Award: OCE-1259994

Award Title: Collaborative Research: Gene content, gene expression, and physiology in mesopelagic ammonia-oxidizing archaea
Funding Source: NSF Division of Ocean Sciences (NSF OCE)
Program Manager: David L. Garrison

Outcomes Report

Archaea are ubiquitous and abundant members of the marine plankton. Once thought of as rare organisms found in exotic extremes of temperature, pressure, or salinity, archaea are now known in nearly every marine environment. One group, the marine Thaumarchaea, was a focus for this project. The Thaumarchaea are among the most populous microbes in the oceans and catalyze the globally relevant process of ammonia oxidation. The Thaumarchaea have only recently been domesticated into tractable laboratory cultures, though many of these are from soil, brackish systems, or other non-open ocean environments. We have generated several high quality genomes for Thaumarchaea enrichment cultures from the open ocean through metagenomic sequencing, assembly, genome binning, genome closure, and subsequent annotation. Recruitment of metagenomics data from global expeditions show that these genomes are more representative of shallow open ocean populations of thaumarchaea, though it was also noted that different lineages are found in deeper waters. Controlled growth experiments with these enrichment cultures, which are essentially mini-metatranscriptomes, were combined with available thaumarchaea rich metatranscriptomes from marine ecosystems. Subsequent analyses of gene co-expression showed that the thaumarchaea transcriptome is quite modular (Fig. 1); most genes are organized into a discrete number highly connected hubs that show coordinated expression. The usage of data from a diverse range of environments including multiple different ecosystems indicates that these co-expression networks are robust and likely predictive, allowing for a transition from observational to predictive ecosystem profiling. Coalescing available culture-based genomes, meta-genome assembled genomes, and single cell genomes into a single database, we examined the currently known diversity of this group, which highlights uncultivated lineages of these organisms. Numerous machine-learning guided tools for metagenome and network analysis have also been developed as part of these efforts, with training for numerous students or young scientists transiting to graduate school. Last Modified: 08/22/2018 Submitted by: Christopher Dupont

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People

Principal Investigator: Christopher Dupont (J. Craig Venter Institute, Inc.)