Modeling of the Coral Microbiome: the Influence of Temperature and Microbial Network. Lima, L., F., O., Weissman, M., Reed, M., Papudeshi, B., Alker, A., T., Morris, M., M., Edwards, R., A., de Putron, S., J., Vaidya, N., K., & Dinsdale, E., A. mBio, American Society for Microbiology, 3, 2020. ![Modeling of the Coral Microbiome: the Influence of Temperature and Microbial Network [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
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Website doi abstract bibtex 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.
@article{
title = {Modeling of the Coral Microbiome: the Influence of Temperature and Microbial Network},
type = {article},
year = {2020},
volume = {11},
websites = {http://mbio.asm.org/lookup/doi/10.1128/mBio.02691-19},
month = {3},
publisher = {American Society for Microbiology},
day = {3},
id = {9a8187a7-31ae-3901-b654-7705770f24a6},
created = {2020-04-29T21:56:56.535Z},
accessed = {2020-03-06},
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last_modified = {2020-04-29T21:56:56.636Z},
read = {false},
starred = {false},
authored = {false},
confirmed = {false},
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abstract = {<p> 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 <named-content content-type="genus-species">Pseudodiploria strigosa</named-content> 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 <italic>P. strigosa</italic> in Bermuda is primarily structured by seasonal fluctuations in temperature at a reef scale, while the microbial network is a secondary driver. </p>},
bibtype = {article},
author = {Lima, Laís F. O. and Weissman, Maya and Reed, Micheal and Papudeshi, Bhavya and Alker, Amanda T. and Morris, Megan M. and Edwards, Robert A. and de Putron, Samantha J. and Vaidya, Naveen K. and Dinsdale, Elizabeth A.},
editor = {Medina, Monica and McFall-Ngai, Margaret J.},
doi = {10.1128/mBio.02691-19},
journal = {mBio},
number = {2}
}
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