Seppe Kuehn

The structure and evolution of functional microbial communities

Summary: Sequencing technologies have revolutionized our understanding of the structure of microbial ecosystems in oceans, soils, lakes and higher organisms. While sequencing reveals a taxonomic and genomic “parts list” for microbial communities, a grand challenge remains: how and why do these parts assemble into dynamic, interacting and functional ecosystems? In short, can we understand why specific genomic or taxonomic associations are present in microbial communities? How do these associations drive metabolic function, species-species interactions, and ecosystem stability? My research program takes on these questions using sophisticated laboratory-based measurements and statistical analysis of genomic variation on functional microbial communities isolated from natural environments. I am focusing on three microbial systems to discover the principles by which genomes drive metabolic function (denitrification), interactions (phototroph-heterotroph communities) and ecosystem stability (self-sustaining closed ecosystems).

The remarkable complexity of microbial communities drives global nutrient cycles, provides metabolic capabilities for higher organisms and is critical to the persistence and stability of nearly all ecosystems on Earth. The sequencing revolution has opened a window into the structure of microbial systems in natural settings. These studies have revealed staggering taxonomic diversity and conserved metabolic capabilities for communities in similar environmental conditions. A key challenge remains: can we translate insights from sequencing into a complete understanding of interactions, metabolism and stability in microbial ecosystems?

Existing efforts to map genomic data to community function, dynamics and stability are based on detailed ``bottom-up" models of all metabolic processes within a community. These models attempt to capture every metabolic reaction, metabolite and signaling molecule by including an immense number of rate equations and metabolic constraints. Unfortunately, such models are difficult to construct and interpret, and challenging to generalize. Further, this approach is currently constrained to communities of a few model organisms and has not been successfully applied to complex natural ecosystems.

My research program takes an alternative approach by building empirically-driven statistical models for understanding metabolic function, interactions and stability on the basis of species, genome or pathway-level composition. My approach will make three important, new contributions to the field. First, because my approach is statistical and based on genomic information I expect it to be generalizable, permitting predictions for natural communities where only sequence data are available. Second, by working with naturally derived microbial communities we engage the full complexity of natural systems without restricting ourselves to a small set of model organisms. Finally, these advances are enabled by my unique abilities to build instrumentation which generate high-quality data that I subject to rigorous analysis and mathematical modeling. I anticipate high-impact discoveries that will address basic questions about the evolutionary organization of microbial communities through a set of three integrated experiments:

Genomics of denitrifying bacterial communities: By isolating ensembles of denitrifying bacterial communities and combining sequencing with high-throughput metabolic measurements, I am discovering how genetic variation defines community metabolic function.

Interactions and dynamics in phototroph-heterotroph communities: (this proposal) What is the structure of evolutionarily conserved phototroph-heterotroph interaction networks? How does this structure confer resilience on phototroph-heterotroph communities? To answer these questions I combine sophisticated measurements of species-species interactions and community response to perturbations.

Stability in self-sustaining closed microbial communities: Closed microbial ecosystems are model biospheres which sustain themselves indefinitely when provided with only light. By combining sequencing with high-throughput measurements of nutrient cycling in closed ecosystems, I will discover how specific interactions and metabolic capabilities affect ecosystem stability and long-term persistence.