Genomic design principles of carbon exchange between algae and bacteria

The research will discover the rules for building ecosystems that cycle carbon. Life on Earth is sustained by a global carbon cycle. Understanding how and why this cycling is maintained is a critically important question given the growing impact of human activity on carbon dioxide levels in the atmosphere.

Carbon cycling happens through two processes: photosynthesis which creates sugars and biomass from carbon dioxide, and respiration which converts sugars and biomass back to carbon dioxide. Remarkably, roughly half of all of the photosynthesis on the planet is performed by microbes such as algae. Algae provide carbon in the form of sugars and other compounds to bacteria.

The resulting growth of bacteria is not only essential for the health and function of nearly all ecosystems on the planet but key to the global carbon cycle. Despite the critical nature of this interaction, the principles the govern how carbon flows from algae to bacteria and back are not known. The project will uncover these principles using closed microbial biospheres: hermetically sealed microbial communities that sustain a carbon cycle between algae and bacteria when provided with only light.

Uncovering these rules will open the door to predicting, designing, and controlling carbon flow through microbial communities with applications from modeling global carbon cycling to biofuels. The research is accompanied by a new curriculum that is directed at bringing cutting-edge experimental and data science methods to students of Ecology and Evolution as well as accessible demonstrations of scientific ideas to middle school age students.

The research objective of this proposal is to discover the design principles governing carbon exchange between algae and bacteria and to apply these principles to build synthetic communities with predefined carbon cycling capabilities. A key roadblock to unlocking the power of microbial communities is that we do not know how genomic structure determines metabolic function.

The project combines carbon-cycling closed microbial communities, high-throughput measurements, and machine-learning to discover the key genomic features (pathways and taxa) that enable carbon exchange between algae and bacteria. The outcomes of the project will be: (1) Combine machine learning and high-throughput measurements of carbon cycling to predict carbon exchange between algae and bacteria using taxonomic and metagenomic information and to test these predictions in synthetic consortia. (2) Dissect the interactions that sustain carbon exchange in algae-bacteria communities by exploiting a massively parallel droplet microfluidic platform to measure thousands of interactions.

Leverage these insights to construct a consumer-resource model of carbon exchange and predict the impact of algal mutations on carbon recycling.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.