Genome Enabled Ecology of Pseudo-Nitzschia Infecting Viruses and Their Impact on Pseudo-Nitzschia CommunitiesEPA Grant Number: FP917438
Title: Genome Enabled Ecology of Pseudo-Nitzschia Infecting Viruses and Their Impact on Pseudo-Nitzschia Communities
Investigators: Carlson, Michael CG.
Institution: University of Washington
EPA Project Officer: Jones, Brandon
Project Period: September 1, 2012 through August 31, 2015
Project Amount: $126,000
RFA: STAR Graduate Fellowships (2012) RFA Text | Recipients Lists
Research Category: Academic Fellowships , Fellowship - Oceanography
To understand how viruses influence Pseudo-nitzschia community dynamics, a model host-virus system will be established with an isolated and characterized Pseudo-nitzschia-infecting virus. Using genomeenabled methods, the biogeography of Pseudo-nitzschia infecting viruses and their impact on Pseudo-nitzschia assemblages in the Pacific Northwest will be assessed.
A virus that infects the highly toxigenic Pseudo-nitzschia multiseries will be isolated and characterized. The study will sequence its genome and compare it to previously sequenced diatom virus genomes, the publically available P. multiseries genome, and viral metagenomes. Based on the diversity of Pseudo-nitzschia virus-like genes observed in the metagenomic data and the other diatom virus genomes, quantitative polymerase chain reaction assays will be designed to specifically target Pseudo-nitzschia infecting viruses, which will be used to quantify their abundance in the environment. The Pacific Northwest Region will be the focus of field research not only because of its robust oceanographic monitoring, but also because of the numerous economic, cultural and social interests invested in this region. The study will establish three sampling schemes to understand how viruses and their co-occurring hosts change in space, seasonally and over the course of individual blooms. The study also will use a DNA fingerprinting technique to quantitatively characterize the Pseudo-nitzschia community diversity. The molecular data of both Pseudo-nitzschia and their viruses will be used to understand how viral mortality influences the diversity and abundance of Pseudo-nitzschia over seasonal cycles, contributes to bloom formation and demise, and drives regional differences between Pseudo-nitzschia communities.
With short infection cycles and large burst sizes (viruses per cell), the infection dynamics of diatom viruses appear to be optimized for rapidly growing diatom populations. On the timescale of bloom events, total Pseudo-nitzschia virus abundance should increase rapidly over the course of the bloom, while Pseudo-nitzschia abundance conversely should decline once a critical concentration of viruses is reached in the water column. If viruses are the cause of decline of a toxic bloom, by the nature of lysis, which releases cell contents into the water column, cellular domoic acid would be converted to dissolved DA and not be transferred up the food web. Additionally, this decline of Pseudo-nitzschia should lead to a shift in the overall phytoplankton community to either other diatom genera or other phytoplankton groups. Over the course of the season, Pseudo-nitzschia virus abundance should be correlated with diatom abundance and therefore be highest in the spring and summer. However, changes in the species composition of Pseudo-nitzschia communities, which generally shift from low toxin producers in the spring to high toxin producers in the fall, should be reflected in changes in viral diversity as well. Finally, both viral selection and environmental conditions lead to the various distinct Pseudo-nitzschia communities found in the Pacific Northwest.
Potential to Further Environmental/Human
Understanding Pseudo-nitzschia viruses has several implications for the field dynamics of the diatoms it infects, such as limiting the duration and mediating the impact of toxic Pseudo-nitzschia bloom events. Ultimately, understanding how Pseudo-nitzschia communities are changing in space and time is critical to the development of models that seek to forecast potentially toxic events and can aid in the protection of commercial interests and public health.