Studying the effects of stressors on the cnidarian microbiome with computational models

Coral reefs are structurally complex habitats constructed by reef-building cnidarians. These biodiversity hotspots provide important services to humans, but are among the most threatened marine ecosystems. For example, small short-term changes in ocean temperature can lead to bleaching events that weaken or destroy large areas of coral reef.6 Cnidarians including corals have complex microbiomes.3,4 Changes in the microbiome can precede bleaching events, suggesting that symbiotic interactions between cnidarians and their microbiota play a role in the response to external stressors.7

The Marine Microbial Symbioses lab at the University of Melbourne is developing methods to manipulate cnidarian microbiomes to help reefs respond to environmental stress.8,9 The efficient design and effective application of these tools demands a thorough understanding of the composition, dynamics, and function of cnidarian microbiomes in stressed and healthy states.4 This understanding is in its infancy. The PI labs at the University of Manchester work on a surprising parallel system: the human respiratory microbiome in asthma. As part of the CURE project10 (https://www.cureasthma.eu/), this team is developing mathematical and computational tools to understand the composition and dynamics of healthy and asthmatic microbiomes, and to develop phage therapies that can guide asthmatic microbiomes back to healthy states. This project will bring these two lines of research together, applying tools developed for the alleviation of asthma in humans to help understand and protect coral reefs.

In this project, the student will achieve three main research objectives:

1. Develop a biological market (i.e., game theory) model to predict how multi-species symbioses will respond to environmental changes.
2. Develop classification models to characterise healthy and distressed states in the cnidarian microbiome.
3. Characterise the functional profiles of cnidarian microbiota in the presence and absence of environmental stressors.

The student will be based in Manchester for components 1 and 3, and in Melbourne for component 2. Much of the metagenomic data needed for this project has already been collected by the Melbourne team, so the project can go forward even new data cannot be obtained. However, we anticipate that the student will collect additional data and test model predictions with experimental work in Melbourne or Manchester, thus ensuring that the student gains expertise in both wet and dry aspects of metagenomics research.