Award: OCE-1040597

Award Title: EAGER: Application of transcriptomics to investigate organism-environment relationships in marine zooplankton
Funding Source: NSF Division of Ocean Sciences (NSF OCE)
Program Manager: David L. Garrison

Outcomes Report

Overview: Global climate change is affecting all environments on our planet benefiting some organisms while hurting others. However, predicting winners and losers is difficult, because biologists have in depth knowledge of only a few model species. Therefore, it has become critical to study how ecologically important species respond to their environment. Throughout the Gulf of Maine and the North Atlantic, young fishes feed primarily on the copepod, a planktonic crustacean smaller than a grain of rice. Its small size and vast habitat make the copepod a poor subject for traditional physiological studies aimed at understanding how environmental factors affect their life cycle. Recent breakthroughs in molecular biology now allow scientists to take a snapshot of an animalÆs messenger RNAs. Known as transcriptomics, this technique catalogs the messages used by the cells to control the animalÆs life processes. In essence, scientists are now able to listen in on the instructions being sent out directing an organismÆs response to its changing environment. With respect to copepods, the challenge is to identify and understand each message, in order to track down the causes of population changes. Intellectual Merit: The first transcriptome for the key North Atlantic copepod Calanus finmarchicus has now been published and made available for scientists everywhere. Highlights of the study include: 1) the observation of large percentages of silent genes in any particular life stage of this copepod; 2) the identification of messages that are only highly expressed in individuals preparing to enter dormancy (diapause), a critical event in the annual population cycle; and 3) the discovery of a number of previously unknown genes, suggesting a more complex genome than those of model arthropods, such as the fruit fly and the water flea. The project has led to eleven peer-reviewed publications that were fully or partially supported under this award. The reference transcriptome generated is being used to study how exposure to the toxic alga, Alexandrium fundyense affects the biology of C. finmarchicus. This toxic alga is responsible for the red tide in the Gulf of Maine and is a recurring health problem in the region. The results from the exposure experiments will become part of a Ph.D. dissertation (expected graduation: May 2015). Microarray and platform data for C. finmarchicus are available at The Gene Expression Omnibus (GEO), accession numbers GSE34322 and GPL14742 and RNA-Seq data are available at BioProject PRJNA236528 (www.ncbi.nlm.nih.gov). Custom Perl scripts for data processing are publicly available through: github.com/LenzLab/RNA-seq-scripts. Broader Impacts: The broader impacts of the project fall into three separate categories: 1) training of future STEM workforce; 2) exposure of scientific research and its significance to a broader audience; and 3) generating resources for the scientific community. 1) NSF funding was leveraged to provide training from high school to post-doctoral levels. Four high-school students participated in the collection, identification, maintenance and culture of marine organisms as well as engaged in data mining using national databases and bioinformatics tools at the Mt. Desert Island Biological Laboratory. They also attended seminars and other scientific activities. One graduate student participated in these summer activities as well, while running the experiments she needed for her dissertation. The post-doc and the second graduate student were collaborators from U. of Connecticut and they worked on the microarray studies, and generated the first set of high-throughput sequencing data. Two undergraduate students from the University of Hawaii at Manoa were biology majors from under-represented minority groups. They learned fundamental skills in the analysis and mining of sequence data generated for non-model organisms. One student used the tools she learned to develop a...

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Principal Investigator: Petra H. Lenz (University of Hawaii)