Decoding divergence in marine protistan communities: from strain diversity to basin biogeography
type: CreativeWork
description: Protists (microbial eukaryotes) in the global ocean are critical components of primary productivity and nutrient recycling. Protists are genetically diverse and have distinctive ecological niches based on genetically-driven differences in physiological fitness. A deeper understanding of which dimensions of protistan genetic diversity translate to measurable phenotypic variation is needed to predict the impact of protists on marine biogeochemistry and protists’ environmental change sensitivity. I cultured twelve strains of the coccolithophore Gephyrocapsa huxleyi across temperatures, which revealed strain-specific differences in thermal optima and niche widths. I used traits measured during the experiments to design a Darwin ecosystem model simulation, which demonstrated basin-specific biogeography of thermal optima and niche widths (Chapter 2). For seven of the twelve strains, I sequenced transcriptomes at 3-5 temperatures to assess gene expression variation. Using the RNAseq data, I developed a regression modeling approach to identify proteome allocation model parameters. Combining differential expression analysis, gene abundance normalization, and the regression model to explore the proteome allocation model parameter space, I probed differences in modeled strategies of G. huxleyi strains in response to temperature (Chapter 3). Scalable workflows highlight the challenge and promise of meta-omic data to link community structure to physiology. I developed a pipeline for metatranscriptome analysis and taxonomic annotation to address the lack of tools built specifically for microbial eukaryotes, and created mock communities to assess recovery success in protistan metatranscriptome analysis workflows (Chapters 4 and 5). I applied these tools to a three-year metatranscriptomic dataset from Cape Cod Bay to investigate a recent emergence of a summer occolithophore population in the 20-year time series, tracking shifts in nutrient physiology to identify potential bottom-up…
abstract: Protists (microbial eukaryotes) in the global ocean are critical components of primary productivity and nutrient recycling. Protists are genetically diverse and have distinctive ecological niches based on genetically-driven differences in physiological fitness. A deeper understanding of which dimensions of protistan genetic diversity translate to measurable phenotypic variation is needed to predict the impact of protists on marine biogeochemistry and protists’ environmental change sensitivity. I cultured twelve strains of the coccolithophore Gephyrocapsa huxleyi across temperatures, which revealed strain-specific differences in thermal optima and niche widths. I used traits measured during the experiments to design a Darwin ecosystem model simulation, which demonstrated basin-specific biogeography of thermal optima and niche widths (Chapter 2). For seven of the twelve strains, I sequenced transcriptomes at 3-5 temperatures to assess gene expression variation. Using the RNAseq data, I developed a regression modeling approach to identify proteome allocation model parameters. Combining differential expression analysis, gene abundance normalization, and the regression model to explore the proteome allocation model parameter space, I probed differences in modeled strategies of G. huxleyi strains in response to temperature (Chapter 3). Scalable workflows highlight the challenge and promise of meta-omic data to link community structure to physiology. I developed a pipeline for metatranscriptome analysis and taxonomic annotation to address the lack of tools built specifically for microbial eukaryotes, and created mock communities to assess recovery success in protistan metatranscriptome analysis workflows (Chapters 4 and 5). I applied these tools to a three-year metatranscriptomic dataset from Cape Cod Bay to investigate a recent emergence of a summer occolithophore population in the 20-year time series, tracking shifts in nutrient physiology to identify potential bottom-up…
author: Krinos Quinn, Arianna I.
language: en_US
keywords: Phytoplankton, Omics, Modelling
license: http://creativecommons.org/licenses/by-nc-nd/4.0/
date published: 2024-09
date modified: 2026-07-27T22:05:04Z
provider: Woods Hole Oceanographic Institute
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Massachusetts Institute of Technology and Woods Hole Oceanographic Institution

PropertyValue
property ID: Handle
value: https://hdl.handle.net/1912/70395

PropertyValue
property ID: DOI
value: 10.1575/1912/70395