Graduate Studies

 

First Advisor

Hyun-Seob Song

Department

Complex Biosystems

Date of this Version

Spring 2024

Document Type

Dissertation

Comments

Copyright 2024, Aimee Kristin Kessell. Used by permission

Abstract

As a major component of fungal cell walls and exoskeletons of invertebrates, chitin is widespread in soils, constituting the second most abundant biopolymer in nature. Composed of N-acetyl-D-glucosamine chains, it serves as a vital source of nutrients, including both carbon and nitrogen, for the growth of microorganisms. A solid understanding of the microbial degradation of chitin is critical for predicting their impacts on biogeochemical cycling in soil ecosystems. Organisms that degrade biopolymers (degraders) produce energetically expensive extracellular enzymes to break down complex organic carbons into simpler labile forms that are sharable with other species, including those that do not contribute directly to the degradation process (cheaters). Therefore, it impacts not only the metabolic and growth efficiencies of the degraders but also fosters diverse interspecies interactions within microbial communities. The level of complexity in this process necessitates the use of mechanistic metabolic models. However, reconstruction of phenotype-consistent genome-scale metabolic networks is still challenging due to the frequent occurrence of false positives (model prediction of biomass production in media where actual organism cannot grow) when gapfilled using typical sequential gapfilling approaches. In this work, I developed a new iterative gapfilling method to address this issue and applied it to build metabolic networks of chitin-degrading communities and their isolates—using a consortium of Cellvibrio japonicus (degrader) and Escherichia coli (non-degrader) as a model system. This new development revealed previously unknown and interesting findings on how bioenergetic cost on chitin degradation affects degrader’s metabolism and its interactions with non-degraders. The model also provided mechanistic interpretations of the predicted changes in metabolism and interactions based on carbon and nitrogen use efficiencies. Both the methods and findings are reproducible, and may be used in other biopolymer-degrading communities.

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