Microbial communities, through their metabolism, drive carbon cycling in marine environments. Marine heterotrophic bacteria are crucial players in this process, but the diversity of their metabolic preferences, evolved to match specific environmental conditions and inter-species partnerships, are still poorly understood. The emergent properties of microbial ecosystems make the challenge of predicting how heterotroph metabolisms influence the carbon cycle impossible with conventional approaches; teasing apart the mechanisms requires an intimate understanding of the organisms and organic matter involved. The primary goal of my dissertation research was to reduce the complexity of the ocean microbiome and organic matter to a tractable set of variables capable of reproducing basic principles of the heterotroph-organic matter relationship in a meaningful way. This challenge was addressed in multiple ways: First, I generated a phenotypic atlas for the growth capability of 63 heterotrophic marine strains across eight different classes of carbon compounds, which are representative of key components of marine dissolved organic matter. By computationally exploring the relationships between this atlas of phenotypes and the genomes of the different strains, I obtained new insight into the metabolic diversity of these marine bacteria and uncovered surprising patterns that can help understand the role of heterotrophs in the oceans. Second, I applied to this phenotypic atlas a new machine learning approach aimed at identifying subsets of environments that are informative about the whole dataset. This approach, which in this specific case study provided biologically interpretable metabolic axes for strain discrimination, is broadly applicable to high-throughput phenotypic data and can help map and understand complex biological systems. Third, I conducted a pilot study that integrates observations from laboratory and field experiments to explore the effect of microbe-microbe interactions on heterotrophic metabolic phenotypes in a highly complex natural ecosystem. Ultimately, in addition to helping understand the importance of heterotrophs under different conditions, the phenotypic fingerprints I obtained can help build higher resolution quantitative models of global microbial activity and biogeochemical cycles in the oceans. / 2025-06-16T00:00:00Z
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/46376 |
| Date | 17 June 2023 |
| Creators | Forchielli, Elena Jean |
| Contributors | Segrè, Daniel |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
| Rights | Attribution-NonCommercial-NoDerivatives 4.0 International, http://creativecommons.org/licenses/by-nc-nd/4.0/ |
Page generated in 0.0111 seconds