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PLoS One. 2016 Sep 6;11(9):e0160929. doi: 10.1371/journal.pone.0160929. eCollection 2016.

Patterns of Transcript Abundance of Eukaryotic Biogeochemically-Relevant Genes in the Amazon River Plume.

Author information

1
University of South Florida College of Marine Science, St. Petersburg, FL, United States of America.
2
Department of Microbial and Environmental Genomics, J. Craig Venter Institute, San Diego, CA, United States of America.
3
Romberg Tiburon Center, San Francisco State University, Tiburon, California, United States of America.
4
Horn Point Laboratory, University of Maryland Center for Environmental Science, Cambridge, MD, United States of America.
5
College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, Oregon, United States of America.
6
Rhodes College, Memphis, TN, United States of America.
7
Ocean Sciences, University of California, Santa Cruz, CA, United States of America.
8
Department of Ecology, Environment, and Plant Sciences, Stockholm University, Stockholm, Sweden.
9
Lamont Doherty Earth Observatory, Columbia University, Palisades, NY, United States of America.
10
School of Biology, Georgia Institute of Technology, Atlanta, GA, United States of America.
11
Department of Biology and Biotechnology Graduate Program, American University in Cairo, New Cairo, Egypt.
12
Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States of America.
13
Department of Marine Sciences, University of Georgia, Athens, GA, United States of America.

Abstract

The Amazon River has the largest discharge of all rivers on Earth, and its complex plume system fuels a wide array of biogeochemical processes, across a large area of the western tropical North Atlantic. The plume thus stimulates microbial processes affecting carbon sequestration and nutrient cycles at a global scale. Chromosomal gene expression patterns of the 2.0 to 156 μm size-fraction eukaryotic microbial community were investigated in the Amazon River Plume, generating a robust dataset (more than 100 million mRNA sequences) that depicts the metabolic capabilities and interactions among the eukaryotic microbes. Combining classical oceanographic field measurements with metatranscriptomics yielded characterization of the hydrographic conditions simultaneous with a quantification of transcriptional activity and identity of the community. We highlight the patterns of eukaryotic gene expression for 31 biogeochemically significant gene targets hypothesized to be valuable within forecasting models. An advantage to this targeted approach is that the database of reference sequences used to identify the target genes was selectively constructed and highly curated optimizing taxonomic coverage, throughput, and the accuracy of annotations. A coastal diatom bloom highly expressed nitrate transporters and carbonic anhydrase presumably to support high growth rates and enhance uptake of low levels of dissolved nitrate and CO2. Diatom-diazotroph association (DDA: diatoms with nitrogen fixing symbionts) blooms were common when surface salinity was mesohaline and dissolved nitrate concentrations were below detection, and hence did not show evidence of nitrate utilization, suggesting they relied on ammonium transporters to aquire recently fixed nitrogen. These DDA blooms in the outer plume had rapid turnover of the photosystem D1 protein presumably caused by photodegradation under increased light penetration in clearer waters, and increased expression of silicon transporters as silicon became limiting. Expression of these genes, including carbonic anhydrase and transporters for nitrate and phosphate, were found to reflect the physiological status and biogeochemistry of river plume environments. These relatively stable patterns of eukaryotic transcript abundance occurred over modest spatiotemporal scales, with similarity observed in sample duplicates collected up to 2.45 km in space and 120 minutes in time. These results confirm the use of metatranscriptomics as a valuable tool to understand and predict microbial community function.

PMID:
27598790
PMCID:
PMC5012681
DOI:
10.1371/journal.pone.0160929
[Indexed for MEDLINE]
Free PMC Article

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