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

Laís F O Lima,Elizabeth A Dinsdale,Bhavya Papudeshi,Maya Weissman,Micheal Reed,Megan M Morris,Amanda T Alker,Naveen K Vaidya,Robert A Edwards,Samantha J De Putron,Monica Medina,Margaret J Mcfall-Ngai

doi:10.1128/mbio.02691-19

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.

Highlights

Host-associated microbial communities are shaped by extrinsic and intrinsic factors to the holobiont organism
We considered temperature as the major driver affecting the predictability of the coral surface mucus layer (SML) microbiome of both reef zones compared to microbial network (Fig. 7)
Differences in accuracies were driven by the distinct temperature profiles used across the model scenarios, while different microbial network profiles caused no apparent effect

Summary

Introduction

Host-associated microbial communities are shaped by extrinsic and intrinsic factors to the holobiont organism. 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. 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. The community structure of a host-associated microbiome is shaped by factors that are both extrinsic (e.g., abiotic conditions and community composition of microand macroorganisms in the surrounding environment) and intrinsic Identifying the role that each factor plays in predicting the diversity and community structure in the microbiome of host organisms is a major priority in microbial ecology, especially in the context of environmental changes [5,6,7,8]. The coral microbiome provides essential services to the holobiont, such as nutrient cycling [17,18,19,20] and protection against opportunistic pathogens via competition and the production of antibiotic compounds [21,22,23]

Methods

Results

Conclusion