRicardo TelesYanping ZangJorge Frias-LopezBMCarticleBiology (General)QH301-705.5ENBMC Biology, Vol 19, Iss 1, Pp 1-17 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Ana Duran-Pinedo Jose Solbiati Flavia Teles Ricardo Teles Yanping Zang Jorge Frias-Lopez Long-term dynamics of the human oral microbiome during clinical disease progression
description	Abstract Background Oral microbiome dysbiosis is linked to overt inflammation of tooth-supporting tissues, leading to periodontitis, an oral condition that can cause tooth and bone loss. Microbiome dysbiosis has been described as a disruption in the symbiotic microbiota composition’s stability that could adversely affect the host’s health status. However, the precise microbiome dynamics that lead to dysbiosis and the progression of the disease are largely unknown. The objective of our study was to investigate the long-term dynamics of periodontitis progression and its connection to dysbiosis. Results We studied three different teeth groups: sites that showed disease progression, sites that remained stable during the study, and sites that exhibited a cyclic deepening followed by spontaneous recovery. Time-series analysis revealed that communities followed a characteristic succession of bacteria clusters. Stable and fluctuating sites showed high asynchrony in the communities (i.e., different species responding dissimilarly through time) and a reordering of the communities where directional changes dominated (i.e., sample distance increases over time) in the stable sites but not in the fluctuating sites. Progressing sites exhibited low asynchrony and convergence (i.e., samples distance decreases over time). Moreover, new species were more likely to be recruited in stable samples if a close relative was not recruited previously. In contrast, progressing and fluctuating sites followed a neutral recruitment model, indicating that competition between closely related species is a significant component of species-species interactions in stable samples. Finally, periodontal treatment did not select similar communities but stabilized α-diversity, centered the abundance of different clusters to the mean, and increased community rearrangement. Conclusions Here, we show that ecological principles can define dysbiosis and explain the evolution and outcomes of specific microbial communities of the oral microbiome in periodontitis progression. All sites showed an ecological succession in community composition. Stable sites were characterized by high asynchrony, a reordering of the communities where directional changes dominated, and new species were more likely to be recruited if a close relative was not recruited previously. Progressing sites were characterized by low asynchrony, community convergence, and a neutral model of recruitment. Finally, fluctuating sites were characterized by high asynchrony, community convergence, and a neutral recruitment model.
format	article
author	Ana Duran-Pinedo Jose Solbiati Flavia Teles Ricardo Teles Yanping Zang Jorge Frias-Lopez
author_facet	Ana Duran-Pinedo Jose Solbiati Flavia Teles Ricardo Teles Yanping Zang Jorge Frias-Lopez
author_sort	Ana Duran-Pinedo
title	Long-term dynamics of the human oral microbiome during clinical disease progression
title_short	Long-term dynamics of the human oral microbiome during clinical disease progression
title_full	Long-term dynamics of the human oral microbiome during clinical disease progression
title_fullStr	Long-term dynamics of the human oral microbiome during clinical disease progression
title_full_unstemmed	Long-term dynamics of the human oral microbiome during clinical disease progression
title_sort	long-term dynamics of the human oral microbiome during clinical disease progression
publisher	BMC
publishDate	2021
url	https://doaj.org/article/abd635cab3a44a2c83eb4ba6f7cbec01
work_keys_str_mv	AT anaduranpinedo longtermdynamicsofthehumanoralmicrobiomeduringclinicaldiseaseprogression AT josesolbiati longtermdynamicsofthehumanoralmicrobiomeduringclinicaldiseaseprogression AT flaviateles longtermdynamicsofthehumanoralmicrobiomeduringclinicaldiseaseprogression AT ricardoteles longtermdynamicsofthehumanoralmicrobiomeduringclinicaldiseaseprogression AT yanpingzang longtermdynamicsofthehumanoralmicrobiomeduringclinicaldiseaseprogression AT jorgefriaslopez longtermdynamicsofthehumanoralmicrobiomeduringclinicaldiseaseprogression
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Long-term dynamics of the human oral microbiome during clinical disease progression

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