Systems approaches to infer microbial community interactions and their impacts on ecosystem function

Microbes and the communities they form play critical roles in ecosystem function, from facilitating the biogeochemical cycling of essential nutrients, such as carbon, nitrogen, and sulfur, to acting as the foundation of complex food webs. Microbes also play important roles as eukaryotic symbionts, where they can have profound effects on host fitness. Thus, understanding how microbial community interactions and environmental context shape the functional capabilities of microbiomes is of vital importance if we want to engineer these systems to address challenges in human health and the health of natural ecosystems. Here, I show how systems approaches can be leveraged to overcome the inherent limitations of inferring microbial community interactions directly from correlation structure, which has been the standard approach in the microbiome field. Specifically, I highlight how multi-omic characterization, MCMMs, and synthetic communities (SynComs) can be leveraged to better understand niche competition between nitrate-reducing bacteria and sulfate-reducing bacteria in oxygen-depleted ecosystems, the importance of pathway partitioning in nitrate-reducing communities, and the role of nitrate-reducing communities in nitrous oxide emissions. I also highlight how microbial community-scale metabolic models (MCMMs) can be leveraged to predict Clostridioides difficile (C. difficile) colonization in the human gut microbiome, provide mechanistic insights into the niche of C. difficile across different community contexts, and assess probiotic interventions designed to inhibit C. difficile growth.

Author dc:creator

• Carr, Alex V

Advisors dc:contributor.advisor

• Baliga, Nitin S
• Gibbons, Sean M

• Denitrification
• Human gut microbiome
• Metabolic modeling
• Microbial ecology
• Systems biology
• Microbiology
• Ecology

Statement dc:rights

• CC BY-NC-SA

Language dc:language.iso

en_US

Handle dc:identifier.uri

https://hdl.handle.net/1773/51761

OAI identifier oai:identifier

oai:digital.lib.washington.edu:1773/51761