Mathematical modeling of the coral microbiome: the influence of temperature and microbial network

Lais Lima1, Maya Weissman2, Michaeal Reed3, NP Bhavya4, Amanda Therese Alker5, Megan M Morris6, Rob Edwards7, Samantha J de Putron8, Naveen Vaidya7 and Elizabeth A Dinsdale9, (1)San Diego State University, Biology, San Diego, CA, United States, (2)San Diego State University, United States, (3)San Diego State University, San Diego, CA, United States, (4)National Center for Genome Analysis Support (NCGAS), United States, (5)San Diego State University, Cell and Molecular Biology, San Diego, CA, United States, (6)Stanford University, Biology, Stanford, CA, United States, (7)San Diego State University, San Diego, United States, (8)Bermuda Institute of Ocean Sciences, St. Georges, Bermuda, (9)San Diego State University, Ecology, San Diego, CA, United States
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 community structure, making the microbiome dynamics challenging to predict. The microbiome associated with corals is essential to the health of coral reefs and sensitive to environmental changes. We developed a dynamic model to determine the community structure of the microbiome associated with coral mucus using temperature as an extrinsic factor and microbial network as an intrinsic factor. We sequenced twelve metagenomes associated with the mucus of the coral Pseudodiploria strigosa from inner and outer reefs in Bermuda and a reef zone-specific microbiome was identified. The taxonomic composition and the microbial network of the coral mucus microbiome provided data for the model development and validation. The model was validated by comparing the predicted relative abundances to the relative abundances from the sample data. The model accurately predicts the microbiome of P. strigosa (Wald Linear Hypothesis test, P = 0.9).To investigate the drivers of the coral-microbial community dynamics from each reef zone, the model was applied to six scenarios that combined different profiles of temperature and microbial network. Parameters associated with changes in the accuracy of the model outputs in describing the sample data are considered key factors shaping the coral microbiome structure. Ten out of the total of twelve model outputs analyzed, showed valid linear regression (R² ~ 0.6) between model and sample data depicting the relative abundances of 17 microbial classes. The microbiome was best predicted by model scenarios with the temperature profile that was closest to the local thermal environment, regardless of network profile. Our model shows that seasonal temperature variation is the primary driver of microbiome composition, while the microbial network is a secondary driver.
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