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Computational and Experimental Model...

van Tol, Helena M.

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Computational and Experimental Models of Diatom-Bacteria Interaction.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Computational and Experimental Models of Diatom-Bacteria Interaction./
作者:	van Tol, Helena M.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:	161 p.
附註:	Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
Contained By:	Dissertations Abstracts International81-04B.
標題:	Biological oceanography. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13815103
ISBN:	9781085779104

Computational and Experimental Models of Diatom-Bacteria Interaction.
van Tol, Helena M.

Computational and Experimental Models of Diatom-Bacteria Interaction. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 161 p.

Source: Dissertations Abstracts International, Volume: 81-04, Section: B.

Thesis (Ph.D.)--University of Washington, 2019.

This item must not be sold to any third party vendors.

Microbial interactions structure ecosystems and fuel biogeochemical cycling. The metabolic activities operating in the ocean are critical to the entire planet. In this work, I focused on interactions between diatoms and heterotrophic bacteria. Diatoms are a group of unicellular brown algae with frustules composed of silica. They form the base of coastal and polar marine food webs and contribute one fifth of global primary productivity. The inorganic nutrients fixed by oxygenic photosynthesis fuel secondary productivity by marine bacteria. Marine bacteria and diatoms have a range of different interaction strategies; many are still being elucidated.In Chapter 1, I studied the antagonistic effects of a flavobacterium on diatom cell division. Croceibacter atlanticus inhibits cytokinesis in many species, causing the cells to elongate, become mutlinucleated, and filled with plastids.In Chapter 2, I created a metabolic model of the diatom Thalassiosira pseudonana using the genome and physiological data from the literature. Simulations of diatom growth using Flux Balance Analysis revealed a role for nitrate and sulfate assimilation in dissipating reductants from the plastid. Changing redox and nutrient conditions causes the cell to secrete metabolites including organic carbon, nitrogen, and sulfur.In Chapter 3, I created a metabolic model of the B12-producing alphaproteobacterium Ruegeria pomeroyi. Previous work has demonstrated that R. pomeroyi will provide cobalamin to T. pseudonana in B12-starvation conditions in exchange for organic sulfur and nitrogen. I constrained the metabolic models with transcriptomic data of T. pseudonana and R. pomeroyi in co-culture and simulated their interaction.The distinct character of metabolites produced by diatoms likely fuels interactions with bacteria capable of utilizing those molecules. Bacteria influence diatom metabolism by interfering with the cell cycle, through nutrient-limitation, by altering redox conditions, and providing the cofactors required for growth. In this work, I have contributed to the literature exploring the complexity of diatom-bacteria interactions, where chemical or peptide cues, signals, and antagonists underlie the dynamics of microbial interactions. I have also created a framework for exploring more general metabolic exchanges between diatoms and bacteria. Genome-scale metabolic modeling of interactions between distinct marine microbial communities may be key to accurately predicting the character of dissolve organic matter in the ocean.

ISBN: 9781085779104Subjects--Topical Terms:

2122748
Biological oceanography.
Subjects--Index Terms:

Croceibacter atlanticus

Computational and Experimental Models of Diatom-Bacteria Interaction.
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300 $a 161 p.
500 $a Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
500 $a Includes supplementary digital materials.
500 $a Advisor: Armbrust, Virginia E.
502 $a Thesis (Ph.D.)--University of Washington, 2019.
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520 $a Microbial interactions structure ecosystems and fuel biogeochemical cycling. The metabolic activities operating in the ocean are critical to the entire planet. In this work, I focused on interactions between diatoms and heterotrophic bacteria. Diatoms are a group of unicellular brown algae with frustules composed of silica. They form the base of coastal and polar marine food webs and contribute one fifth of global primary productivity. The inorganic nutrients fixed by oxygenic photosynthesis fuel secondary productivity by marine bacteria. Marine bacteria and diatoms have a range of different interaction strategies; many are still being elucidated.In Chapter 1, I studied the antagonistic effects of a flavobacterium on diatom cell division. Croceibacter atlanticus inhibits cytokinesis in many species, causing the cells to elongate, become mutlinucleated, and filled with plastids.In Chapter 2, I created a metabolic model of the diatom Thalassiosira pseudonana using the genome and physiological data from the literature. Simulations of diatom growth using Flux Balance Analysis revealed a role for nitrate and sulfate assimilation in dissipating reductants from the plastid. Changing redox and nutrient conditions causes the cell to secrete metabolites including organic carbon, nitrogen, and sulfur.In Chapter 3, I created a metabolic model of the B12-producing alphaproteobacterium Ruegeria pomeroyi. Previous work has demonstrated that R. pomeroyi will provide cobalamin to T. pseudonana in B12-starvation conditions in exchange for organic sulfur and nitrogen. I constrained the metabolic models with transcriptomic data of T. pseudonana and R. pomeroyi in co-culture and simulated their interaction.The distinct character of metabolites produced by diatoms likely fuels interactions with bacteria capable of utilizing those molecules. Bacteria influence diatom metabolism by interfering with the cell cycle, through nutrient-limitation, by altering redox conditions, and providing the cofactors required for growth. In this work, I have contributed to the literature exploring the complexity of diatom-bacteria interactions, where chemical or peptide cues, signals, and antagonists underlie the dynamics of microbial interactions. I have also created a framework for exploring more general metabolic exchanges between diatoms and bacteria. Genome-scale metabolic modeling of interactions between distinct marine microbial communities may be key to accurately predicting the character of dissolve organic matter in the ocean.
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653 $a Thalassiosira pseudonana
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