Ecosystems comprise large groups of highly interdependent organisms. Cnidarians, such
as sea anemones and corals, are keystone species in many marine ecosystems, especially
coral reefs. Each individual cnidarian also constitutes an ecosystem unto itself, a "holo-
biont", consisting of the host animal and accompanying microbial symbionts. To interro-
gate cnidarian holobionts, I used computational approaches to analyze the transcriptomes
of three cnidarians and build mechanistic models of their microbial symbionts. In par-
ticular, I analyzed and annotated the transcriptomes of the cauliflower coral Pocillopora
damicornis, the lined sea anemone Edwardsiella lineata, and the starlet sea anemone Ne-
matostella vectensis, providing information about the molecular functions expressed by
these organisms, and allowing development of a corresponding set of public databases:
PocilloporaBase, EdBase, and an updated version of StellaBase, that facilitate access to
the corresponding datasets. Additionally, I developed a method to infer the phylogenetic
antiquity of transcripts. This method also allowed me to identify transcripts from other
organisms (e.g., microbes) belonging to the anemone or coral holobiont.
In parallel – in order better to understand the microbial symbionts that share envi-
ronments with cnidarian hosts, I also developed new computer-simulation approaches for
modeling metabolic interactions between different microbial species. These approaches are
based on genome-scale stoichiometric reconstructions of metabolic networks and on Flux
Balance Analysis (FBA). In addition to contributing to the development and testing of a
new FBA-based platform for modeling communities in structured environments (Compu-
tation Of Microbial Ecosystems in Time and Space, or COMETS), I used this platform for
specific in silico experiments on microbial symbiosis. In particular, I computed all pairwise
interactions between 582 different prokaryotic models, and identified global patterns of pu-
tative positive (cross-feeding) vs. negative (food competition) interactions in this matrix
of species pairs. I found that about 7% of the pairs yielded a greater biomass when grown
together than when grown separately as monocultures. Despite existing challenges, such as
the limitations of gap-filling steps in model construction and the need for a better knowl-
edge of nutrient composition in natural environments, this approach could in the future
help forecast shifts in the coral holobiont under likely scenarios of marine environmen-
tal changes. In general, this work demonstrates how the integration of high-throughput
sequencing technology and mechanistic systems-biology simulations, can provide unique
tools to analyze interactions between microbes, and to mitigate or reverse adverse changes
in marine ecosystems.
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/14029 |
| Date | 09 November 2015 |
| Creators | Granger, Brian Robert |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
| Rights | Attribution-NonCommercial 4.0 International, Attribution-NonCommercial 4.0 International, http://creativecommons.org/licenses/by-nc/4.0/, http://creativecommons.org/licenses/by-nc/4.0/ |
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