Modeling of the Coral Microbiome: the Influence of Temperature and Microbial Network

ABSTRACT Host-associated microbial communities are shaped by extrinsic and intrinsic factors to the holobiont organism. Environmental factors and microbe-microbe interactions act simultaneously on the microbial community structure, making the microbiome dynamics challenging to predict. The coral mic...

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Autores principales:	Laís F. O. Lima, Maya Weissman, Micheal Reed, Bhavya Papudeshi, Amanda T. Alker, Megan M. Morris, Robert A. Edwards, Samantha J. de Putron, Naveen K. Vaidya, Elizabeth A. Dinsdale
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2020
Materias:	host-microbe metagenomics microbial communities Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/c49ace81b83b45348039cf658d63d8c0
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spelling	oai:doaj.org-article:c49ace81b83b45348039cf658d63d8c02021-11-15T15:57:02ZModeling of the Coral Microbiome: the Influence of Temperature and Microbial Network10.1128/mBio.02691-192150-7511https://doaj.org/article/c49ace81b83b45348039cf658d63d8c02020-04-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mBio.02691-19https://doaj.org/toc/2150-7511ABSTRACT Host-associated microbial communities are shaped by extrinsic and intrinsic factors to the holobiont organism. Environmental factors and microbe-microbe interactions act simultaneously on the microbial community structure, making the microbiome dynamics challenging to predict. The coral microbiome is essential to the health of coral reefs and sensitive to environmental changes. Here, we develop a dynamic model to determine the microbial community structure associated with the surface mucus layer (SML) of corals using temperature as an extrinsic factor and microbial network as an intrinsic factor. The model was validated by comparing the predicted relative abundances of microbial taxa to the relative abundances of microbial taxa from the sample data. The SML microbiome from Pseudodiploria strigosa was collected across reef zones in Bermuda, where inner and outer reefs are exposed to distinct thermal profiles. A shotgun metagenomics approach was used to describe the taxonomic composition and the microbial network of the coral SML microbiome. By simulating the annual temperature fluctuations at each reef zone, the model output is statistically identical to the observed data. The model was further applied to six scenarios that combined different profiles of temperature and microbial network to investigate the influence of each of these two factors on the model accuracy. The SML microbiome was best predicted by model scenarios with the temperature profile that was closest to the local thermal environment, regardless of the microbial network profile. Our model shows that the SML microbiome of P. strigosa in Bermuda is primarily structured by seasonal fluctuations in temperature at a reef scale, while the microbial network is a secondary driver. IMPORTANCE Coral microbiome dysbiosis (i.e., shifts in the microbial community structure or complete loss of microbial symbionts) caused by environmental changes is a key player in the decline of coral health worldwide. Multiple factors in the water column and the surrounding biological community influence the dynamics of the coral microbiome. However, by including only temperature as an external factor, our model proved to be successful in describing the microbial community associated with the surface mucus layer (SML) of the coral P. strigosa. The dynamic model developed and validated in this study is a potential tool to predict the coral microbiome under different temperature conditions.Laís F. O. LimaMaya WeissmanMicheal ReedBhavya PapudeshiAmanda T. AlkerMegan M. MorrisRobert A. EdwardsSamantha J. de PutronNaveen K. VaidyaElizabeth A. DinsdaleAmerican Society for Microbiologyarticlehost-microbemetagenomicsmicrobial communitiesMicrobiologyQR1-502ENmBio, Vol 11, Iss 2 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	host-microbe metagenomics microbial communities Microbiology QR1-502
spellingShingle	host-microbe metagenomics microbial communities Microbiology QR1-502 Laís F. O. Lima Maya Weissman Micheal Reed Bhavya Papudeshi Amanda T. Alker Megan M. Morris Robert A. Edwards Samantha J. de Putron Naveen K. Vaidya Elizabeth A. Dinsdale Modeling of the Coral Microbiome: the Influence of Temperature and Microbial Network
description	ABSTRACT Host-associated microbial communities are shaped by extrinsic and intrinsic factors to the holobiont organism. Environmental factors and microbe-microbe interactions act simultaneously on the microbial community structure, making the microbiome dynamics challenging to predict. The coral microbiome is essential to the health of coral reefs and sensitive to environmental changes. Here, we develop a dynamic model to determine the microbial community structure associated with the surface mucus layer (SML) of corals using temperature as an extrinsic factor and microbial network as an intrinsic factor. The model was validated by comparing the predicted relative abundances of microbial taxa to the relative abundances of microbial taxa from the sample data. The SML microbiome from Pseudodiploria strigosa was collected across reef zones in Bermuda, where inner and outer reefs are exposed to distinct thermal profiles. A shotgun metagenomics approach was used to describe the taxonomic composition and the microbial network of the coral SML microbiome. By simulating the annual temperature fluctuations at each reef zone, the model output is statistically identical to the observed data. The model was further applied to six scenarios that combined different profiles of temperature and microbial network to investigate the influence of each of these two factors on the model accuracy. The SML microbiome was best predicted by model scenarios with the temperature profile that was closest to the local thermal environment, regardless of the microbial network profile. Our model shows that the SML microbiome of P. strigosa in Bermuda is primarily structured by seasonal fluctuations in temperature at a reef scale, while the microbial network is a secondary driver. IMPORTANCE Coral microbiome dysbiosis (i.e., shifts in the microbial community structure or complete loss of microbial symbionts) caused by environmental changes is a key player in the decline of coral health worldwide. Multiple factors in the water column and the surrounding biological community influence the dynamics of the coral microbiome. However, by including only temperature as an external factor, our model proved to be successful in describing the microbial community associated with the surface mucus layer (SML) of the coral P. strigosa. The dynamic model developed and validated in this study is a potential tool to predict the coral microbiome under different temperature conditions.
format	article
author	Laís F. O. Lima Maya Weissman Micheal Reed Bhavya Papudeshi Amanda T. Alker Megan M. Morris Robert A. Edwards Samantha J. de Putron Naveen K. Vaidya Elizabeth A. Dinsdale
author_facet	Laís F. O. Lima Maya Weissman Micheal Reed Bhavya Papudeshi Amanda T. Alker Megan M. Morris Robert A. Edwards Samantha J. de Putron Naveen K. Vaidya Elizabeth A. Dinsdale
author_sort	Laís F. O. Lima
title	Modeling of the Coral Microbiome: the Influence of Temperature and Microbial Network
title_short	Modeling of the Coral Microbiome: the Influence of Temperature and Microbial Network
title_full	Modeling of the Coral Microbiome: the Influence of Temperature and Microbial Network
title_fullStr	Modeling of the Coral Microbiome: the Influence of Temperature and Microbial Network
title_full_unstemmed	Modeling of the Coral Microbiome: the Influence of Temperature and Microbial Network
title_sort	modeling of the coral microbiome: the influence of temperature and microbial network
publisher	American Society for Microbiology
publishDate	2020
url	https://doaj.org/article/c49ace81b83b45348039cf658d63d8c0
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Modeling of the Coral Microbiome: the Influence of Temperature and Microbial Network

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