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

Journal Article · · mBio (Online)
 [1];  [2];  [3];  [4];  [3];  [5];  [6];  [7];  [6];  [6]
  1. San Diego State Univ., CA (United States); Univ. of California, Davis, CA (United States)
  2. San Diego State Univ., CA (United States); Brown Univ., Providence, RI (United States)
  3. San Diego State Univ., CA (United States)
  4. Indiana Univ., Bloomington, IN (United States). National Center for Genome Analysis Support, Pervasive Inst. of Technology
  5. San Diego State Univ., CA (United States); Stanford Univ., CA (United States)
  6. San Diego State Univ., CA (United States); San Diego State Univ., CA (United States). Viral Information Inst.
  7. Bermuda Inst. of Ocean Sciences, St. George's (Bermuda)
+ Show Author Affiliations
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. 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.
Research Organization:
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Sponsoring Organization:
National Science Foundation (NSF); USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
AC52-07NA27344
OSTI ID:
1775027
Report Number(s):
LLNL-JRNL--814122; 1022596
Journal Information:
mBio (Online), Journal Name: mBio (Online) Journal Issue: 2 Vol. 11; ISSN 2150-7511
Publisher:
American Society for Microbiology (ASM)Copyright Statement
Country of Publication:
United States
Language:
English

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Additional file 1 of A coral-associated actinobacterium mitigates coral bleaching under heat stress dataset January 2023

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Related Subjects

59 BASIC BIOLOGICAL SCIENCES
host-microbe
metagenomics
microbial communities